To read this content please select one of the options below:

Please note you do not have access to teaching notes, concepts of travel behaviour research.

Threats from Car Traffic to the Quality of Urban Life

ISBN : 978-0-08-044853-4 , eISBN : 978-0-08-048144-9

Publication date: 12 April 2007

Acknowledgements

This chapter draws on Axhausen (2006) and discussions during my lecturing and supervision. I gratefully acknowledge the input from those taking part.

Axhausen, K.W. (2007), "Concepts of Travel Behaviour Research", Gärling, T. and Steg, L. (Ed.) Threats from Car Traffic to the Quality of Urban Life , Emerald Group Publishing Limited, Leeds, pp. 165-185. https://doi.org/10.1108/9780080481449-009

Emerald Group Publishing Limited

Copyright © 2007 Emerald Group Publishing Limited

We’re listening — tell us what you think

Something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

  • View Record

https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

TRID the TRIS and ITRD database

Concepts of Travel Behavior Research

In the first part this chapter proposes a conceptual framework for travel behavior research through a definition of the scope of the research topic, essentially human activity schedules, and a conceptualization of the traveler as a network actor negotiating infrastructure and human networks and dealing with the social content of the activities undertaken. In the second part of the chapter an operationalization of this framework through the dynamic microsimulation of daily life nested within the microsimulation of longer-term projects and choices.

  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780080448534
  • Abstract reprinted with permission from Elsevier.
  • Axhausen, Kay W
  • Publication Date: 2007
  • Media Type: Print
  • Edition: First
  • Features: Figures; Maps; References;
  • Pagination: pp 165-185
  • Monograph Title: Threats from Car Traffic to the Quality of Urban Life: Problems, Causes, and Solutions

Subject/Index Terms

  • TRT Terms: Activity choices ; Behavior ; Human factors ; Microsimulation ; Research ; Schedules ; Travel behavior ; Travelers
  • Uncontrolled Terms: Daily activity plans
  • Subject Areas: Highways; Planning and Forecasting; Public Transportation; Research; Safety and Human Factors; Society; I72: Traffic and Transport Planning;

Filing Info

  • Accession Number: 01076811
  • Record Type: Publication
  • ISBN: 9780080448534
  • Files: TRIS
  • Created Date: Sep 21 2007 1:55PM

Travel Behavior and Urban Land Use

Chapter 2 travel behavior theories.

Two different perspectives, individual and collective, can explain travel behavior. When people contextualizing travel as a personal choice or decision-making, the traveler as a subject make mode choices, driving or not. When travel behavior is understood as a social phenomenon, researcher observe and understand the trips as a whole. The two perspectives derived two schools of theory, mode choice and human mobility. In the school of mode choice, travel distance could be treat as an independent variable, a part of travel cost, or could be decided in the next step after mode choice , such as route choice. In the school of human mobility, driving distance grab more attentions.

2.1 Mode Choices (Travel as a subject)

Are ‘decision’ and ‘choice’ the same when discussing travel modes? Literally, a ‘choice’ is one decision given all available options at the same time. While ‘decision’ is a broader concept. A decision could be a schedule with a combination of many choices, such as modes, destination, and activities. A decision related to travel behavior could even include bicycle or car purchase, and relocation. This section will start from the theories of mode choice, then extend to a broader discussion of decision processes.

2.1.1 Rational Choice Theory

For prescriptive, analytical everyday decision-making, rationality is a basic assumption in reasoned behavior or rational choice theories ( Edwards 1954 ; Von Neumann and Morgenstern 1944 ) .

This category is also called ‘Normative Decision Theory,’ which assume people a traveler is an ideal decision maker who are full rational. It requires three necessary steps including information collection, utility evaluation, and choice making.

  • Expected Utility Theory (EUT)

Traditional economics focus on the utility evaluation and come up with the Expected Utility Theory (EUT) which is also called Consumer Choice Theory. The rule of EUT is Random Utility Maximization (RUM) ( Ben-Akiva and Lerman 1985 ; McFadden 1973 ) . This classical theory claims that customer always choose the one most appropriate by comparing the advantages and disadvantages of a range of alternatives, evaluating the benefits and costs of each possible outcome. Eventually travelers will select the optimal solution with the maximum ‘utility’ from the choice set.

In real life, Rational Choice Theory can not accurately describe the actual human behavior. Individuals do not often collect and analyse all the relevant information. They are not ‘ideal’ and are not able to calculate the utility for all possible alternatives with perfect accuracy. In many cases, the travel decision is not regarded as the ‘best’ one to achieve travelers’ desired objective. Many other theories were developmed to fix these issues.

2.1.2 Bounded Rational Behavior

Bounded rationality focused on the limitation of self-control ( March and Simon 2005 ) . In reality, individuals are behaving under many constraints including incomplete information, limited time, and cognitive capacity. The observed behaviors often are not optimal and are inconsistent with ‘pure’ rationality. Bounded rationality claims that, when people make decisions under constraints, heuristics and rules of thumb are more common than statistical inference. People are satisfied with a ‘good enough’ decision unless there is a definitively better alternative. The recently witnessed events would have stronger effects on an individual’s decision than others ( Camerer, Loewenstein, and Rabin 2004 ) .

2.1.3 Theory of Planned Behavior

In psychology, many theories and models are developed to explain people’s decision-making processes. 4

Ajzen and Fishbein ( 1977 ) proposed the Theory of Reasoned Action (TRA) to understand people’s behavioral intentions and actual behaviors. They found two deciding psychological elements as attitudes and subjective norms . Ajzen ( 1991 ) adds a new part of Perceived Behavioral Control (PBC) and renames TRA as Theory of Planned Behavior (TPB).

Attitudes are personal evaluation and it means how people prefer or are against performing an activity. For example, a commuter might choose transit in spite of the longer travel time because this person believes that transit is an environment-friendly transport mode.

Subjective norm is the social pressure from others. In the example above, choosing transit is because of other people’s normative expectations rather than personal desirability.

PBC represents some nonvolitional factors such as time, budget, and resources. PBC is assessed by the individual’s perception of ease or difficulty of the behavior. PBC is one reason of the different between intentions and actual behaviors, which is called attitude-behavior gap ( Kollmuss and Agyeman 2002 ; Lane and Potter 2007 ) . In this case, a commuter might choose transit because this person is confident in catching the bus every day.

Based on RUM models, McFadden ( 2001 ) proposes a similar framework called the choice process including attitudes, perception, and preference. This framework is further developed to hybrid choice model (HCM) and non-RUM decision protocols ( Ben-Akiva et al. 2002 ) .

Two meta-analyses found that intentions to drive, perceived behavioral control, habits and past behavior play the primary roles in travel mode choice. Among these factors, PBC have the strongest effects on private car use. People don’t want to reduce the car use because they think it is very inconvenient. The effect of attitudes is modest while subjective norms have weak effect on car use ( Lanzini and Khan 2017 ; Gardner and Abraham 2008 ) .

2.1.4 Prospect Theory

Kahneman and Tversky ( 1979 ) introduced the ProspectTheory 5 to study the impacts of biases. Prospect Theory is a descriptive theory with three main components: First, people are more sensitive to the sure things (e.g., the probability between 0.9 and 1.0, or between 0.0 and 0.1 ), while being indifferent to the middle range (e.g., from 0.45 to 0.55). Second, people care more about the change of overall proportion than the absolute values regardless of gains or losses. Third, people make choice based on a reference point, rather than the overall situation or worth. Economist also extend the theory of expected utility maximization to Behavioral Economics by address the influence of psychology on human behavior.

  • Regret Theory

Regret Theory introduces the notions of risk or uncertainty in decisions ( Loomes and Sugden 1982 ) . Psychological studies found that individuals will not only try to maximize the utility but to minimize the anticipation of regret. The fear of regret could affect people’s rational behavior. For example, A high risk of congestion in peak hours could encourage a commuter to choose transit mode. Likewise, a good reputation for punctuality can give traveler confidence in the rail system.

In addition to the traditional utility framework, a regret term is added to address the uncertainty resolution. The utility function on the best alternative outcome will be smaller after subtracting the regret term, which is an increasing, continuous and non-negative function.

  • Cognitive Bias

Another psychological factor, cognitive bias can result in judgement errors. For example, people treat potential gains and losses differently, that is called Loss Aversion. Loss Aversion suggests that the negative feeling about losses is greater than the positive response to gains ( Tversky and Kahneman 1992 ) . As a result, individual’s decisions may not be consistent with evidence and tend to pay additional costs to avoid losses.

2.2 Human Mobility (Travel as an object)

2.2.1 trip distribution laws.

There is a long history of human mobility studies. In Physics and Geography, travel distance and pattern are treated as an objective phenomenon. The related theories try to use some statistical expressions to fit the aggregated trip distributions.

Gravity Law is a dominant theory in this field. Scholars have developed some more delicate forms of Gravity Law and found some mathematical relationship to other famous distribution laws. Some theories from different perspectives, like intervening opportunities also show strong ability for explaining travel patterns and regularities.

2.2.1.1 Distance Based Theories

  • Law of Migration

An early theory called Law of Migration by Ravenstein ( 1885 ) tried to explain the regional migration patterns. This found is based on observation rather than quantitative analysis. But it capture the fact that the direction of migration is toward the regional center with great commerce and industry. It also pointed out that distance is a primary factor for migrant. This theory inspired many studies on population movement consequently. Even today, socio-economic factors and distance-constraints are the essential parts in the relevant models and frameworks.

Zip’s law is also called discrete Pareto distribution . It is found in linguistics to explain the inverse relationship between the frequency and rank of a word. The charm is that this rank-frequency distribution disclosed a universal law in many realms of society and physics, such as urban size, corporation sizes, cells’ transcriptomes and so on. Zipf interpreted the two competing factors as force of diversification and unification . The former produces larger amount of cases and the later tries to upgrade the rank. An equilibrium of the rank-frequency balance is controlled through a parameter \(\alpha\) in the exponent. For example, a city’s population size \(m\) has a negative power relationship to its rank \(r\) as below. 6

\[ m \sim 1 / r^{\alpha} \]

Zipf ( 1946 ) extended this expression to describe the traffic in both directions between two cities:

\[ t_{ij}\propto \frac{m_i m_j} {d_{ij}} \]

where \(t_{ij}\) represent the traffic flow of goods between two centers \(i\) and \(j\) with population sizes \(m_i\) and \(m_j\) . \(d_{ij}\) is the distance from \(i\) to \(j\) . Because Zipf’s formula has a same form with Newtonian mechanics ( Newton 1848 ) , people call this expression as Gravity Law.

  • Gravity Law

As the most influential theory, Gravity Law asserts that the amount of traffic flow between two centers is proportional to the product of their mass and inverse to their distance. The mass is often measured by population size.

\[ p_{ij}\propto m_i m_j f(d_{ij}), \qquad i\ne j \]

where \(p_{ij}\) is the probability of commuting between origin \(i\) and destination \(j\) , satisfying \(\sum_{i,j=1}^n p_{ij}=1\) . \(m_i\) and \(m_j\) are the population of two census units. The travel cost between the two places is represented as a distance decay function of \(d_{ij}\) .

Exponential and power are the two forms of the distance decay function with a parameter \(\lambda\) showed as below:

\[ f(d_{ij})=\exp(-\lambda d_{ij}) \] and

\[ f(d_{ij})={d_{ij}}^{-\lambda} \] The function implies that the movements between the origin and destination decays with their distance. In transportation modeling, a common form of gravity model is :

\[ T_{ij}= \alpha_i O_i \cdot \beta_j D_j \cdot f(d_{ij}) \]

where \(T_{ij}\) is the flow between \(i\) and \(j\) . the two population are replaced by total tirp generation of origin \(O_i\) and total trip attraction of destination \(D_i\) . \(\alpha_i\) and \(\beta_j\) are two constraining parameters to satisfy \(\sum_{i}^{n_i}T_{ij} = D_j\) and \(\sum_{j}^{n_j}T_{ij} = O_i\) . It means that \(\alpha_i = [\sum_{j}^{n_j} \beta_j D_j \cdot f(d_{ij})]^{-1}\) and \(\beta_j = [\sum_{i}^{n_i} \alpha_i O_i \cdot f(d_{ij})]^{-1}\) . Thus, this model is called as doubly constrained gravity model.

If it relieves the two constrains. this model will be simplified to single-constrained and unconstrained gravity model. By assuming \(\alpha\beta\) is an adjustment parameter irrelevant to locations \(i\) and \(j\) for controlling the total flows, this model will not guarantee that the attraction of a destination equals the sum of flow from all origins, and the generation of a origin equals the sum of flow to all destinations.

Broadly speaking, Zipf’s law and Gravity Law have a common essence of power law, or scaling pattern. The Zipfian distribution is one of a family of power-law probability distributions. The power-law distribution also holds in many realms: urban size, population density, street blocks, building heights, etc.

The state-of-the-art studies of human mobility agree that travel behavior follows a power-law distribution at the population level ( Barbosa et al. 2018 ) . An example is Brockmann, Hufnagel, and Geisel ( 2006 ) use dollar bills to track travel habits and confirm this theory. It reflects the fact that both trip and land use, as two geographic variables, follow some Paretian-like distribution. Apparently, it conflicts with Gaussian thinking, the foundation frame of linear models based on the location and scale parameters. 7

Meanwhile, the log-normal distribution may be asymptotically equivalent to a special case of Zipf’s law, which could support the logarithm transform in current VMT-density models ( Saichev, Malevergne, and Sornette 2010 ) .

2.2.1.2 Opportunity Based Theories

  • Law of Intervening Opportunities

Law of Intervening Opportunities by Stouffer ( 1940 ) developed the migration theory in a different direction. Stouffer proposed that “the number of people going a given distance is directly proportional to the number of opportunities at that distance and inversely proportional to the number of intervening opportunities.”

Comparing with gravity law, the number of intervening opportunities \(s_{ij}\) replaces the distance between origin and destination. For example, a resident living in location \(i\) is attracted to location \(j\) with \(s_{ij}\) job opportunities in between.

\[ p_{ij}\propto m_i \frac{P(1|m_i,m_j,s_{ij})}{\sum_{k=1}^n P(1|m_i,m_j,s_{ij})}, \qquad i\ne j \]

where the conditional probability \(P(1|m_i,m_j,s_{ij})\) can be expressed by Schneider (1959) as:

\[ P(1|m_i,m_j,s_{ij})=\exp[-\gamma s_{ij}] - \exp[-\gamma (m_j + s_{ij})] \]

  • Radiation Law

Simini et al. ( 2012 ) propose a radiation model express the probability of the destination \(j\) absorbing a person living in location \(i\) as below:

\[ P(1|m_i,m_j,s_{ij})= \frac{m_i m_j}{(m_i + s_{ij})(m_i + m_j + s_{ij})} \]

Or in transportation model it is expressed as:

\[ T_{ij}= O_i\cdot\frac{m_i m_j}{(m_i + s_{ij})(m_i + m_j + s_{ij})} \] To approximating the number of opportunities, \(s_{ij}\) is from the population within a circle centered at origin. The radius is the distance between \(i\) and \(j\) . Then \(m_i + m_j + s_{ij}\) represents the total population within the circle, and \(m_i + s_{ij}\) is the total population within the circle but excluding \(j\) , that is:

\[ T_{ij}= O_i\cdot\frac{m_i }{m_i + s_{ij}}\cdot\frac{m_j}{m_i + m_j + s_{ij}} \] The part of fraction converts to the product of two weights, the weights of origin and destination in the whole region. Although distance \(d_{ij}\) doesn’t appear in the expression of radiation model, it is still a determinant as in gravity model.

  • Distance Decay (hazard models)

Using the survival analysis framework, Yang et al. ( 2014 ) further extended this model by assuming a trip from origin to destination as a time-to-event process. Here time variable is replaced by the number of opportunities.

The survival function \(S(t)=Pr(T>t)\) represents the cumulative probability of the event not happened within a certain amount of opportunities. Choosing Weibull distribution as the survival function, \(S(t)=\exp[-\lambda t^\alpha]\) with scale parameter \(\lambda \in (0, +\infty)\) . By assuming \(f(\lambda)=\exp[-\lambda]\) and integral on \(\lambda\) , the derivation is:

By replacing \(t\) with \(m_i+s_{ij}\) , the conditional probability is:

\[ \begin{aligned} P(1|m_i,m_j,s_{ij})= &\frac{P(T>m_i+s_{ij})-P(T> m_i+s_{ij}+m_j)}{P(T>m_i)} \\ =&\frac{[(m_i + s_{ij} + m_j)^{\alpha}-(m_i + s_{ij})^{\alpha}](m_i^{\alpha}+1)}{[(m_i + s_{ij} + m_j)^{\alpha}+1][(m_i + s_{ij})^{\alpha}+1]}\\ \end{aligned} \]

where \(\alpha\) is a parameter adjusting the effect of the number of job opportunities between origins and destinations.

A similar method can be found in Ding, Mishra, et al. ( 2017 ) ’s study. They use a multilevel hazard model to examine the effects of TAZ level and individual level factors with respect to commuting distance using the data of Washington metropolitan area.

Based on commuting data from six countries, Lenormand, Bassolas, and Ramasco ( 2016 ) found gravity law performs better than the intervening opportunities law. The reasons could be the circle with radius \(d_{ij}\) can not accurately represent the real influencing area, and the different between population and opportunities is not captured in this way.

2.2.2 Time Geography

In contrast to overall trip distribution, the movements of individuals are always research interest in geography. Hägerstraand ( 1970 ) proposed some concepts and tools in space and time to measure and understand the individual trajectories. This branch is called time geography. The famous “space-time aquarium/prism” is a 3D cube by adding temporal scales on the geographic space. It can capture the detailed structure and behavior of traveler.

A daily travel could include multiple trips and form a travel chain. The traveler may switch the sequence or adjust the routes to optimize the chain and minimize the travel costs. The daily total travel distance is the summation of every trip distances. The number of trips denotes as trip count. It exists but not so common that driving itself is the travel purpose, especially in daily life.

At individual level, time geography borrows some physical and mathematical concept and methods such as random walk, Brownian motion, and Levy flight

Along with the wide usage of Global Positioning System (GPS), high performance computer, and sophisticated algorithms, the high-resolution data being collected. The relevant studies also have a dramatic increase after 2005.

2.3 Discussion

The theories of travel behavior follow a positivism tradition for a long time. Economics and geography give some strong explanations for both macro and micro travel patterns. In order to remove the limitation of ideal rationality, more sociological and psychological theories and methods are introduced into this field. Gradually, people realized the normative concept is not sufficient for real world applications. More descriptive and narrative arguments appear in transportation and land use planning. An example is the shift from mobility to accessibility.

A primary trend in urban and transportation fields recently is the transition from techno-centric to socio-centric (Lanzini & Stochetti, 2020) 8 The socio-centric methods claim that accessibility is the key concept for evaluating urban sustainable mobility. This trend emphasizes the interpretations of travel behavior are context dependent and avoids generalizations.

Research in human mobility insist the positivism methodology and has some significant contributions because the individuals differences are confounded at the macro level. Under this framework, geographic distance always plays a prominent role in all human mobility theories. In adding to travel distance and Origin-Destination Matrices, Some primary metrics such as Mean Square Displacement and Radius of gyration are defined to quantitatively describe travel behaviors

A vital insight is that human behavior has two mobility roles: explorers and returners. It might be an inherent property of society, the instinct of exploring more territory and keeping together for division of labor. The explorers’ behavior is consistent with the theory of utility maximization. People are always looking for more benefit. The concept of habit also match the behavior of ‘preferential return,’ which means people are natural or nurtured likely to return to frequently visited locations or recently-visited locations.

Both gravity and opportunities theory choose population size as the source of travel demand. This is a rough assumption and is not enough to get more accurate predictions. One solution is to use empirical observed demand to calibrate the model case by case. Another way is to find more suitable variables such as residential, employment, or activity size to improve the model.

When area of interest is intra-urban, the O-D matrix records the trip connections among all paired locations. The matrix contain plenty of information including urban spatial structure, opportunities, activities and other socio-economic characteristics. The theories imply that O-D matrix have some strong connections to travel behavior in some ways. The first challenge is how to mine the information and extract some explainable elements. A limitation is that the empirical O-D matrix may only reflect the particular characteristics in that city and can not be applied to others. The second challenge is how to get a generalized interpretation,

Once choosing the individual perspective, current theories and methods are still insufficient. For example, the physical transportation network is only a part of travel decisions. Social networks with a ‘hub-and-spoke structure’ play a prominent role in finding a job. Using social media data, some studies provide valuable insight but still have a gap to form new theories.

An interdisciplinary perspective could provide a theoretical explanation for model selection. Existing mobility theories can play an anchor to identify the key variables’ property and confirm the additive and linear relation among the factors.

CMDT=Cognitive moral development theory (Kohlberg, 1984),

ITB=Ipsative theory of behavior (Frey, 1988),

NAM=Norm activation model (Schwartz, 1977,Schwartz and Howard, 1981),

SDT=Self-determination theory(Deci & Ryan, 1985),

TAM=Technology acceptance model(Davis, 1989),

TDM=Travel demand management measures,

TNC=Theory of normative conduct (Cialdini et al., 1990,Cialdini et al., 1991),

TPB=Theory of planned behavior(Ajzen, 1985,Ajzen, 1991),

VBN=Value-belief-norm (Stern, 2000,Stern et al., 1999),

MGB=Model of goal-directed behavior(Perugini & Bagozzi, 2001), ↩︎

“Prospect Theory - an Overview ScienceDirect Topics ” ( n.d. ) ↩︎

Visser ( 2013 ) ; Jiang, Yin, and Liu ( 2015 ) ; Rozenfeld et al. ( 2011 ) ; Gomez-Lievano, Youn, and Bettencourt ( 2012 ) ; Hackmann and Klarl ( 2020 ) ↩︎

Jiang and Jia ( 2011 ) ; Chen and Jiang ( 2018 ) ; Jiang ( 2018a ) ; Jiang ( 2018b ) ↩︎

Lanzini P., Stochetti A.. From Techno-Centrism toSocio-Centrism: The Evolution of Principles for Urban Sustainable Mobility [J].International Journal of Sustainable Transportation, 2020, in Press. ↩︎

TF Resource

Travel Behavior

Introduction

Topics in Travel Behavior

Representations of Decision Making Behavior in Travel Modeling

Activity-Travel Planning and Decision Making Behaviors

Joint Travel Behavior

Other Resources

Page categories

More pages in this category:

# introduction.

(opens new window) . When individuals travel to specific locations at certain times, they are engaging in activities like work (satisfying the need for resources), socializing (belonging and esteem), recreation and leisure (self-actualization), etc. Travel behavior is the process people use to organize their time to meet these needs under a variety of constraints (e.g., time, cost, social obligations).

Travel behavior is a broad topic that touches many aspects of travel demand modeling and forecasting. Kostas Goulias has stated that travel behavior modeling:

...refers primarily to the modeling and analysis of travel demand on the basis of theories and analytical methods from a variety of scientific fields. They include, but are not limited to, the use of time and its allocation to travel and activities, the use of time in a variety of time contexts and stages in the life of people, and the organization and use of space at any level of social organization, such as the individual, the household, the community, and other formal or informal groups. [2]

Some basic assumptions are often used when analyzing travel behavior. In some cases, these are generalities built from observed behavior or simplifications of complexities of human behavior. Common assumptions include the following:

  • An individual with a higher income will tend to be less sensitive to travel cost than an individual with lower income.
  • Travelers are attracted to locations with a large amount of households and jobs and will tend to travel further distances to these locations than lower density areas.
  • Single occupancy vehicle travel is generally preferred over transit, even after accounting for time and cost of travel.

(opens new window) is collected on a periodic basis to understand the local and temporal context of travel behavior. Modelers must be careful not to imbed behavioral assumptions into models that may change over time when forecasting.

To the extent that is possible, travel behaviors should be treated often as sensitivities in the model so that a range of scenarios are predicted, realizing that certain behaviors are not precise mechanical operations, but malleable based on many possibly unknowable factors. One role of the modeler, then, is to identify how travel will be impacted if certain behavioral trends continue or cease.

(opens new window) depict the time tradeoffs among activities and travel in the day.

(opens new window) but requires a subscription to access. These reviews taken together provide a solid foundation to understanding the current state of travel behavior research

# Topics in Travel Behavior

# representations of decision making behavior in travel modeling (opens new window).

Decision-making is a central element of traveler behavior modeling. Many of the behaviors represented in travel demand model are directly related to a decision making process - from choosing travel modes to selecting routes to reach a destination. The way in which these decisions are modeled, however, varies greatly for different decisions and in different model frameworks. The most common form that decision making behavior takes is that of the discrete choice model . However, many other decision making behaviors are utilized in travel models, including heuristic methods, or processes derived from artificial intelligence such as the computational process model.

# Activity-Travel Planning and Decision Making Behaviors (opens new window)

Activity-travel planning behaviors generally refer to the behavioral processes by which individuals plan, schedule and implement their day-to-day activity and travel plans. The topic of activity-travel planning encompasses many different traveler behaviors, from activity conception/generation, to the decision-making processes by which the activity plan is carried out. These behaviors are represented in various conceptional models of activity planning behavior, and are implemented in many activity-based models.

# Joint Travel Behavior (opens new window)

Joint Travel Behavior refers to how people choose to travel when considering the travel of other household members or persons in their social networks. For instance, children under driving age need to be driven to activities by parents, which requires coordination of household schedules. Household members often need to decide how to share vehicles to conduct daily activities. Some activities are more likely to be conducted with other people, like eating a meal or socializing, whereas other activities may often be conducted alone, such as commuting to work.

# Other Resources

(opens new window)

(opens new window) .

Other pages on this website include:

  • Travel Behavior of Diverse Populations
  • Using policy to affect travel behavior

# References

Maslow, A (1954). Motivation and personality. New York, NY: Harper. p. 236. ISBN 0-06-041987-3. ↩︎

(opens new window) ↩︎

← Index Accessibility →

This site uses cookies to learn which topics interest our readers.

Travel attitudes or behaviours: Which one changes when they conflict?

  • Published: 16 October 2021
  • Volume 50 , pages 25–42, ( 2023 )

Cite this article

  • Laura McCarthy 1 ,
  • Alexa Delbosc   ORCID: orcid.org/0000-0001-8744-3469 1 ,
  • Maarten Kroesen 2 &
  • Mathijs de Haas 3  

12k Accesses

12 Citations

512 Altmetric

Explore all metrics

In recent years, there has been a resurgence of interest in the ‘disagreement’ or dissonance between travel attitudes and behaviours. This has shown that when people experience travel-related dissonance they are less satisfied with their travel experience. However, what remains unclear is whether people experiencing dissonance are more likely to change their travel attitude or their behaviour, so that they are more closely aligned. Moreover, it is unclear whether and how life events, such as having a child, interact with creating or reducing travel-related dissonance. Using data from a large and well-designed longitudinal study, this paper addresses these two gaps in the literature on travel-related cognitive dissonance through an exploratory study. The findings suggest that dissonant travellers are more likely to change their segment membership than consonant travellers. Furthermore, in line with the theory of cognitive dissonance, people may adjust either their attitudes or behaviours to achieve a state of consonance. This suggests that policymakers should not only focus on subtle nudges aimed at changing attitudes (and subsequently behaviours) in desirable directions but also on implementing policies aimed at directly influencing behaviours, assuming that attitudes will follow.

Similar content being viewed by others

concept of travel behavior

Social Determinants of Mental Health: Where We Are and Where We Need to Go

Margarita Alegría, Amanda NeMoyer, … Kiara Alvarez

concept of travel behavior

Rural–nonrural divide in car access and unmet travel need in the United States

Weijing Wang, Sierra Espeland, … Dana Rowangould

concept of travel behavior

Evaluating Transportation Routes Between China and Vietnam Based on Delphi–CFPR

Avoid common mistakes on your manuscript.

Introduction

Transportation researchers have long been interested in the relationship between attitudes and travel behaviour. Most of this research focuses on the congruent relationship between attitudes and behaviour, showing that positive mode-specific attitudes predict the use of that mode (Kroesen and Chorus 2018 ) or conversely that travel behaviours shape attitudes (Tardiff 1977 ; Dobson et al. 1978 ; Kroesen et al. 2017 ). More recently, researchers have become interested in the state of ‘disagreement’ between attitudes and behaviours. When actions and attitudes do not match, people experience an unpleasant psychological state. In psychology, this state is referred to as cognitive dissonance and it has been a subject of research since the 1950s (Festinger 1957 ). When people experience cognitive dissonance, they tend to change either their attitude or their behaviour so that the two are more closely aligned.

In recent years transportation researchers have found that when people experience travel-related dissonance they are less satisfied with their travel experience (De Vos 2018 ). However, a recent review highlighted that to date no studies have been able to examine whether people change their travel behaviour to reduce travel-related dissonance (De Vos and Singleton 2020 ).

Furthermore, to date, no studies on travel-related dissonance have examined the role that life events might play in inducing (or reducing) a state of cognitive dissonance. The mobility biographies approach highlights the important role of life events, such as moving houses or changing jobs, in triggering travel behaviour change (Clark et al. 2016a ; de Haas et al. 2018 ). For instance, moving house may allow an individual with negative attitudes towards the car but frequent car-use to move to a less car-dependent location. However, if the choice of home location is constrained, it may also induce dissonance, for example, if an individual who prefers transit must move to a car-dependent area for work (see for example the ‘reverse causality hypothesis’ between the built environment and travel, (van Wee et al. 2019 )).

This paper attempts to address these two gaps in the literature on travel-related cognitive dissonance through an exploratory study. It does this through three research questions:

Over time, how stable is the state of travel-related cognitive dissonance?

Are people who experience travel-related cognitive dissonance more likely to change their travel behaviour or attitudes?

What is the impact of life events on inducing or reducing travel-related cognitive dissonance?

We explore these research questions using longitudinal panel data to examine whether mode-dissonant or mode-consonant individuals are more likely to remain in the same category in subsequent waves. Secondly, we explore whether mode-dissonant travellers are more likely to adapt their behaviour or attitudes to reduce dissonance. Finally, we explore the effect of life events on creating or reducing dissonance between travel attitudes and behaviours. Insights from these research questions may have implications for strategies encouraging travel behaviour change, identifying potential segments of the population who are more open to changing their travel behaviour.

Cognitive dissonance theory and travel behaviour

Cognitive Dissonance Theory (CDT) posits that people have an internal drive to resolve inconsistencies between attitudes and behaviours (Festinger 1957 ). If an inconsistency exists or arises between a particular attitude and behaviour, people will typically experience some level of psychological discomfort. In turn, this motivates people to adopt strategies to reduce this discomfort. Festinger ( 1957 ) outlined typical strategies to alleviate the discomfort: changing one of the dissonance conditions, changing the cognition through acquiring new knowledge that assists to alleviate the dissonance or trivialising the dissonance. For example, a person experiencing dissonance between his/her smoking behaviour and the cognition that smoking is bad for health may alter the behaviour (quit smoking) or the cognition (downplay the negative health effects of smoking). A priori, the theory of cognitive consistency assumes no dominance over which strategy is more likely to occur. Hence, based on this theory, an influence from behaviour towards attitudes is as likely as an influence from attitudes towards behaviour.

In the early days of social-psychological research, CDT was among the most heavily researched theories in the field. Literally hundreds of experiments have been conducted (Aronson 1992 ). Interestingly, however, little efforts have been made to operationalise the theory in a field setting. With respect to a particular target behaviour, one may identify four groups, two consonant ones and two dissonant ones, as depicted in Table 1 below. This approach to classifying people into categories of dissonant vs consonant has been used in studies of travel behaviour and residential location choice (Kroesen et al. 2017 ; Kamruzzaman et al. 2021 ). By assessing how membership of these four groups evolves, the main premise of CDT may already be tested in an observational setting, namely the notion that members of consonant groups are more likely to stay in their respective groups compared to members of the dissonant groups, who may be expected to either change their behaviour or their attitude towards the behaviour or adjust their circumstances. This approach assumes the adjustment of attitudes or behaviours is the only mechanism adopted to alleviate dissonance. Moreover, it does not account for the scale or variations in other background factors that may influence the extent and type of reduction strategies adopted. Nonetheless, this simplified approach allows for the fundamental concept of CDT to be tested in a field setting.

In this paper, we aim to return to the exploration of changes in attitude-behaviour inconsistencies over time, focusing on travel behaviour in particular. A few recent studies have explored this topic. Kroesen et al. ( 2017 ) used latent class analysis to identify groups of consonant and dissonant travellers and found, in line with CDT, that consonant travellers were less likely to change travel behaviour over time than dissonant travellers. De Vos ( 2018 ) also identified groups of consonant/dissonant travellers for different modes, revealing sizeable groups of dissonant travellers, especially regarding the use of the bicycle. The contribution of that study rests in the link that was made with travel satisfaction. In line with expectations, respondents travelling with their preferred travel mode (i.e., consonant travellers) seemed to experience their trip more positively compared to people travelling with a non-preferred travel mode (i.e., dissonant travellers). This result is well in line with CDT, in which inconsistency is assumed to create a state of stress/arousal, which can also be regarded as a state of dissatisfaction.

The present study also begins by classifying and describing consonant/dissonant travellers. However, this study extends the analysis to a second point in time to assess how stable these states are over time, and whether people are more likely to change their attitude or their behaviour to reduce dissonance. Given this is a major gap in the field, (identified in a recent review of CDT research (De Vos and Singleton 2020 )), it represents the first contribution of this paper.

Life events and cognitive dissonance

The second contribution of this paper is to explore the relationship between life events and travel-related dissonance. The mobility biographies approach, first introduced by Lanzendorf ( 2003 ), provides a framework for understanding travel behaviour changes throughout the life course. The framework introduces the theory of multiple life domains and recognises that a change in one domain or area will have ramifications for other areas (Lanzendorf 2003 ). As comprehensive reviews of the state of the field and the development of the theoretical framework have recently been conducted (see Müggenburg et al. ( 2015 ) and Scheiner ( 2018 )), we will instead focus this discussion on aspects of the framework relevant to our study.

A key concept emerging from the mobility biographies approach is the notion of stability and change in travel behaviour throughout the life course. Drawing on the role of habit as an important determinant of travel mode choice (Banister 1978 ; Aarts et al. 1997 ), the approach posits that life events, such as moving house or changing jobs, can disrupt stable travel routines. A life event can prompt changes to circumstances or context, which, in turn, requires an individual to switch from habitual to reflective thinking about their travel mode choices. It is these periods, in which travel routines are disrupted and habits are weakened, that provide a valuable opportunity to intervene and encourage the adoption of more sustainable travel choices (Verplanken et al. 2008 ).

Studies in this field have tended to focus on how behaviour, specifically mode use, changes following (or in anticipation of) a life event. Life events associated with an increase in car-based mobility include childbirth (Oakil et al. 2016 ; de Haas et al. 2018 ) and entering the workforce for the first time (Busch-Geertsema and Lanzendorf 2017 ). In contrast, life events associated with a decrease in car-based mobility are often associated with changes resulting in a reduction of income or change in activity patterns, such as unemployment or retirement (Oakil et al. 2014 ).

Overall, research in this field has shown that individuals are more likely to change their travel behaviour as a result of a life event than during stable circumstances (Clark et al. 2016b ). A ‘dissonant’ life event may induce an inconsistency between the frequency of using a travel mode and the attitudes towards that travel mode. For instance, in the case of childbirth, cycling frequency may reduce but positive attitudes towards cycling may remain. Conversely, a dissonance-reducing life event may create an opportunity for an individual to align their travel mode use with their attitudes to that travel mode. For instance, an individual with positive attitudes towards public transport moving home from a car-dependent location to a transit orientated location may reduce dissonance. It should be noted here that there are differing views regarding the hypothesized role of life events and inconsistencies between travel attitudes and behaviours. While Verplanken et al. ( 2008 ) posits that life events can prompt self-activation, enabling an individual to actively reflect on their optimal travel choices, applying cognitive dissonance theory, we are proposing that the life event may provide an opportunity to reconcile mismatches between travel attitudes and behaviours (rather than the self).

Using the four profiles of dissonant/consonant travellers described in Table 1 , we explore whether respondents who have experienced a life event between the two waves are more likely to transition into a different profile in the second wave. This analysis, which forms the second contribution of this paper, is largely exploratory.

Data source

The Netherlands Mobility Panel (knowns as the MPN) is an annual household panel survey that was set up to study the short-run and long-run dynamics in the travel behaviour of Dutch individuals and households, and to determine how changes in personal- and household characteristics, and in other travel-related factors, correlate with changes in travel behaviour (Hoogendoorn-Lanser et al. 2015 ). The first wave of data collection started in 2013 and the panel consists of approximately 2,000 complete households. Respondents are recruited by means of a screening questionnaire in which an adult household member is asked whether the whole household wants to participate in the MPN for several years. Yearly, after entering the panel, the same adult household member fills out a household questionnaire to gather basic information about the household. Furthermore, all household members of 12 years and older are asked to fill out an individual questionnaire and complete a three-day online travel diary. The individual questionnaire consists largely of questions that are repeated yearly. This includes questions regarding any life events that respondents have experienced in the previous 12 months. However, a special topic is repeated bi-annually. In the second and fourth waves of the MPN the special topic focused on attitudes and perceptions with regard to various modes of transportation. Therefore, the second (2014) and fourth (2016) waves of the MPN are used in this study. Table 2 presents basic demographics and life events among the survey sample.

In order to account for attrition and to maintain a representative sample, additional households are recruited yearly. In the second wave, additional focus was placed on recruiting certain groups (such as young and low educated people) since they were somewhat underrepresented in the first wave and had higher nonresponse levels. Due to attrition and recruitment of new households, there are some slight variations between waves in terms of sample composition. However, based on the so-called Gold Standard (which reflects the composition of the Dutch population on several personal- and household characteristics) it can be concluded that the sample is fairly representative for the Dutch population. The largest deviation is found on educational level, with an underrepresentation of low-educated people and an overrepresentation of high-educated people.

Defining segment membership

Table 3 presents the two measures used to determine segment membership: a composite measure of mode specific attitudes and the frequency of mode use. The measures derived from two sets of questions in the MPN survey. The MPN asks about attitudes towards travel by car, cycling and public transit. Six items are asked (Comfort/Relaxing/Saves time/Safe/Flexible/Pleasurable) measured on a five-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree”. Cronbach’s Alpha for all modes exceeded 0.8 (Table 3 ), indicating a sufficient level of internal consistency to allow for a composite score to be generated. Scores for each of the six items were combined, creating a scale ranging from 6 to 30. An average of the six items was then generated and rounded to the nearest integer, providing a value of between 1 and 5. The measure regarding mode use frequency derives from a question asking about frequency of car, public transport and bicycle use over the previous year. Responses were recoded to a five-point scale to align with the attitude measure.

The attitudinal measure does not directly align with more common psychological literature typically operationalising dissonance as an unpleasant feeling or tension. Nonetheless, cognitive dissonance theory explicitly states that people attempt to align their attitudes with behaviour. As such, the attitudinal measure we use in our study is still largely aligned with what CDT posits.

We used the two measures to categorise respondents into four groups based on their behaviour (frequent or infrequent) and attitudes (negative or positive). Each respondent was categorised into one of the four groups (Frequent-Positive; Frequent-Negative; Infrequent-Positive; Infrequent-Negative) for each mode (car, cycling and public transport). Respondents were categorised as “Infrequent” if they used the travel mode three days per month or less and “Frequent” if they used the travel mode at least weekly.

To allow a more equitable distribution of the four categories, respondents whose attitude towards a particular mode was a value of between 1 and 3 were classified as “Negative” and 4 or 5 as “Positive”. Classifying respondents with neutral attitudes towards cycling and car in either the positive or negative category had a minimal bearing on the results. However, as a large number of respondents held neutral attitudes towards public transport, changing whether they were grouped in the positive or negative category lead to a significant change in the size of the consonant and dissonant segments for this mode. However, if “neutral” attitudes were re-classified as “positive” for public transport use, this resulted in multiple segments with very few respondents. Therefore we continued the analysis with all “neutral” responses coded as negative. While this approach is imperfect, and the public transport results must be treated with some caution, it ensures all segments are of a sufficient size to allow for meaningful analysis.

Table 4 shows the size of each segment and the percentage of respondents in each behaviour or attitudinal category. Overall, the largest segments among each of the three modes are consonant. Given the considerably lower frequency of public transport use compared with cycling and car use among respondents, the largest segment size in the Car and Cycle mode is Frequent-Positive, whereas among the Public Transport mode it is the Infrequent-Negative group. The Car model has the largest share of consonant respondents (81%), followed by Cycling (76%) and Public Transport (68%).

Once the travellers were classified into consonant and dissonant groups, we conducted a logistic regression model that included initial segment membership for each of the modes. Seven life events were included and four exogenous variables (gender, employment status, age and location) as active covariates. The seven life events included: childbirth, partnership formation and dissolution, starting an education course, starting or stopping working and changing employment.

Segment profiles

Table 5 outlines the demographic characteristics of each of the twelve groups. As the characteristics of each segment is not the focus of this paper, these will not be discussed in detail. However, the profile of each segment is plausible. For example, those travellers typically comprising ‘captive’ transport markets, such as students (who are entitled to free public transit in The Netherlands), are underrepresented in the Frequent-Positive Car group but overrepresented in the Frequent-Negative Cycling and Public Transport Groups. This suggests that while members of these groups have a preference for car-based mobility, their current financial circumstances make car ownership unattainable and instead they use public transport or cycle. Similarly, Frequent Public Transport users, both negative and positive, are overrepresented in highly urbanised locations and underrepresented in rural locations, likely reflecting the different level of public transport service in the respective locations.

Stability of travel-related cognitive dissonance

Table 6 shows the movements between groups between Wave 1 (2014) and 2 (2016) for car, cycling and public transit modes respectively. Given earlier research (Kroesen et al. 2017 ), we would expect the consonant segments to be the most stable (and have the highest probability of remaining in the same segment in the second wave) and the dissonant segments to be the least stable. This is the case for the largest consonant segments within each mode. Approximately 89% of the FP Car segment remained in the same segment in Wave 2 with similar proportions for the FP Cycling (83%) and IN Public Transit segments (74%). However, the smallest consonant segments (IN Car, IN Cycling and FP Public Transit) were less likely to remain in the same segment in the second wave. This is likely due to forming a much smaller proportion of the sample (5% car; 15% cycling; 7% PT) and, as such, slight changes (which may in part be random, e.g. when resulting from random from measurement errors) may appear more pronounced.

Interestingly, across all modes, dissonant travellers were more likely to remain in the Infrequent-Positive segment between waves than their counterparts in the Frequent-Negative dissonant segment. For instance, approximately half of the 2014 IP segment for car (53%), cycling (51%) and public transport (48%) remained in this state in 2016. In contrast, the proportion of respondents remaining in the Frequent-Negative segments between waves was 34% (car), 32% (cycling) and 42% (public transport). This suggests infrequent use and positive attitudes are perhaps less of a motivator to reduce dissonance than frequent use and negative attitudes.

These findings address the first research question of this paper. The next section explores whether people who experience travel-related cognitive dissonance are more likely to change their travel behaviour or their attitudes.

Change in behaviour and attitudes among formerly dissonant travellers

A core aim of this paper was to explore whether respondents experiencing travel-related cognitive dissonance more likely to change their travel behaviour or attitudes. To explore this, we assessed the movements between segments, between waves. Respondents in one of the dissonant segments in 2014 (either ‘Frequent-Negative’ or ‘Infrequent-Positive’) could transition into a consonant segment in 2016 by adjusting either their attitudes or their behaviour.

We would expect Frequent Negative travellers to be travelling against their attitudes due to external factors, such as poor transport infrastructure or conceivably time, income or family limitations. As such, we would expect an adjustment in attitudes in order to move to a consonant group (FP). Interestingly, however, whether respondents in a dissonant segment changed their attitude or behaviour varied depending on the mode and their original segment (see Table 7 )

For both car travel and cycling, people who were IP in 2014 were more likely to change their behaviour than their attitude to become consonant travellers. Over 80% of IP drivers in 2014 who became consonant in 2016 did so because they drove more in 2016; 54% of IP cyclists did the same. In contrast, FN drivers and cyclists in 2014 who became consonant in 2016 were more likely to change their attitudes; 87% of FN drivers and 70% of cyclists who changed classification did so because they had a more positive attitude to the respective modes in 2016.

The pattern was reversed for public transport. Here, 73% of IP transit users who became consonant between 2014 and 2016 did this by reducing their attitude to public transport, becoming IN users. Among the FN public transport riders who became consonant in 2016, 80% did this by reducing their ridership (becoming IN).

These contrasting patterns may be explained by the baseline attitudes toward the three modes and considerably lower use of public transport among the sample. Overall attitudes toward cars and bicycles are more positive than public transport and only 20% of respondents are categorised as ‘frequent’ public transport users compared with 83% of car travellers and 70% of cyclists.

The final section of the findings addresses the third research question, exploring whether life events induce or reduce travel-related cognitive dissonance.

Life events and changes in dissonance

Table 8 presents the results of paired sample t-tests showing the changes in mean attitudes and behaviour between 2014 and 2016. The changes are presented overall and by respondents who have experienced a life event. It should be noted that these are derived from ordinal scales and no correction was made for multiple comparisons; however these initial results provide an indication of the direction of change between survey waves. Among the overall sample, there are marginally significant declines in cycling and increases in public transit use. There were no significant changes to attitudes towards each mode between the survey years. Interestingly, among the life events examined, only behaviour changes are significant suggesting that when experiencing a life event, behavioural rather than attitudinal changes create dissonance. However, as we are examining the aggregate changes this may mask changes apparent within individual groups.

Most life events analysed were associated with some behaviour change between the two survey years. Echoing findings from previous studies examining the effects of different life events on travel mode use, childbirth is associated with a slight decline in cycling (de Haas et al. 2018 ). Of note, partnership formation is associated with an increase in car-use and decrease in cycling. This is likely attributed to the new partner owning a vehicle and, as a result, more joint trips being conducted by car rather than cycling. An alternative interpretation could also be, however, that it is more cost-efficient to own a vehicle as couple, and, as a result respondents may have been more likely to purchase a vehicle in the intervening two years between the survey waves. Starting and stopping working is associated with an increase and decrease in car use, respectively. Stopping working is also associated with an increase in public transit use. These mode changes are likely a result of income or time-budget changes, as a result of changing employment status.

Starting an education course is associated with a decrease in cycling. This may reflect the local context, as in The Netherlands students can travel for free on public transport to and from their education institution. Finally, changing jobs (but not changing job location) is associated with an increase in car use and decrease in public transport use. This could reflect gaining a promotion and a subsequent increase in salary or obtaining a company car.

Table 9 presents the results of a logistic regression model that included initial segment membership for each of the modes and shows the probabilities of changing segments between 2014 and 2016 by type of life event. The results have been presented in this format rather than logit parameters for easier interpretation. Statistically significant movements between segments were evident among respondents who have experienced childbirth, partnership formation and dissolution, starting an education course and changing employment or employment location. However, across all life events and modes, respondents were equally likely to become either more consonant or more dissonant following a life event.

Of note, childbirth was associated with a greater likelihood of forming part of the IP Cycling segment and a lower likelihood of forming part of the FP Cycling segment. This reflects the decline in cycling evident in Table 6 and echoes previous research showing new parenthood is associated with a decline in cycling (Scheiner and Holz-Rau 2013 ; de Haas et al. 2018 ). Interestingly, childbirth is also associated with an increasing likelihood of forming part of the IN Public Transit segment. As public transit is commonly used for commuting, this may reflect the changes in activity patterns as result of taking parental leave and reduced use of this mode for work travel.

Partnership formation and dissolution were associated with slight movements between the segments. Partnership dissolution was associated with an increased likelihood of forming part of the IP Car and IP PT segment. In contrast, partnership formation increased the likelihood of forming part of the IN Cycling and PT segments. These changes could be attributed to the new partner bringing a car to the household (or, in the case of dissolution, taking away a car). Among respondents who experienced a partnership formation, vehicle ownership increased from 0.8 vehicles per household to 1.0 vehicles per household (compared to a slight decline in vehicle ownership across the entire survey sample). This suggests the new partner may bring a vehicle to the relationship or they may be more likely to purchase a new vehicle as a couple, with subsequent implications for the respondents’ daily travel behaviour.

Starting an education course is associated with a decreased likelihood of forming part of the Infrequent-Negative PT segment and increased likelihood of forming part of either Frequent Positive or Frequent Negative PT segment (although the latter two changes are not significant). This likely reflects the growth of public transit use during tertiary study and provision of reduced fares available to students.

Finally, the two employment life events were associated with only marginal movements between segments. Changing job was associated with a greater likelihood of forming part of the FP Car segment while changing job location was associated with a greater likelihood of forming part of the IN PT segment and a less likely to form part of the FP PT segment.

For some life events the results are difficult to interpret. Perhaps this is because the life event is not captured at a sufficiently granular level. For instance, life events resulting in location changes (such as moving house, changing jobs or changing job locations) will likely have varying effects depending on the spatial and temporal context of the change. Surprisingly, no significant effects were evident for starting or stopping employment.

Discussion and conclusion

This paper explored stability among dissonant and consonant travellers and whether dissonant travellers are more likely to adjust their behaviour or attitudes to reach a state of consonance. Moreover, it explored the role of life events in creating or reducing dissonance between travel attitudes and behaviours. Differences between travel attitudes and behaviour were explored by public transit, cycling and car use, using panel data. It should be noted there are limitations regarding the measure we used to assess dissonance and the dissonance strategies we tested (only behaviour or attitude change). The psychology literature has identified a range of other mechanisms to reduce dissonance, such as self-forgiving, denial of responsibility or forgetting about the dissonance (Kruglanski et al. 2018 ), that depend on the intensity of negative feeling and familiarity of the situation (Cancino-Montecinos et al. 2020 ). In a travel behaviour context, it is plausible people could adopt a range of these and other strategies to reduce dissonance. The implications of this limitation are discussed below.

Addressing our first research question, consistent with earlier research (Kroesen et al. 2017 ), between the survey years, dissonant travellers were more likely to change their segment membership than consonant travellers. Interestingly, dissonant travellers were more likely to remain in the Infrequent-Positive segment between waves than travellers in the Frequent-Negative segment. This suggests that infrequent use and positive attitudes are perhaps less cognitively distressing than frequent use and negative attitudes. This may be evidence that people are practicing a range of mental mechanisms to reduce this dissonance, such as self-forgiving or denial of responsibility (e.g. ‘I’d love to cycle but it’s not my fault it isn’t safe) (Kruglanski et al. 2018 ).

Should this indeed be true, then a straightforward implication for policy would be that positive attitudes, e.g. towards bicycle use, are not enough to increase the uptake of cycling. Instead, policies that negatively influence the attitudes towards alternative modes (e.g. the car) may potentially be more effective. Similarly, encouraging an increase in behavioural frequency for certain modes (e.g. encouraging infrequent-negative cyclists to cycle more frequently), may lead to more positive attitudes towards this mode.

In addition, from a scientific perspective, it would be interesting to explore whether dissonance resulting from infrequent use and positive attitudes is indeed less distressing than dissonance resulting from frequent use and negative attitudes. As shown by De Vos ( 2018 ) dissonance between the chosen and preferred mode also translates itself into travel dissatisfaction. This concept could be used to assess whether those belonging to the segment “frequent use and negative attitudes” are (even) more dissatisfied with their travel behaviour than those belonging to the segment “infrequent use and positive attitudes”.

Our second research question explored whether dissonant travellers were more likely to adjust their behaviour or attitudes. The results depended on the mode and their original class. A similar pattern was evident between car and cycling modes. Infrequent car travellers and cyclists with positive attitudes were more likely to increase their use than adjust their attitudes to reach a state of consonance in the following wave. Conversely, among frequent car-users and cyclists with negative attitudes, their attitudes were likely to become more favourable in the following wave. The reverse picture was evident for public transport; this is likely to be attributed to the baseline use and attitudes towards this mode being considerably lower than for car and cycling.

A general take-way from the results above is that, in line with the theory of cognitive dissonance, people may adjust both their attitudes and behaviours to achieve a state of consonance. From a policy perspective, this means that policymakers should not only focus on subtle nudges aimed at changing attitudes (and subsequently behaviours) in desirable directions but may also implement policies that aim to directly influence behaviours, e.g. by setting rules or pricing policies, assuming that attitudes will follow.

Finally, we explored whether life events induced or reduced dissonance. Nearly all of the life events included in the analysis were associated with changing levels of cognitive dissonance across at least one travel mode. However, across all life events participants were equally likely to become more or less consonant following a life event. And while certain life events were associated with behavioural changes between the two survey years, no life events were associated with attitudinal changes towards the travel mode. This suggests that following life events, behavioural rather than attitudinal changes tend to create dissonance. Future analysis, using data from subsequent waves, could explore whether there is a lag in attitude change following the behavioural change.

Life events associated with an increase in car-based mobility (either as a result of increasing car-use or decreasing use of cycling or public transit) included: childbirth, moving in with a partner, starting work and changing jobs. An increase in car-use, over time, is likely to contribute to the adoption of more favourable attitudes towards car-use. This, in turn, makes it less likely individuals will return to their former sustainable travel patterns, if, or when, their circumstances change. The adoption of more favourable attitudes towards car-use poses problems for transportation planners and policymakers seeking to encourage more sustainable travel modes. Further qualitative research may assist in unpicking the processes by which travel attitudes are adjusted (or not) to align with new travel behaviours. This would provide important insights to contribute to policies tasked at curbing the adoption of car-based mobility associated with certain life events.

Although the panel data provides a rich and detailed source of life events, several limitations exist. Due to the small number of respondents experiencing certain life events, some of the results were difficult to interpret. Furthermore, the classification of life events, especially those that instigated a relocation of work or home location, could be analysed at a more granular level. As data from future years becomes available, and more participants experience these life events, this will assist with interpreting the changing levels of cognitive dissonance associated with experiencing less common life events.

Further limitations regard the measures and classification process we adopted. As this was an exploratory analysis, we conducted an a priori classification. However, future analyses could adopt a post-hoc classification process which may yield more nuanced segments of behavioural and attitudinal adjustments made by dissonant travellers. Moreover, we assumed travellers were only to reach a consonant state through adjusting their attitudes or behaviours where we have acknowledged that multiple strategies (such as forgetting or trivialising) may also be adopted (Kruglanski et al. 2018 ). In addition, we did not account for the magnitude of the dissonance or familiarity of the situation in influencing the dissonance reduction strategy adopted (Cancino-Montecinos et al. 2020 ). As this was an exploratory study, aiming to test fundamental concepts of CDT, these topics are proposed for future research.

The panel data used in this analysis was conducted before the COVID-19 pandemic. Given the anticipated structural changes to employment practices prompted by the pandemic, it may provide an opportunity for respondents to reconcile disparities between their travel attitudes and behaviour. For instance, respondents in the ‘Frequent-Negative’ categories for car or public transport may have the opportunity to work from home, reducing the need for commuting. Initial analysis of MPN respondents suggests that, of those respondents working at home during the pandemic, over a quarter expect to work from home more in the future (de Haas et al. 2020 ). Data from future waves will reveal the extent that this has transpired into changing travel practices and the subsequent impact this may have on reducing travel related cognitive dissonance.

Aarts, H., Verplanken, B., Van Knippenberg, A.: Habit and information use in travel mode choices. Acta Physiol. (oxf.) 96 (1–2), 1–14 (1997)

Google Scholar  

Aronson, E.: The return of the repressed: dissonance theory makes a comeback. Psychol. Inq. 3 (4), 303–311 (1992)

Article   Google Scholar  

Banister, D.: Influence of habit formation on modal choice—a heuristic model. Transportation 7 (1), 5–18 (1978)

Busch-Geertsema, A., Lanzendorf, M.: From university to work life—jumping behind the wheel? Explaining mode change of students making the transition to professional life. Transp. Res. Part a: Policy Pract. 106 , 181–196 (2017)

Cancino-Montecinos, S., Björklund, F., Lindholm, T.: A general model of dissonance reduction: unifying past accounts via an emotion regulation perspective. Front. Psychol. 11 , 540081 (2020)

Clark, B., Chatterjee, K., Melia, S.: Changes in level of household car ownership: the role of life events and spatial context. Transportation 43 , 565–599 (2016a)

Clark, B., Chatterjee, K., Melia, S.: Changes to commute mode: the role of life events, spatial context and environmental attitude. Transp. Res. Part a: Policy Pract. 89 , 89–105 (2016b)

de Haas, M.C., Scheepers, C.E., Harms, L.W.J., Kroesen, M.: Travel pattern transitions: applying latent transition analysis within the mobility biographies framework. Transp. Res. Part a: Policy Pract. 107 , 140–151 (2018)

de Haas, M., Faber, R., Hamersma, M.: How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: evidence from longitudinal data in the Netherlands. Transp. Res. Interdiscip. Perspect. 6 , 100150 (2020)

De Vos, J.: Do people travel with their preferred travel mode? Analysing the extent of travel mode dissonance and its effect on travel satisfaction. Transp. Res. Part a: Policy Pract. 117 , 261–274 (2018)

De Vos, J., Singleton, P.A.: Travel and cognitive dissonance. Transp. Res. Part a: Policy Pract. 138 , 525–536 (2020)

Dobson, R., Dunbar, F., Smith, C.J., Reibstein, D., Lovelock, C.: Structural models for the analysis of traveler attitude-behavior relationships. Transportation 7 (4), 351–363 (1978)

Festinger, L.: A theory of cognitive dissonance. Stanford University Press, Stanford, CA (1957)

Book   Google Scholar  

Hoogendoorn-Lanser, S., Schaap, N.T.W., OldeKalter, M.-J.: The Netherlands mobility panel: an innovative design approach for web-based longitudinal travel data collection. Transp. Res. Procedia 11 , 311–329 (2015)

Kamruzzaman, M., Giles-Corti, B., De Vos, J., Witlox, F., Shatu, F., Turrell, G.: The life and death of residential dissonants in transit-oriented development: a discrete time survival analysis. J. Transp. Geogr. 90 , 102921 (2021)

Kroesen, M., Chorus, C.: The role of general and specific attitudes in predicting travel behaviour—a fatal dilemma? Travel Behav. Soc. 10 , 33–41 (2018)

Kroesen, M., Handy, S., Chorus, C.: Do attitudes cause behavior or vice versa? An alternative conceptualization of the attitude-behavior relationship in travel behavior modeling. Transp. Res. Part a: Policy Pract. 101 , 190–202 (2017)

Kruglanski, A.W., Jasko, K., Milyavsky, M., Chernikova, M., Webber, D., Pierro, A., Di Santo, D.: Cognitive consistency theory in social psychology: a paradigm reconsidered. Psychol. Inq. 29 (2), 45–59 (2018)

Lanzendorf, M.: Mobility biographies. A new perspective for understanding travel behaviour. In: 10th International Conference on Travel Behaviour Research (IATBR). Lucerne (2003)

Müggenburg, H., Busch-Geertsema, A., Lanzendorf, M.: Mobility biographies: a review of achievements and challenges of the mobility biographies approach and a framework for further research. J. Transp. Geogr. 46 , 151–163 (2015)

Oakil, A.T.M., Ettema, D., Arentze, T., Timmermans, H.: Changing household car ownership level and life cycle events: an action in anticipation or an action on occurrence. Transportation 41 (4), 889–904 (2014)

Oakil, A.T.M., Manting, D., Nijland, H.: Dynamics in car ownership: the role of entry into parenthood. Eur. J. Transp. Infrastruct. Res. 16 (4), 661–673 (2016)

Scheiner, J.: Why is there change in travel behaviour? In search of a theoretical framework for mobility biographies. Erdkunde 72 (1), 41–62 (2018)

Scheiner, J., Holz-Rau, C.: A comprehensive study of life course, cohort, and period effects on changes in travel mode use. Transp. Res. Part a: Policy Pract. 47 , 167–181 (2013)

Tardiff, T.J.: Causal inferences involving transportation attitudes and behavior. Transp. Res. 11 (6), 397–404 (1977)

van Wee, B., De Vos, J., Maat, K.: Impacts of the built environment and travel behaviour on attitudes: theories underpinning the reverse causality hypothesis. J. Transp. Geogr. 80 , 102540 (2019)

Verplanken, B., Walker, I., Davis, A., Jurasek, M.: Context change and travel mode choice: Combining the habit discontinuity and self-activation hypotheses. J. Environ. Psychol. 28 (2), 121–127 (2008)

Download references

Author information

Authors and affiliations.

Department of Civil Engineering, Institute of Transport Studies, Monash University, 23 College Walk, Clayton, VIC, 3800, Australia

Laura McCarthy & Alexa Delbosc

Faculty of Technology, Policy and Management, TU Delft, Building 31. Jafalaan 5, 2628 BX, Delft, The Netherlands

Maarten Kroesen

KiM Netherlands Institute for Transport Policy Analysis, Bezuidenhoutseweg 20, 2594 AV, The Hague, The Netherlands

Mathijs de Haas

You can also search for this author in PubMed   Google Scholar

Contributions

Laura McCarthy: Research conception, research design, data analysis, results interpretation, lead paper writing. Alexa Delbosc: Research design, results interpretation, paper writing. Maarten Kroesen: Research design, results interpretation, paper writing. Mathijs de Haas: Data analysis, paper writing.

Corresponding author

Correspondence to Alexa Delbosc .

Ethics declarations

Conflict of interest.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

McCarthy, L., Delbosc, A., Kroesen, M. et al. Travel attitudes or behaviours: Which one changes when they conflict?. Transportation 50 , 25–42 (2023). https://doi.org/10.1007/s11116-021-10236-x

Download citation

Accepted : 21 September 2021

Published : 16 October 2021

Issue Date : February 2023

DOI : https://doi.org/10.1007/s11116-021-10236-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Cognitive dissonance
  • Travel behaviour
  • Life events
  • Find a journal
  • Publish with us
  • Track your research

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Elsevier - PMC COVID-19 Collection

Logo of pheelsevier

COVID-19 and its long-term effects on activity participation and travel behaviour: A multiperspective view

Bert van wee.

a Transport and Logistics Group, Faculty Technology, Policy and Management, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands

Frank Witlox

b Department of Geography, Ghent University, Ghent, Belgium

This paper discusses possible long-term effects of COVID-19 on activity-travel behaviour. Making use of theories and concepts from economics, psychology, sociology, and geography, this work argues that lasting effects can be expected, and specifically that peak demand among car and public transport users may be lower than if the pandemic would never have happened. The magnitude of such effects at the aggregate level in terms of the total travel time of all inhabitants of a country or state is likely limited. Such lasting effects imply that additional infrastructure extensions to reduce congestion on roads and crowding in public transport might have a lower benefit-cost ratio than would be the case without these impacts. The paper discusses avenues for future research, including work on the role of attitude changes, the formation of new habitual behaviour, new social norms and practices, well-being effects, and the role of Information and Communication Technologies (ICT).

1. Introduction

The impact of COVID-19 on travel behaviour has become a major focus. Many papers on this issue have already been published, and very likely, many others will be published in the coming years. For example, Beck and Hensher (2020) show that from late May to early June 2020 in Australia, people started travelling less, especially by car, and for shopping, social and recreational activities. Lamb et al. (2020) show that the decision to fly among people in the USA depends on perceived levels of COVID-19 threat, agreeableness, affect, and fear. Parady et al. (2020) show that in Japan, social influences lead to self-restriction. Van der Drift et al. (2021) , for the Netherlands, find strong increases in cycling and that not everyone needs to travel during peak hours, whereas Hook et al. (2021) , for Flanders, find an increase in so-called undirected travel trips (i.e., travel without a specific destination).

Of course, many more empirical papers thus far have studied activity-travel behaviour in the short to medium term because the outbreak of the virus only started in late 2019 in China and in 2020 in most other parts of the world. A very important question is “what will happen after the pandemic is over?” To what extent can lasting impacts of COVID-19 on activity-travel behaviour be expected? Will ‘the new normal’ remain or only temporarily influence behaviour? If there is a lasting impact, which type of impact will result, and what will this mean for, among other factors, the capacity of transport systems, the demand for office space, and the retail and Information and Communication Technologies (ICT) sectors? The answers to such questions are relevant not only to science but also to policy makers because societal challenges (safety, the environment, health, and congestion) and the benefits and drawbacks of policy options depend on travel behaviour developments.

One means to answer this question is to examine comparable (potentially) disruptive trends. Helpful in this respect is the literature review of van Cranenburgh et al. (2012) , which shows that most (potentially) disruptive trends, such as 9–11, the oil crises, and ICT, only had relatively minor lasting effects. The authors estimate travel behaviour indicators to be influenced by 5 to 10% (the decrease in travel behaviour indicators such as the number of trips or kilometres travelled is on the order of magnitude of 5–10%). Based on these results, it can be hypothesized that the long-term effects of COVID-19 can easily be overestimated. On the other hand, several of the trends reviewed by van Cranenburgh et al. (2012) only lasted a relatively short period of time, such as the oil crises. This finding is corroborated by Chng et al. (2018) , Lattarulo et al. (2019) and Circella (2021) , who, in the context of analysing the long-term consequences of observed changes in travel behaviour, find that these changes are difficult to realize. Hence, the results of the past are no guarantee for the future.

Another means to shed light on the long-term effects of COVID-19 involves reflecting on such effects based on theories and theoretical concepts. This is the scope of this paper. Our main question is as follows: What do dominant theories and concepts tell us about possible long-term effects of COVID-19 on travel behaviour and activity participation? It is important to realize that this paper was written during the pandemic (March 2021), when research on long-term effects could not be carried out (an exception being research conducted in countries where the COVID-19 virus seems to have disappeared). Therefore, it is quite speculative to assume specific long-term impacts.

The methodology used to answer this question involves a combination of selecting dominant theories and concepts; identifying determinants of travel behaviour according to these theories and concepts; and finally reflecting on possible long-term effects of COVID-19 making use of these theories, concepts and determinants.

With respect to theories, we draw upon the fields of economics, psychology, sociology, and geography, which are all relevant disciplines for studying travel behaviour ( Dijst et al., 2013 ; Van Acker et al., 2010 ). By far, the economic theory most commonly used to study travel behaviour is random utility theory (RUT) ( Varian, 1992 ). In the area of psychology, the theory of planned behaviour (TPB) ( Ajzen, 1991 ) is very frequently used to understand behavioural choices. From sociology, we borrow social practice theory (SPT) ( Shove et al., 2012 ), and from geography, we have the theory of time geography (TG) ( Hägerstrand, 1970 ). This paper departs from these four theories. In addition, following Dijst et al. (2013) , this work builds upon the psychology-based needs, opportunities and abilities (NOA) model ( Vlek et al., 1997 ) because it nicely complements the TPB in some respects. Next, the paper covers three concepts: habitual behaviour ( Verplanken et al., 1997 ), attitude changes, and constant travel time budgets. The basis for using these theories and concepts is first that these theories largely dominate travel behaviour studies and second that the combination of theories and concepts allows for a comprehensive understanding of possible long-term impacts of COVID-19 on activity-travel behaviour. However, we realize that the selection of theories is open for debate. We argue that the concepts that we select, such as utility, attitude changes, and constraints, labelled ‘determinants for travel behaviour’ in Table 1 (see below), are more important than the choice for the specific theory underpinning the existence of these concepts. The reader might have a preference for other specific theories underpinning the importance of these concepts.

Determinants for behaviour.

TPB: Theory of Planned Behaviour, NOA: Needs, Opportunities, and Abilities Model, SPT: Social Practice Theory, UT: Utility Theory, TG: Time Geography.

The paper is limited to a focus on travel behaviour and activities as far as they are relevant to travel behaviour. For example, the substitution of onsite activities with online activities is within the scope of this work, but the substitution of forms of online activities not influencing travel behaviour falls outside its scope.

Section 2 presents the key determinants of travel and activity behaviour. This is followed by a discussion of the potential lasting effects of COVID-19 based on theories and theoretical concepts. Section 4 summarizes the most important conclusions and discusses findings presenting a research agenda.

2. Key determinants of activity-travel behaviour

The model commonly used to determine the factors that explain and predict activity-travel behaviour is grounded in the activity-based paradigm ( Kitamura, 1988 ; Axhausen and Gärling, 1992 ; Ortúzar and Willumsen, 2011 ). In essence, such a model aims to answer basic what, why, where, how, when, and with whom questions. Travel is derived from activity participation. Activities can be spatially localized or not and/or are time constrained or not, leading to more, less, or no travel. When bounded in space and time, choices relating to trip destinations, means of transport, routes, and timing are subject to individual, household, and broader societal perceived constraints, opportunities, preferences, attitudes and social norms.

Table 1 lists determinants that influence behaviour according to one or more of the theories introduced above.

The determinants/concepts listed in this table will be used to discuss the possible long-term impacts of COVID-19 according to the selected theories. Note that the theories and concepts are not necessarily mutually exclusive. Some overlap exists. For example, UT can explain why at the aggregate level, the amount of time people spend on travel is quiet constant (see below). A strong motivation or intention for some behaviour corresponds with the high utility of that behaviour. A low level of perceived behavioural control corresponds with the high disutility of that behaviour. Constraints to activity scheduling as recognized in time geography influence the feasibility of some choice options.

Based on the many publications focused on COVID-19 and travel and activity behaviour (see some examples above), we hypothesize that the dominant impacts of COVID-19 on travel and activity behaviour are first that many people might have become more experienced with respect to online activities substituting for onsite activities such as e-working, e-learning and e-shopping. Next, individuals might have switched modes from public (shared) transport to private (individual) modes (car, (e)bike or moped) due to (expected) infection risks, restrictions on travel for some modes (trains, busses, trams, and aircraft), increased car availability at the household level (the car of the household member who is working from home is now available to other household members), or the lower congestion levels during the pandemic. Third, individuals might have experienced behavioural adaptations being combining destination and mode choices. For example, people might have substituted a holiday in a remote location for which they would have to fly for a domestic (or at least more local) holiday by car. Finally, people might have cancelled certain events, such as a conference for which one would have had to fly not offered online. The activity-travel behaviour impacts of COVID-19 can be understood from the determinants presented in Table 1 because restrictions and risks of travel and the benefits and drawbacks of online activities relate to these determinants. For example, the disutility of travel likely increases due to a fear of infection, and this fear also influences intentions, motivations and attitudes. Additionally, social norms and practices might cause people to travel less. Due to better ICT tools (access to and options offered) and more experience with these tools, individuals' use might become more feasible and not only for the young and technically savvy. A reduced need to travel probably increases one's time-space flexibility.

3. Reflections on theories and concepts

This section more closely examined the selected theories and concepts to better understand the possible lasting impacts of COVID-19 on travel and activity behaviour. Fig. 1 provides an overview of the theories and concepts introduced below and conceptualizes the mutual links between these theories and concepts and long-term changes in activity and travel behaviour.

Fig. 1

Long-term activity and travel behaviour model.

All direct impacts of activity and travel behaviour according to theories and concepts of these long-term changes are explained below. In addition, the theories are related. Fewer constraints (TG) and changes in perceived behavioural control (TPB) likely change the balance of benefits and drawbacks towards more online working (UT). Reduced constraints (TGs) likely increase perceived behavioural control (TPB), and changing social norms might influence social practices, at least because of changes in (perceived) competencies. Finally, changes in long-term activity and travel behaviour might influence all theoretical blocks, as have activity and travel behaviour during the COVID-19 pandemic. For example, if more people continue to work remotely (or perform other activities online), this should decrease crowding (public transport) and congestion and change the balance of benefits and drawbacks of online versus on site activities. Long-term changes in activity and travel behaviour influence the constraints affecting people and might lead to changing habits and attitudes and social practices.

3.1. Utility theory

Utility theory assumes that people are well informed and trade-off the positive utility of activities and the negative utility of travel (time, monetary costs, effort, perceived risks, etc.). It is a very influential and frequently used theory for modelling choices in general and for modelling travel behaviour in particular ( Ortúzar and Willumsen, 2011 ).

COVID-19 might have made people more aware of online tools supporting several forms of communication (bilateral or group), with examples including Skype (for Business), Zoom, Teams, and Webex. Such tools are often provided by employers but are also provided by others inviting people to participate in online meetings. Because of the (frequent) use of online tools, many people might not only have become aware of such tools but might also have become more experienced in using them. They might now be more aware of the benefits and drawbacks of online activities. Teleworking is also more broadly accepted by employers, and both employers and employees recognise their benefits. Next, people might become more aware of the benefits and drawbacks of modes of travel. For example, people who switching from local public transport to cycling might experience the flexibility, low cost, health and well-being benefits of cycling, reducing the overall disutility of cycling relative to the pre-COVID-19 era. If this is the case, some long-term impacts of COVID-19 might be expected because such benefits and drawbacks of travel options and online activities will not disappear once the pandemic is over.

3.2. Time geography

From the field of geography, the contribution of time geography as initiated by Hägerstrand (1970) can be helpful for understanding the long-term impacts of COVID-19 on activity-travel behaviour. Time geography emphasizes that people select activity and travel options within the boundaries of three types of constraints: capability constraints (biological, mental and instrumental limitations), coupling constraints (because people need to come together at specific times and often also in certain places), and authority constraints (authority-induced constraints to access, for example, because of restrictions on store open hours).

The most important COVID-19-induced changes influencing activities and travel are probably changes in activities from onsite to online. From online experiences, perhaps after initial struggles to work with online communication tools, people might have increased their capacities to participate in activities online, reducing capability constraints. This process also might have reduced travel-related coupling constraints because travel time is absent, allowing for more flexibility in activity and travel patterns. Finally, some authority constraints, such as those induced by restrictions on opening hours for shops, do not apply if people shop online. It can be hypothesized that people adapt their behaviour based on their positive experiences with some of the reduced constraints, such as those reduced by ICT. Because most reductions in constraints will not disappear after the pandemic, lasting effects of COVID-19 on activity and travel patterns can be expected.

3.3. Theory of planned behaviour, attitude changes, habitual behaviour

Next, we move to theories and concepts from psychology. The theory of planned behaviour (TPB) ( Ajzen, 1991 ) postulates that behaviour depends on intentions and perceived behavioural control (PBC). Next, PBC, attitudes and social norms influence intentions.

COVID-19 can have an impact on social norms. For example, employers could expect people to work at home as much as possible without forbidding them from working onsite. Alternatively, people might experience criticism from others in their social network(s) from having participated in activities that could result in infection risks. Such social norms next might change individuals' intentions and subsequent behaviour. Additionally, PBC might be influenced by experiences with COVID-19. For example, people might realize that they use online tools more effectively than they once thought. Changes in PBC and social norms can influence behavioural intentions and future behaviour. PBC, as explained, can also directly change behaviour. In this case, changes in the (perceived) skills required to use online tools can directly lead to behavioural changes. Some of the factors leading to behavioural changes, such as social norms influencing infection behaviour influencing risks, will disappear after the pandemic. However, other factors will likely remain after the pandemic, or at least changes in perceived behavioural control in the case of online activities and the use of related tools.

An important concept in psychology in general, and of TPB more specifically, is the concept of attitudes. TPB assumes attitudes to be constant, or – in some modified versions – to only change based on interactions between attitudes, social norms and PBC. Increasingly, the academic literature has recognized that attitudes do not have to be constant. For example, based on a model from Eagly and Chaiken (1993) , van Wee et al. (2019) present a model for attitude changes explaining that attitudes can change because of new information (‘knowing’, the cognitive dimension), new experiences (‘doing’, the behavioural dimension) and emotions (‘feeling’, the affective dimension). Triggers can directly influence each dimension, and all three dimensions interact in a complex manner. COVID-19 and related societal changes can be seen as triggers affecting all three dimensions. For example, new information about online tools has likely updated people's cognitive dimension. By using such tools, individuals' experiences may lead to changes in the behavioural dimension. People might experience positive or negative emotions through their adoption of online activities (often communications) over onsite activities. For instance, individuals might view online grocery ordering negatively because they miss having personal interactions at shops or supermarkets, or they might have realized that one does not need to see, smell and feel fresh produce to be able to buy what one wants. Another COVID-19-related trigger is social pressure, as explained above. People, for example, might experience negative emotions if their behaviour is considered by others to increase risks of spreading the virus. If long-term attitude changes occur due to COVID-19-related triggers, a lasting effect on travel behaviour can be expected, as explained by the theory of planned behaviour. Note that some attitude changes might be temporal, especially those related to social norms of behaviour in place during the pandemic or related to infection risks. For example, De Haas et al. (2020) found that in the Netherlands, attitudes towards travelling by public transport have become more negative during the pandemic, and those surrounding car use have become more positive, but it is not clear if such changes in attitudes will last if infection risks disappear after the pandemic.

Psychologists have emphasized that people do not always select all options available and compare all alternatives and finally make the decision that achieves the highest expected level of utility, as already explained by Simon (1955) , who introduced the concept of bounded rationality. Bounded rationality leads to people not evaluating all benefits and drawbacks of all choice options, as does the concept of habitual behaviour. In some cases, behaviour is habitual, and this also applies to travel behaviour, especially in the case of repetitive trips ( Verplanken et al., 1997 ). For example, people do not each day consider their mode choices and departure times for their frequent commute trips. From the perspective of economics, this makes sense because it takes considerable effort to compare alternatives every day, and the probability of another choice being made over the typical choice is quite low. Therefore, the additional (transaction) costs do not outweigh the possible gains.

At the time of writing, March 2021, in most parts of the world, the COVID-19 era has lasted approximately three quarters of a year. Although to the best of our knowledge, the literature does not provide clear answers to the question of how long behaviour changes should last to develop new habits, this likely is long enough to change travel habits. If so, new habitual behaviour might have emerged, and it seems plausible that COVID-19 might have some lasting impacts on travel and activity patterns, even if the virus disappears. On the other hand, some people may also tire of activity and travel practices applied during the pandemic, and some might prefer to return to their behaviour before the pandemic, and thus a change in habits after the pandemic relative to those before the pandemic does not have to apply to all.

3.4. Social practice theory

Social practice theory ( Shove et al., 2012 ; Spotswood, 2016 ) emerged from a response occurring in the field of sociology to understand social action. The unit of inquiry is focused on social practices and how social practices change over time. Thus, we move away from individuals and individual behaviour and from higher structural macroeconomic phenomena (economic systems) and focus on the social practices of the groups in which people participate. A social practice (such as driving, cycling to work, or preparing food) is a routinized, automatic action that involves a number of interconnected elements that include materials (objects, infrastructures, and technology), forms of competence and know how (skills), and meanings (required or optional, functional or fun, and individual or group activities). Changes in these elements, as a result of COVID-19, might lead to changing practices that have a much broader and long-lasting impact than individuals changing their behaviour ( Breadsell et al., 2019 ). If the materials (the hardware needed for a social practice) change, such as from access to (better) technologies that allow people to work remotely or improved food delivery apps, this will likely affect individual behaviour, turning it into a social practice with long(er) lasting impacts. The same applies for know-how (referring to the skills required to perform actions that fit with the social practice) and the associated meaning.

3.5. Constant travel time budgets

From our discussion of theories and concepts originating from economics, psychology, geography and sociology, we continue by discussing travel time budgets. A fundamental concept of travel behaviour is the concept of the constant travel time budget (Marchetti's constant). According to this concept, a large group of people, such as all of the inhabitants of a country, at the aggregate level spends, on average, a quite constant share of its total time travelling, 60–75 min per person per day (e.g., Mokhtarian and Chen, 2004 ). Note that this concept explicitly applies at the aggregate level, not at the individual level. The concept has proven to be quite robust over time. Even though modern transport systems result in higher travel speed, people on average do not travel less time than they did before the introduction of these systems but travel more kilometres within the same amount of time.

According to this concept, it is quite unlikely that due to COVID-19, people over the long term will on average spend less time travelling. If, for example, COVID-19 has a lasting effect on the level of teleworking and people with office jobs commute only two or three days per week and telework on other days, they might accept a job further from home or a house further from their work. Alternatively, individuals might visit family or friends at night after a full day of working from home. It is important to realize that at the individual level, changes in travel times can occur, and the concept only applies at the aggregate level. For society and policy, aggregate impacts are more important, such as for decisions on infrastructure extensions.

Because of the robustness of the concept (to the best of our knowledge, all research on travel time budget appoints in the same direction), we hypothesize that any scenario in which people on average spend substantially less time travelling should be approached with scepticism. ‘Optimistic’ expectations implying that because of COVID-19-related experiences, people will spend substantially less time travelling are probably overly optimistic.

Several theories underpin the concept of constant travel time budgets ( Peters et al., 2001 ), and utility theory (see above) is one of them. According to this theory, when travel times decrease, accessibility increases, and people could be willing to travel to a more remote activity location with higher added value. For example, individuals could accept a better job or shop at a less expensive supermarket.

4. Conclusion and discussion

2 , 3 explain why some lasting effects due to COVID-19 can be expected, with explanations including disruptions to habitual behaviour, changes in attitudes, a new equilibrium between the costs and benefits of behavioural options, changes in social practices, and their causal mechanisms. However, the concept of constant travel time budgets implies that we should be careful in expecting significant lasting effects in terms of a reduction in average travel time expenditure at the aggregate level. Direct effects leading to people spending less time travelling will probably to a large extent be compensated by indirect effects increasing travel times.

However, behind the aggregate constancy, many personal dynamics might occur, so for the discussion of impacts of COVID-19 on travel and activity behaviour, it is very important to distinguish between the microscale level of people and households, the mesoscale level of the group, and the macroscale level of averages for (parts of) societies.

The determination of which theory/concept or combination of theories/concepts can best be used to understand the lasting effects of COVID-19 on travel and activity behaviour depends on the research question at stake. Changes in attitudes and subsequent behavioural impacts can best be understood from psychology, trade-offs between online and onsite settings can better be understood from utility theory, patterns of new trend adoption relate to social practice theory, and the increased time-space flexibility resulting from online activities replacing onsite activities can best be understood from time geography.

From a spatial perspective, the discussion of the long-term impacts of COVID-19 is relevant for at least a number of reasons. First, this discussion emphasizes the need to rethink accessibility because online and onsite accessibility will probably become increasingly interwoven as a result of substitution from onsite to online settings. Second, if this substitution takes place, several location choices will face fewer constraints. This probably applies to residential choices that can be selected with more flexibility relative to work locations, while work locations can probably be chosen more flexibly relative to residential locations. Additionally, the choice of other destination types might become more flexible if people visit these locations less frequently, with the choice of a general practitioner offering online consults or consults at a university serving as an example. Third, if online activity participation becomes more important, in substituting travel to activity locations, the quality of the living environment might become more important.

Regarding the policy relevance of the likely long-term effects of COVID-19 on travel and activity behaviour, lower congestion levels on roads can be expected as well as less crowding in public transport. The precise impacts are still uncertain, and it is too early to say how significant changes will be. The most likely changes should be due to a shift from onsite to online settings and from regular to more discretionary trips. Due to indirect effects, the time saved for travel might be compensated by accepting longer commute distances or additional travel for other purposes. A person who works remotely all day at home might prefer to visit family or friends or go to a bar at night. In the case of commuting and education, at least a reduction in travel frequencies can be expected, and probably also a reduction in rush hour traffic, leading to less congestion on roads and less crowding in public transport. Even if people still travel to work, they will probably be more flexible with respect to when they travel. Individuals might, for example, first work online for one or a few hours and then travel to work after the morning rush hour traffic subsided.

Due to the lower pressure on peak hours, investments in capacity extensions of existing links (such as parts of the motorway network or railway stations and lines) to reduce congestion (roads) and crowding (public transport) might become less attractive, assuming that attractiveness is based on the difference between societal benefits and costs. In addition, policies that encourage cycling might become even more attractive because of the lesser associated infection risks (which might include a short- to medium-term advantage) and due to lasting effects on people who started cycling due the pandemic and have thus become more positive towards cycling (attitude changes).

In addition to these infrastructural implications, spatial planning will probably allow for more flexibility with respect to choosing locations for new residential areas because fewer constraints with respect to commuting will apply. Preferences for dwelling types could change if people continue to work more frequently from home, resulting in the creation of more work spaces and perhaps more outdoor space. Second, because the quality of living environments will probably become more important, as argued above, planning activities leading to more attractive environments will be more beneficial.

Many research challenges related to the impact of the pandemic on activity and travel behaviour remain. Of course, the long-term impacts of COVID-19, as hypothesized in this paper, cannot be validated because at the time of writing (March 2021), the pandemic is still occurring. Validation will be possible over the longer run, perhaps at least one year after the pandemic is over. A very general research challenge therefore is to explore the lasting impacts of COVID-19 on travel and activity behaviour in general and to validate our model ( Fig. 1 ). Such research could explicitly study different groups of the population (workers, students, the elderly, and others), different geographic contexts (OECD countries, rapidly developing countries such as the BRIC countries, and the rest of the word), differences in health safety policies applied during the pandemic (more or less restrictions) and time horizons: Which changes occur along which time scales? We think that the importance of mobility cultures could result in important differences between and even within countries, with cycling culture being an important example. In cities or countries without a cycling culture, the switch to cycling will probably be minimal.

Let us end with some specific research challenges. First, the impact of COVID-19 on activity and travel behaviour provides a unique opportunity to study if, for whom and why attitudes change. Second, we recommend research into the long-term impacts of COVID-19 on social practices. How did/will these impacts change and for whom and why? Third, COVID-19-related changes in travel and activities offer a good opportunity to study changes in habitual behaviour and social practice. How strong are these changes when did they materialize and for whom and under which conditions? In addition, the impact of COVID-19 on activity and travel patterns and future options for activity patterns as addressed by time geography constitute an interesting research avenue. Perhaps people who substitute activities requiring time-consuming trips (work and education) face increased options of activity patterns not available before COVID-19. Research could also test the concept of constant travel time budgets. Will this concept still hold after COVID-19? It probably will, but validation is recommended. Finally, we realize that the theories we discuss here are not the only theories that could be useful for studying behaviour after the pandemic. We recommend exploring the usefulness of additional theories. For example, prospective theory ( Kahneman and Tversky, 1979 ), and more specifically loss aversion and reference points, could be helpful for understanding why people might prefer to not ‘lose’ the advantages of certain behaviours performed during the pandemic, such as reductions in commuting time and flexible activity scheduling. Behaviour during the pandemic could become a reference point.

In addition to these research challenges, we see several other opportunities for related research. Without the ambition to provide a comprehensive list, we discuss a few topics. A first topic concerns well-being and health. Health, well-being and quality of life are related not only to the era of restrictions but also to lasting impacts on travel and activities. To what extent do health, well-being and quality of life change and for whom, when and why? The first indications of COVID-19-induced health and well-being effects can be found in, for example, De Vos (2020) . Second, equity is an important topic to be studied. Some changes fuelled by COVID-19, such as increased options to work from home, might affect groups of the population differently. Some activities might undergo democratization (such as e-shopping), while others will remain for the happy few. Most likely, higher educated and income individuals with (office) jobs benefit more from the increased online options than others, such as those with blue collar jobs who cannot perform their work remotely. On the other hand, if congestion and crowding levels are lowered, those who still need to travel to work will benefit most from lower congestion and crowding levels. A third focus of future research could be the role of ICT in influencing travel and activity patterns. For example, will better tools for communication be developed? Will people increasingly see online activities as a substitute for onsite activities, and if so, for whom, for which activities, and under which conditions will this apply? Could ICT increasingly become a ‘pain killer’ in the case of future restrictions due to pandemics? Fourth, we recommend research into policy responses to long-term changes in activity and travel behaviour. Do, for example, local municipalities reallocate public space over modalities? Perhaps such municipalities will provide more space for pedestrians and cyclists at the cost of private vehicles. Fifth, it is slightly speculative to assume that these changes in activity-travel behaviour will have impacts on technical and service innovations, such as autonomous cars, micro-mobility, (e)bikes and mobility as a service (MaaS). We hypothesize that commuting levels of large parts of the population, especially those with office work, and business travel will decrease due to COVID-19. If so, the benefits of autonomous vehicles might decrease, and those of more local modes of travel such as (e)biking, micro mobility, walking, and urban MaaS travel might increase if people substitute commute and business travel time for local travel for other purposes. These changes could also make cars less attractive in general (strengthening the peak car effect). Sixth, in what sense can COVID-19 be considered a potential game with respect to customers' purchasing of goods? Will pandemic experiences of store closures and perceived infection risks of online shopping, followed by substitution from on site to online shopping, have long-term impacts on shopping behaviour? Finally, what are the implications for home deliveries and related logistics?

Acknowledgements

We thank two anonymous reviewers for their useful suggestions.

ZonMw, the Netherlands Organization for Health Research and Development, funded the project leading to this paper.

  • Ajzen I. The theory of planned behavior. Organ. Behav. Human Decision Process. 1991; 50 :179–211. [ Google Scholar ]
  • Axhausen K., Gärling T. Activity-based approaches to travel analysis: conceptual frameworks, models and research problems. Transp. Rev. 1992; 12 :324–341. [ Google Scholar ]
  • Beck M.J., Hensher D.A. Insights into the impact of COVID-19 on household travel and activities in Australia – the early days of easing restrictions. Transp. Policy. 2020; 99 :95–119. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Breadsell J.K., Eon C., Morrison G.M. Understanding resource consumption in the home, community and society through behaviour and social practice theories. Sustainability. 2019; 11 :6513. doi: 10.3390/su11226513. [ CrossRef ] [ Google Scholar ]
  • Chng S., Abraham C., White M.P., Hoffmann C. Psychological theories of car use: an integrative review and conceptual framework. J. Environ. Psychol. 2018; 55 :23–33. [ Google Scholar ]
  • Circella G. Paper presented at the 3 Revolutions Policy Conference (March 3, 2021). UC Davis Institute of Transport Studies. 2021. Investigating the temporary vs. longer-term impacts of the COVID-19 pandemic on mobility. https://lnkd.in/eQJaqSq accessed March 18, 2021. [ Google Scholar ]
  • De Haas M., Faber R., Hamersma M. How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: evidence from longitudinal data in the Netherlands. Transport. Res. Interdisc. Perspect. 2020; 6 :100150. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • De Vos J. The effect of COVID-19 and subsequent social distancing on travel behavior. Transport. Res. Interdisc. Perspect. 2020; 5 :100121. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Dijst M., Rietveld P., Steg L. In: The Transport System and Transport Policy. An Introduction. van Wee B., Annema J.A., Banister D., editors. Edward Elgar; Cheltenham: 2013. Individual needs, opportunities and travel behaviour: a multidisciplinary perspective based on psychology, economics and geography. [ Google Scholar ]
  • Eagly A., Chaiken S. Harcourt, Brace and Jovanovich; Fort Worth, TX: 1993. The Psychology of Attitude. [ Google Scholar ]
  • Hägerstrand T. What about people in regional science? Paper for the Regional Science Association. 1970; 24 :7–21. [ Google Scholar ]
  • Hook H., De Vos J., Van Acker V., Witlox F. Does undirected travel compensate for reduced directed travel during lockdown? Transportation Letters. 2021 doi: 10.1080/19427867.2021.1892935. [ CrossRef ] [ Google Scholar ]
  • Kahneman D., Tversky A. Prospect theory: an analysis of decision under risk. Econometrica. 1979; 47 (2):263–292. [ Google Scholar ]
  • Kitamura R. An evaluation of activity-based travel analysis. Transportation. 1988; 15 :9–34. [ Google Scholar ]
  • Lamb T.L., Winter S.R., Rice S., Ruskin K.J., Vaughn A. Factors that predict passengers willingness to fly during and after the COVID-19 pandemic. J. Air Transp. Manag. 2020; 89 :101897. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Lattarulo P., Masucci V., Pazienza M.G. Resistance to change: car use and routines. Transp. Policy. 2019; 74 :63–72. [ Google Scholar ]
  • Mokhtarian P.L., Chen C. TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets. Transp. Res. A Policy Pract. 2004; 38 (9):643–667. [ Google Scholar ]
  • Ortúzar J. de D., Willumsen L.G. 4th edition. John Wiley & Sons; Chichester (UK): 2011. Modelling Transport. [ Google Scholar ]
  • Parady G., Taniguchi A., Takami K. Travel behavior changes during the COVID-19 pandemic in Japan: analyzing the effects of risk perception and social influence on going-out self-restriction. Transportation Research Interdisciplinary Perspectives. 2020; 7 :100181. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Peters P., de Wilde R., Clement B., Peeters P. Maastricht/Ede, Universiteit Maastricht, Faculteit der Cultuurwetenschappen/Peeters Advies; 2001. A Constant on the Move? Travel Time, Virtual Mobility and Constant Travel Time Budgets (Een Constante in Beweging? Reistijd, Virtuele Mobiliteit en de BREVER-Wet) [ Google Scholar ]
  • Shove E., Pantzar M., Watson M. Sage Publications; London: 2012. The Dynamics of Social Practice. Everyday Life and how it Changes. [ Google Scholar ]
  • Simon H.A. A behavioral model of rational choice. Q. J. Econ. 1955; 69 (1):99–118. [ Google Scholar ]
  • Spotswood F. Policy Press; Bristol: 2016. Beyond Behaviour Change. Key Issues, Interdisciplinary Approaches and Future Directions. [ Google Scholar ]
  • Van Acker V., van Wee B., Witlox F. When transport geography meets social psychology: toward a conceptual model of travel behaviour. Transp. Rev. 2010; 30 (2):219–240. [ Google Scholar ]
  • van Cranenburgh S., Chorus C., van Wee B. Substantial changes and their impact on mobility: a typology and an overview of the literature. Transp. Rev. 2012; 32 (5):569–597. [ Google Scholar ]
  • van der Drift S., Wismans L., Olde Kalter M.-J. Changing mobility patterns in the Netherlands during COVID-19 outbreak. J. Location Based Services. 2021; 16 in pres) [ Google Scholar ]
  • Van Wee B., De Vos J., Maat K. Impacts of the built environment and travel behaviour on attitudes: theories underpinning the reverse causality hypothesis. J. Transp. Geogr. 2019; 80 :102540. [ Google Scholar ]
  • Varian H. Norton; New York: 1992. Microeconomic Analysis. [ Google Scholar ]
  • Verplanken B., Aarts H., Van Knippenberg A. Habit, information acquisition, and the process of making travel mode choices. Eur. J. Soc. Psychol. 1997; 27 (5):539–560. [ Google Scholar ]
  • Vlek C., Jager W., Steg L. Modellen en strategieën voor gedragsverandering ter vermindering van collectieve risico’s. Nederlands Tijdschrift voor de Psychologie. 1997; 52 :174–191. [ Google Scholar ]

StarsInsider

StarsInsider

Discover what a "pick-me girl" is and why the concept is so toxic

Posted: 29 January 2024 | Last updated: 29 January 2024

<p>Conversation about the "pick-me girl" has exploded online in recent years. Our culture has fueled it from both ends, by encouraging pick-me behavior and by empowering everyone who can type in the hashtag #pickme on TikTok to criticize the behavior when they see it—which is perhaps why it has nearly six billion views and counting on the social media platform.</p> <p>Upon learning the definition of a pick-me girl, and almost certainly upon witnessing one, it's easy to make a swift and unforgiving verdict that collapses the <a href="https://www.starsinsider.com/celebrity/251352/thats-what-she-said-when-famous-women-speak-out-about-feminism" rel="noopener">complexities of feminism</a>. But our knee-jerk negative response to the so-called phenomenon exposes an even darker underbelly of gender inequality, one which reflects our own criticism back at us.</p> <p>To learn more about what a pick-me girl is, how the concept traps women in a cycle of male approval, and how we can ever escape the conditions within which the whole discourse exists, click on the following gallery.</p><p>You may also like:<a href="https://www.starsinsider.com/n/130164?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=565406v2en-sg"> Bizarre facts about the death penalty </a></p>

Conversation about the "pick-me girl" has exploded online in recent years. Our culture has fueled it from both ends, by encouraging pick-me behavior and by empowering everyone who can type in the hashtag #pickme on TikTok to criticize the behavior when they see it—which is perhaps why it has nearly six billion views and counting on the social media platform.

Upon learning the definition of a pick-me girl, and almost certainly upon witnessing one, it's easy to make a swift and unforgiving verdict that collapses the complexities of feminism . But our knee-jerk negative response to the so-called phenomenon exposes an even darker underbelly of gender inequality, one which reflects our own criticism back at us.

To learn more about what a pick-me girl is, how the concept traps women in a cycle of male approval, and how we can ever escape the conditions within which the whole discourse exists, click on the following gallery.

You may also like: Bizarre facts about the death penalty

<p>The concept is broadly defined as a heterosexual woman who goes out of her way to impress guys and make herself seem like she's "not like other girls."</p>

What is a pick-me girl?

The concept is broadly defined as a heterosexual woman who goes out of her way to impress guys and make herself seem like she's "not like other girls."

Follow us and access great exclusive content every day

<p>"'Pick me' behavior is used to draw people in and then keep them there," Jessica Alderson, relationship expert and So Syncd co-founder, told Refinery29. "They want you to think that they're unique and that you wouldn't be able to find anyone else who is nearly as special as them."</p>

The psychology

"'Pick me' behavior is used to draw people in and then keep them there," Jessica Alderson, relationship expert and So Syncd co-founder, told Refinery29. "They want you to think that they're unique and that you wouldn't be able to find anyone else who is nearly as special as them."

You may also like: The real impact of alcohol in your body

<p>Pick-me behavior can involve dressing more sexily in the presence of men, acting more flirtatious and playful, bragging about wealth and status, and talking badly about others—all purportedly in the pursuit of male attention.</p>

What it involves

Pick-me behavior can involve dressing more sexily in the presence of men, acting more flirtatious and playful, bragging about wealth and status, and talking badly about others—all purportedly in the pursuit of male attention.

<p>A surefire way to spot a pick-me girl is someone who denounces stereotypically feminine behaviors, like wearing makeup, to make herself seem like she's "not like other girls."</p>

Denouncing feminine behaviors

A surefire way to spot a pick-me girl is someone who denounces stereotypically feminine behaviors, like wearing makeup, to make herself seem like she's "not like other girls."

You may also like: Nick Cannon and other celebs who were fired from hit TV shows

<p>Women proclaiming that they "hate girl drama" and they only hang out with guys is also typical pick-me behavior.</p>

Bragging about only having male friends

Women proclaiming that they "hate girl drama" and they only hang out with guys is also typical pick-me behavior.

<p>Pick-me behavior also includes agreeing with or expressing anti-feminist thoughts to align themselves (either in reality or in their minds) with men. It's the tip of the iceberg of subconsciously internalized misogyny, which underlies most of this discourse.</p>

Anti-feminist sentiments

Pick-me behavior also includes agreeing with or expressing anti-feminist thoughts to align themselves (either in reality or in their minds) with men. It's the tip of the iceberg of subconsciously internalized misogyny, which underlies most of this discourse.

You may also like: The unbreakable bond of Princes William and Harry

<p>Another common trait is insisting that they're lower maintenance than most girls and trying to act very laid-back, as if those two traits naturally oppose women's nature.</p>

Insisting they're low maintenance

Another common trait is insisting that they're lower maintenance than most girls and trying to act very laid-back, as if those two traits naturally oppose women's nature.

<p>And at the same time, pick-me behavior also involves adopting some stereotypical behaviors and activities of cisgender, heterosexual men, such as liking sports or drinking beer—as if those were naturally reserved for men.</p>

Adopting masculine behaviors

And at the same time, pick-me behavior also involves adopting some stereotypical behaviors and activities of cisgender, heterosexual men, such as liking sports or drinking beer—as if those were naturally reserved for men.

You may also like: Starstruck! Celebs who fangirl over other celebs

<p>According to relationship coach and author Catherine Wilde, these pick-me traits are a form of courtship "in which an individual attempts to increase their chances of being chosen as a mate by engaging in behaviors that make them more attractive to the opposite sex," as she told Refinery29. It's just like how many male animals tear each other down to show a female that they're the better mate.</p>

It's quite primal

According to relationship coach and author Catherine Wilde, these pick-me traits are a form of courtship "in which an individual attempts to increase their chances of being chosen as a mate by engaging in behaviors that make them more attractive to the opposite sex," as she told Refinery29. It's just like how many male animals tear each other down to show a female that they're the better mate.

<p>One peek online will show you just how loathed pick-me girls are, and the main reason they're so heavily criticized is that their behavior uses men's approval as the basis of a woman's value.</p>

Why it has such a bad reputation

One peek online will show you just how loathed pick-me girls are, and the main reason they're so heavily criticized is that their behavior uses men's approval as the basis of a woman's value.

You may also like: Caught on camera: Celebs pulling funny faces

<p>Additionally, any woman who doesn't want to be like other women inherently generalizes "other women" negatively, and through a patriarchal lens of competition.</p>

Pitting women against women

Additionally, any woman who doesn't want to be like other women inherently generalizes "other women" negatively, and through a patriarchal lens of competition.

<p>The framing of the pick-me girl creates and perpetuates the illusion that in order to make oneself more appealing, women must make other women less appealing. The reality is (or should be) that seeing someone tear others down is actually less attractive.</p>

The illusion of competition

The framing of the pick-me girl creates and perpetuates the illusion that in order to make oneself more appealing, women must make other women less appealing. The reality is (or should be) that seeing someone tear others down is actually less attractive.

You may also like: The biggest scandals in automotive history

<p>The entire concept turns upon men's judgment of women and places men's attention and approval as the highest a woman can hope to win.</p>

Men remain at the center of the discussion

The entire concept turns upon men's judgment of women and places men's attention and approval as the highest a woman can hope to win.

<p>But the intensely negative reaction people have towards pick-me girls highlights another problematic part of society: how little tolerance there is for any woman who is transparently "trying"—maintaining this idea that women must be cool and appealing but only effortlessly and without any apparent agenda.</p>

On the other hand

But the intensely negative reaction people have towards pick-me girls highlights another problematic part of society: how little tolerance there is for any woman who is transparently "trying"—maintaining this idea that women must be cool and appealing but only effortlessly and without any apparent agenda.

You may also like: These are history's most important sea battles

<p>Wilde told Refinery29, "While there is nothing inherently wrong with wanting to be noticed or wanting to feel loved, the term often has a negative connotation because it implies that the person is willing to do anything to get what they want." It's interesting that we can think of countless stories within Hollywood alone celebrating men who are willing to do anything for what they want, but when it comes to women it is quickly shamed.</p>

The problem of trying

Wilde told Refinery29, "While there is nothing inherently wrong with wanting to be noticed or wanting to feel loved, the term often has a negative connotation because it implies that the person is willing to do anything to get what they want." It's interesting that we can think of countless stories within Hollywood alone celebrating men who are willing to do anything for what they want, but when it comes to women it is quickly shamed.

<p>Celebrities have an interesting involvement in this trend because they must all technically be pick-me people to become famous in the first place, clamoring over fellow talented artists to earn the spotlight, and yet only the women are lambasted for it. Many stars, including Addison Rae, Jennifer Lawrence, and Kendall Jenner, have been accused of pick-me behavior, and a few have even addressed it.</p>

Celebrity cases

Celebrities have an interesting involvement in this trend because they must all technically be pick-me people to become famous in the first place, clamoring over fellow talented artists to earn the spotlight, and yet only the women are lambasted for it. Many stars, including Addison Rae, Jennifer Lawrence, and Kendall Jenner, have been accused of pick-me behavior, and a few have even addressed it.

You may also like: Celebrities who retired early

<p>After Kendall Jenner made comments about not liking makeup, a clip went viral from 'Keeping Up with the Kardashians' where she says she has a naturally athletic body, and people accused her of being a pick-me girl. Then she posted a TikTok in February 2022 of herself wiping out on a snowboard with her own audio overtop saying "I'm literally built as an athlete. Every blood test I've ever done has said that I am, like, over the normal limit of athleticness." While some celebrated her in the comments, others said it was just further evidence.</p>

Kendall Jenner

After Kendall Jenner made comments about not liking makeup, a clip went viral from 'Keeping Up with the Kardashians' where she says she has a naturally athletic body, and people accused her of being a pick-me girl. Then she posted a TikTok in February 2022 of herself wiping out on a snowboard with her own audio overtop saying "I'm literally built as an athlete. Every blood test I've ever done has said that I am, like, over the normal limit of athleticness." While some celebrated her in the comments, others said it was just further evidence.

<p>The model has admitted on multiple occasions that she used to be a pick-me girl, but on the 'Going Mental with Eileen Kelly' podcast in March 2023 she defined it as abandoning her "own priorities in order to be loved, or to be chosen." This extended to her career, she said, because she was "appealing to a lot of powerful men, essentially." She added, "I just totally abandoned my own boundaries and my own ideas of what is important."</p>

Emily Ratajkowski

The model has admitted on multiple occasions that she used to be a pick-me girl, but on the 'Going Mental with Eileen Kelly' podcast in March 2023 she defined it as abandoning her "own priorities in order to be loved, or to be chosen." This extended to her career, she said, because she was "appealing to a lot of powerful men, essentially." She added, "I just totally abandoned my own boundaries and my own ideas of what is important."

You may also like: Celebs give their thoughts on the keto diet

<p>Either way you look at it—from being a pick-me to criticizing the pick-me—the discourse keeps us trapped in a patriarchal nightmare in which women remain in a cycle of chasing male approval while competing with one another.</p>

Trapped in a cycle

Either way you look at it—from being a pick-me to criticizing the pick-me—the discourse keeps us trapped in a patriarchal nightmare in which women remain in a cycle of chasing male approval while competing with one another.

<p>While pick-me girls are shamed for being misogynistic, shaming pick-me girls is also being called out as misogynistic. Women are, after all, allowed to have dissenting opinions and to reject things like makeup and embrace things like sports without it being solely in the name of male attention or validation.</p>

While pick-me girls are shamed for being misogynistic, shaming pick-me girls is also being called out as misogynistic. Women are, after all, allowed to have dissenting opinions and to reject things like makeup and embrace things like sports without it being solely in the name of male attention or validation.

You may also like: Actors who played multiple characters in the same movie

<p>In a world that is well known for making people feel insecure about themselves, it makes sense that pick-me behavior is so common. It indicates that the person isn't confident enough to choose their authentic selves and would rather strategically adopt a certain persona to attract attention.</p>

But it's a growing concern in an increasingly insecure world

In a world that is well known for making people feel insecure about themselves, it makes sense that pick-me behavior is so common. It indicates that the person isn't confident enough to choose their authentic selves and would rather strategically adopt a certain persona to attract attention.

<p>Pick-me behavior also sets up the manipulative conditions for a toxic relationship. No relationship should begin by faking a personality, nor by staking your reputation on the denigration of others.</p>

A red flag for relationships

Pick-me behavior also sets up the manipulative conditions for a toxic relationship. No relationship should begin by faking a personality, nor by staking your reputation on the denigration of others.

You may also like: Famous figures whose family members survived the Holocaust

<p>It's not just reserved for single people, either. People who grow insecure in their relationships—for example, if their partner starts liking photos of other women—can begin to take on certain behaviors to make themselves stand out against those women.</p>

People in relationships aren't immune

It's not just reserved for single people, either. People who grow insecure in their relationships—for example, if their partner starts liking photos of other women—can begin to take on certain behaviors to make themselves stand out against those women.

<p>It's impossible to be vulnerable with a partner if you are pretending to be someone you're not, and vulnerability is essential for a real, lasting relationship. Plus, a pick-me personality is not sustainable and you'll be left feeling exhausted, emotionally drained, and resentful.</p>

It will doom relationships

It's impossible to be vulnerable with a partner if you are pretending to be someone you're not, and vulnerability is essential for a real, lasting relationship. Plus, a pick-me personality is not sustainable and you'll be left feeling exhausted, emotionally drained, and resentful.

You may also like: The longest and shortest wars in history

<p>This so-called pick-me phenomenon appears to have partly risen out of the overwhelming sense of competition in the modern dating world. There are so many people available at the tips of our fingers on our phones that it makes sense some people would go to great lengths to stand out.</p>

A condition of the modern dating world

This so-called pick-me phenomenon appears to have partly risen out of the overwhelming sense of competition in the modern dating world. There are so many people available at the tips of our fingers on our phones that it makes sense some people would go to great lengths to stand out.

<p>At the root of the pick-me problem is something we can all relate to: wanting to be chosen and feel loved. It's not something to be ashamed of, and yet we treat it like it is.</p>

A common human condition at its root

At the root of the pick-me problem is something we can all relate to: wanting to be chosen and feel loved. It's not something to be ashamed of, and yet we treat it like it is.

You may also like: Curious facts about ancient Greece

<p>Perhaps in direct opposition to the pick-me girl, the "girl's girl" has arisen as a highly commendable badge of honor. A girl's girl is someone who strives to be ethical and refrain from pettiness in their female relationships, valuing them perhaps even more than their male relationships.</p>

The rise of the "girl's girl"

Perhaps in direct opposition to the pick-me girl, the "girl's girl" has arisen as a highly commendable badge of honor. A girl's girl is someone who strives to be ethical and refrain from pettiness in their female relationships, valuing them perhaps even more than their male relationships.

<p>It involves having close female friends, speaking highly of women even in the presence of men, telling a woman she looks great instead of sneering at her, not gossiping, embracing a wide spectrum of whatever "femininity" means, dressing for yourself, and generally lifting women up without fear that they are your competition.</p>

Girl's girl behavior

It involves having close female friends, speaking highly of women even in the presence of men, telling a woman she looks great instead of sneering at her, not gossiping, embracing a wide spectrum of whatever "femininity" means, dressing for yourself, and generally lifting women up without fear that they are your competition.

You may also like: Fascinating facts about ancient Rome that will surprise you

<p>No matter where you stand on the discourse, the overarching problem is that after years of being policed by the patriarchy on what is and isn't appropriate for women to do, women have consequently begun policing themselves. The only real solution to this discourse is to end it entirely.</p>

The problem of policing

No matter where you stand on the discourse, the overarching problem is that after years of being policed by the patriarchy on what is and isn't appropriate for women to do, women have consequently begun policing themselves. The only real solution to this discourse is to end it entirely.

<p>Rejecting labels of what is traditionally "feminine" or "masculine" is a good place to start. To escape the male gaze and the internalized misogyny that traps them there, women need only become attuned and stay true to their own wants, needs, and desires no matter what others might think, while also affording others that very same opportunity.<br><br>Sources: (Refinery29) (Bustle) (Cosmopolitan)</p> <p>See also: <a href="https://www.starsinsider.com/lifestyle/541574/cultures-that-recognize-more-than-two-genders">Cultures that recognize more than two genders</a></p>

How to move forward

Rejecting labels of what is traditionally "feminine" or "masculine" is a good place to start. To escape the male gaze and the internalized misogyny that traps them there, women need only become attuned and stay true to their own wants, needs, and desires no matter what others might think, while also affording others that very same opportunity. Sources: (Refinery29) (Bustle) (Cosmopolitan)

See more: Women prefer to keep these habits a secret...

<p>x</p><p><a href="https://www.msn.com/en-sg/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content every day</a></p>

More for You

Former Trump White House official Peter Navarro arrives at U.S. Federal Courthouse in Washington, Thursday, Jan. 25, 2024.

Trump aide's sentence for defying Congress highlights 'two-tier' justice system: observer

Kamila Valieva, pictured here at the 2022 Winter Olympics in Beijing. (AP Photo/Natacha Pisarenko)

Olympics: Russian skater Kamila Valieva disqualified, served with four-year ban

Boebert Ex-Husband Charged

Lauren Boebert suffers embarrassing finish in poll of new Colorado district

Russian TV host gloats over America's border divide and predicts 'civil war 2.0'

US 'is heading towards civil war 2.0', TV host warns gleefully

Kim Jong Un Tours North Korean Field

Kim Jong Un Warns North Korea Reaching Poverty Crisis

FILE PHOTO: Toyota Corolla is seen in Los Angeles

Toyota warns 50,000 US vehicle owners to stop driving, get immediate repairs

Georgia state Senate launches Fani Willis probe with subpoena power

Trump prosecutor Fani Willis' White House meetings warrant 'very deep investigation,' ex-prosecutor says

Evangelical Christian voters say supporting Trump is all about abortion. It's more complicated than that.

Evangelical Christian voters say supporting Trump is all about abortion. It's more complicated than that.

Fact Check: Biden Wrongly Called Trump the 'Sitting President' During a January 2024 Speech in South Carolina

Fact Check: Biden Wrongly Called Trump the 'Sitting President' During a January 2024 Speech in South Carolina

The Python banner logo on a computer screen running a code editor.

Windows PCs are now being hit by dangerous malware — here's the steps you need to take to stay safe

Kim Jong Un State Media

North Korea Shares Ominous Kim Jong Un Photos

15 Trivia Tidbits About The Michelin Man, Tolkien, And The Price Is Right

14 Trivia Tidbits That Go Great With Coffee

Broader Implications for Small Businesses

Beloved Eatery Suspends Midday Meals Due to California's Minimum Wage Boost

Restrictions by Judge

Reagan-Appointed Judge Rebukes GOP Leaders for Distorting January 6 Riot, Warns of Future Threats

Former President Donald Trump leaves a press conference at 40 Wall Street on January 17, 2024 in New York City. Trump held a press conference after leaving the second day of his defamation trial involving E. Jean Carroll.

Trump attempts to take credit for stock market record highs under Biden

Smash'd Potato Bowls are priced at $3.49 at most locations

KFC launches new Smash'd Potato Bowls for $3.49: 'Unique twist'

Mass demonstrations sweep across Germany

Mass demonstrations sweep across Germany

Chiefs Veteran To Miss Super Bowl After Tearing ACL in AFC Championship Game

Chiefs Veteran To Miss Super Bowl After Tearing ACL in AFC Championship Game

DC mom says $10,000 tax refund came after claiming certain credit on her return

DC mom says $10,000 tax refund came after claiming certain credit on her return

25 Fun Facts To Tuck Into Those Lil Brain Folds You Got There

22 Fun Facts To Tuck Into Those Lil Brain Folds You Got There

IMAGES

  1. A conceptual model of travel behaviour

    concept of travel behavior

  2. The theory of travel decision-making: A conceptual framework of activ…

    concept of travel behavior

  3. Tourist's behavioral patterns in three stages of decision to travel

    concept of travel behavior

  4. A conceptual model of travel behaviour

    concept of travel behavior

  5. Travel behaviour frequently referred determinants and their potential

    concept of travel behavior

  6. Conceptual model of travel behavior during the transition from

    concept of travel behavior

COMMENTS

  1. Concepts of Travel Behaviour Research

    Concepts of Travel Behaviour Research To read this content please select one of the options below: Add to cart (excl. tax) 30 days to view and download Access and purchase options Concepts of Travel Behaviour Research Kay W. Axhausen Threats from Car Traffic to the Quality of Urban Life ISBN : 978--08-044853-4 , eISBN : 978--08-048144-9

  2. (PDF) Concepts of Travel Behavior Research

    max 100 words) please include The paper proposes a conceptual framework for travel behaviour research through a definition of the scope of the research object, essentially hu man activity...

  3. Travel Behavior

    Abstract Travel behavior is important in many fields, such as urban management and disaster management. Since the breakout of COVID-19, many people have changed their preference in travel, which is called travel behavior pattern, to respond to the impact of COVID-19.

  4. Towards a Comprehensive Conceptual Framework of Active Travel Behavior

    Among the frameworks addressing travel behavior [ most described active travel in generic terms, such as walking [ ], cycling [ ], or even physical activity [ ], but several frameworks were more nuanced by investigating different modes of travel, journey purposes (including walking or cycling for recreation or transport), and various quantitativ...

  5. Concepts of Travel Behavior Research

    In the first part this chapter proposes a conceptual framework for travel behavior research through a definition of the scope of the research topic, essentially human activity schedules, and a conceptualization of the traveler as a network actor negotiating infrastructure and human networks and dealing with the social content of the activities undertaken. In the second part of the chapter an ...

  6. Activity-Travel Behaviour Research: Conceptual Issues, State of the Art

    The 'human activity approach' to the study of travel behaviour represents a synthesis of concepts and analytic approaches partially drawn from several subdisciplines concerned with human spatial behaviour.

  7. Travel behaviour change research: A scientometric review and content

    Introduction Extensive environmental, economic, health and social benefits can be gained from developing more sustainable transport systems ( United Nations, 2016). The International Energy Agency advocates a triple policy approach of "avoid, shift and improve" for encouraging sustainable transport (International Energy Agency, 2020).

  8. Key research themes on travel behavior, lifestyle, and sustainable

    The concept of lifestyle adds a behavioral component to travel models that used to be dominated by engineering and econometric traditions. This article presents an overview of how lifestyle is defined and measured in transport studies, and how travel behavior is influenced by lifestyles.

  9. Concepts of Travel Behavior Research

    Concepts of Travel Behavior Research. In the first part this chapter proposes a conceptual framework for travel behavior research through a definition of the scope of the research topic, essentially human activity schedules, and a conceptualization of the traveler as a network actor negotiating infrastructure and human networks and dealing with the social content of the activities undertaken.

  10. (PDF) Key Research Themes on Travel Behaviour, Lifestyle and

    Phil Goodwin Frank Witlox Ghent University Abstract The concept of lifestyle adds a behavioural component to travel models that used to be dominated by engineering and econometric traditions....

  11. PDF Chapter 11 Concepts of travel behavior research

    Urry's scheme for the categorisation of "mobilities" (Urry, 2000, cited in Larsen et al., 2006) is (1) physical travel of people for work, leisure, family life, and migration; (2) physical movement of objects; (3) imaginative travel elsewhere through images and memories; (4) virtual travel on the Internet, telephones, emails, etc.

  12. A Theoretical Framework to Explain the Impact of Destination

    Self-concept has been widely regarded as a useful concept in understanding and explaining consumers' behavior in regard to their choice. ... Su C. (2000). Destination image, self-congruity, and travel behaviour: Toward an integrative model. Journal of Travel Research, 38(4), 340-352. Crossref. Google Scholar. Sop S. A., Kozak N. (2019 ...

  13. Travel Behavior and Travel Demand

    Travel behavior is like any other activity, possibly excluding religion and politics, in that rational economic forces largely drive it. People base their decisions on the benefits they will enjoy from it, either directly or at the end of the trip, constrained by the generalized costs of the movement in terms of time and money, relative to available resources and the opportunity costs of using ...

  14. Chapter 2 Travel Behavior Theories

    When travel behavior is understood as a social phenomenon, researcher observe and understand the trips as a whole. The two perspectives derived two schools of theory, mode choice and human mobility.

  15. Towards a Comprehensive Conceptual Framework of Active Travel Behavior

    Purpose of Review This paper reviews the use of conceptual frameworks in research on active travel, such as walking and cycling. Generic framework features and a wide range of contents are identified and synthesized into a comprehensive framework of active travel behavior, as part of the Physical Activity through Sustainable Transport Approaches project (PASTA). PASTA is a European ...

  16. Travel Behavior

    Travel behavior is a broad topic that touches many aspects of travel demand modeling and forecasting. Kostas Goulias has stated that travel behavior modeling: ...refers primarily to the modeling and analysis of travel demand on the basis of theories and analytical methods from a variety of scientific fields.

  17. Shaping sustainable travel behaviour: Attitude, skills, and access all

    Under a Creative Commons license open access • The motility framework is employed to understand travel behaviour from a subjective perspective. • Sustainable travel behaviour predictors are compared across three heterogeneous urban contexts. • Perceived functional suitability of a sustainable travel mode varies across different urban contexts. •

  18. Tourist Self-concept, Self-congruity, and Travel Behavior based on

    psychological underpinnings of travel behavior. The findings will provide insights into the tourists' perceptions of themselves and their consequent travel behavior. Key Words: self-congruity, destination image, self-concept, travel behavior, cultural event.

  19. Travel attitudes or behaviours: Which one changes when they conflict

    Most of this research focuses on the congruent relationship between attitudes and behaviour, showing that positive mode-specific attitudes predict the use of that mode (Kroesen and Chorus 2018) or conversely that travel behaviours shape attitudes (Tardiff 1977; Dobson et al. 1978; Kroesen et al. 2017 ).

  20. COVID-19 and its long-term effects on activity participation and travel

    A fundamental concept of travel behaviour is the concept of the constant travel time budget (Marchetti's constant). According to this concept, a large group of people, such as all of the inhabitants of a country, at the aggregate level spends, on average, a quite constant share of its total time travelling, 60-75 min per person per day (e.g ...

  21. Defining, Measuring, and Using the Lifestyle Concept in Modal Choice

    Although there is not a formally stated, agreed-upon definition of "lifestyle," interest in this concept has been growing in travel behavior research. Some studies analyze what might be called lifestyles, but in fact various objective socioeconomic characteristics are combined and referred to as stage of life or household composition.

  22. Travel attitudes, the built environment and travel behavior

    Travel attitudes, the built environment and travel behavior relationships: Causal insights from social psychology theories Mashrur Rahman , Gian-Claudia Sciara Add to Mendeley https://doi.org/10.1016/j.tranpol.2022.04.012 Get rights and content • Multiple causal pathways between the built environment (BE) and travel behavior (TB) are explained. •

  23. Discover what a "pick-me girl" is and why the concept is so toxic

    But our knee-jerk negative response to the so-called phenomenon exposes an even darker underbelly of gender inequality, one which reflects our own criticism back at us. To learn more about what a ...

  24. Urban form, travel behavior, and travel satisfaction

    The other aspect of travel behavior examined in the study is commute mode choice. Commute mode choice is also found to substantially mediate the relationship between urban form and travel satisfaction. Walking is found to be promoted by compact urban forms and is a major travel mode in such areas, used by more than 30% of compact urban residents.