travel cost analysis

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Travel-cost method

The travel-cost method (TCM) is used for calculating economic values of environmental goods. Unlike the contingent valuation method, TCM can only estimate use value of an environmental good or service. It is mainly applied for determining economic values of sites that are used for recreation, such as national parks. For example, TCM can estimate part of economic benefits of coral reefs, beaches or wetlands stemming from their use for recreational activities (diving and snorkelling/swimming and sunbathing/bird watching). It can also serve for evaluating how an increased entrance fee a nature park would affect the number of visitors and total park revenues from the fee. However, it cannot estimate benefits of providing habitat for endemic species.

TCM is based on the assumption that travel costs represent the price of access to a recreational site. Peoples’ willingness to pay for visiting a site is thus estimated based on the number of trips that they make at different travel costs. This is called a revealed preference technique, because it ‘reveals’ willingness to pay based on consumption behaviour of visitors.

The information is collected by conducting a survey among the visitors of a site being valued. The survey should include questions on the number of visits made to the site over some period (usually during the last 12 months), distance travelled from visitor’s home to the site, mode of travel (car, plane, bus, train, etc.), time spent travelling to the site, respondents’ income, and other socio-economic characteristics (gender, age, degree of education, etc). The researcher uses the information on distance and mode of travel to calculate travel costs. Alternatively, visitors can be asked directly in a survey to state their travel costs, although this information tends to be somewhat less reliable. Time spent travelling is considered as part of the travel costs, because this time has an opportunity cost. It could have been used for doing other activities (e.g. working, spending time with friends or enjoying a hobby). The value of time is determined based on the income of each respondent. Time spent at the site is for the same reason also considered as part of travel costs. For example, if respondents visit three different sites in 10 days and spend only 1 day at the site being valued, then only fraction of their travel costs should be assigned to this site (e.g. 1/10). Depending on the fraction used, the final benefit estimates can differ considerably.

Two approaches of TCM are distinguished – individual and zonal. Individual TCM calculates travel costs separately for each individual and requires a more detailed survey of visitors. In zonal TCM, the area surrounding the site is divided into zones, which can be either concentric circles or administrative districts. In this case, the number of visits from each zone is counted. This information is sometimes available (e.g. from the site management), which makes data collection from the visitors simpler and less expensive.

The relationship between travel costs and number of trips (the higher the travel costs, the fewer trips visitors will take) shows us the demand function for the average visitor to the site, from which one can derive the average visitor’s willingness to pay. This average value is then multiplied by the total relevant population in order to estimate the total economic value of a recreational resource.

TCM is based on the behaviour of people who actually use an environmental good and therefore cannot measure non-use values. This method is thus inappropriate for sites with unique characteristics which have a large non-use economic value component (because many people would be willing to pay for its preservation just to know that it exists, although they do not plan to visit the site in the future).

The travel-cost method might also be combined with contingent valuation to estimate an economic value of a change (either enhancement or deterioration) in environmental quality of the NP by asking the same tourists how many trips they would make in the case of a certain quality change. This information could help in estimating the effects that a particular policy causing an environmental quality change would have on the number of visitors and on the economic use value of the NP.

For further reading:

Ward, F.A., Beal, D. (2000) Valuing nature with travel cost models. A manual. Edward Elgar, Cheltenham.

Ecosystem valuation [ www.ecosystemvaluation.org/travel_costs.htm ]

This glossary entry is based on a contribution by Ivana Logar 

EJOLT glossary editors:   Hali Healy, Sylvia Lorek and Beatriz Rodríguez-Labajos

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Travel cost method

This article deals with the Travel Cost Method, which is often used in evaluating the economic value of recreational sites. This is particularly important in the coastal zone because of the level of use and the potential values that can be attached to the natural coastal and marine environment.

The Travel Cost Method (TCM) is one of the most frequently used approaches to estimating the use values of recreational sites. The TCM was initially suggested by Hotelling [1] and subsequently developed by Clawson [2] in order to estimate the benefits from recreation at natural sites. The method is based on the premise that the recreational benefits at a specific site can be derived from the demand function that relates observed users’ behaviour (i.e., the number of trips to the site) to the cost of a visit. One of the most important issues in the TCM is the choice of the costs to be taken into account. The literature usually suggests considering direct variable costs and the opportunity cost of time spent travelling to and at the site. The classical model derived from the economic theory of consumer behaviour postulates that a consumer’s choice is based on all the sacrifices made to obtain the benefits generated by a good or service. If the price ( [math]p[/math] ) is the only sacrifice made by a consumer, the demand function for a good with no substitutes is [math]x=f(p)[/math] , given income and preferences. However, the consumer often incurs other costs ( [math]c[/math] ) in addition to the out-of-pocket price, such as travel expenses, and loss of time and stress from congestion. In this case, the demand function is expressed as [math]x = f(p, c)[/math] . In other words, the price is an imperfect measure of the full cost incurred by the purchaser. Under these conditions, the utility maximising consumer’s behaviour should be reformulated in order to take such costs into account. Given two goods or services [math]x_1, x_2[/math] , their prices [math]p_1, p_2[/math] , the access costs [math]c_1, c_2[/math] and income [math]R[/math] , the utility maximising choice of the consumer is:

[math]max \, U = u(x_1,x_2) \quad subject \, to \quad (p_1+c_1)x_1+(p_2+c_2)x_2=R . \qquad (1)[/math]

Now, let [math]x_1[/math] denote the aggregate of priced goods and services, [math]x_2[/math] the number of annual visits to a recreational site, and assume for the sake of simplicity that the cost of access to the market goods is negligible ( [math]c_1 \approx 0[/math] ) and that the recreational site is free ( [math]p_2=0[/math] ). Under these assumptions, equation (1) can be written as:

[math]max \, U = u(x_1,x_2) \quad subject \, to \quad p_1x_1+c_2x_2=R . \qquad (2)[/math]

Under these conditions, the utility maximising behaviour of the consumer depends on:

The TCM is based on the assumption that changes in the costs of access to the recreational site [math]c_2[/math] have the same effect as a change in price: the number of visits to a site decreases as the cost per visit increases. Under this assumption, the demand function for visits to the recreational site is [math]x_2=f(c_2)[/math] and can be estimated using the number of annual visits as long as it is possible to observe different costs per visit. The basic TCM model is completed by the weak complementarity assumption, which states that trips are a non-decreasing function of the quality of the site, and that the individual forgoes trips to the recreational site when the quality is the lowest possible [3] , [4] . There are two basic approaches to the TCM: the Zonal approach (ZTCM) and the Individual approach (ITCM). The two approaches share the same theoretical premises, but differ from the operational point of view. The original ZTCM takes into account the visitation rate of users coming from different zones with increasing travel costs. By contrast, ITCM, developed by Brown and Nawas [5] and Gum and Martin [6] , estimates the consumer surplus by analysing the individual visitors’ behaviour and the cost sustained for the recreational activity. These are used to estimate the relationship between the number of individual visits in a given time period, usually a year, the cost per visit and other relevant socio-economic variables. The ITCM approach can be considered a refinement or a generalisation of ZTCM [7] .

[math]x_2 = g(c_2) . \qquad (3)[/math]

The demand function can also be estimated for non-homogeneous sub-samples introducing among the independent variables income and socio-economic variables representing individual characteristics [8] . Therefore, if an individual incurs [math]c_2^e[/math] per visit, he chooses to do [math]x_2^e[/math] visits a year, while if the cost per visit increases to [math]c_2^p[/math] the number of visits will decrease to [math]x_2^p[/math] . The cost [math]cp[/math] is the choke price, that is the cost per visit that results in zero visits. The annual user surplus (the use value of the recreational site) is easily obtained by integrating the demand function from zero to the current number of annual visits, and subtracting the total expenditures on visits.

Related articles

• ↑ Hotelling, H. (1949), Letter, In: An Economic Study of the Monetary Evaluation of Recreation in the National Parks , Washington, DC: National Park Service.
• ↑ Clawson, M. (1959), Method for Measuring the Demand for, and Value of, Outdoor Recreation . Resources for the Future, 10, Washington, DC.
• ↑ Freeman, A.M. III. (1993). The Measurement of Environmental and Resource Values: Theory and Method , Washington, DC: Resources for the Future.
• ↑ Herriges, J.A., C. Kling and D.J. Phaneuf (2004), 'What’s the Use? Welfare Estimates from Revealed Preference Models when Weak Complementarity Does Not Hold', Journal of Environmental Economics and Management , 47 (1), pp. 53-68.
• ↑ Brown, W.G. and F. Nawas (1973), 'Impact of Aggregation on the Estimation of Outdoor Recreation Demand Functions', American Journal of Agricultural Economics , 55, 246-249.
• ↑ Gum, R.L. and W.E.Martin (1974), 'Problems and Solutions in Estimating the Demand for and Value of Rural Outdoor Recreation', American Journal of Agricultural Economics , 56, 558-566.
• ↑ Ward, F.A. and D. Beal (2000), Valuing Nature with Travel Cost Method: A Manual , Northampton: Edward Elgar.
• ↑ Hanley, N. and C.L. Spash (1993), Cost Benefit Analysis and the Environment , Aldershot, UK: Edward Elgar.
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The Travel Cost Model

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The travel cost model is used to value recreational uses of the environment. For example, it may be used to value the recreation loss associated with a beach closure due to an oil spill or to value the recreation gain associated with improved water quality on a river. The model is commonly applied in benefit-cost analyses and in natural resource damage assessments where recreation values play a role. Since the model is based on observed behavior, it is used to estimate use values only.

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Adamowicz, W. L. 1994. Habit Formation and Variety Seeking in a Discrete Choice Model of Recreation Demand. Journal of Agricultural and Resource Economics 19(1):19–31.

Google Scholar  

Adamowicz, W., J. Louviere, and M. Williams. 1994. Combining Revealed and Stated Preference Methods for Valuing Environmental Amenities. Journal of Environmental Economics and Management 26:271–92.

Adamowicz, W. L., J. Swait, P. Boxall, and M. Williams. 1997. Perception Versus Objective Measures of Environmental Quality in Combined Revealed and Stated Preference Models of Environmental Evaluation. Journal of Environmental Economics and Management 32:65–84.

Andrews, T. 1996. A Discrete Choice Model of Recreational Trout Angler Benefits in Pennsylvania. Unpublished manuscript, Department of Economics, West Chester University.

Ben-Akiva, M., and S. Lerman. 1985. Discrete Choice Analysis . Cambridge, MA: MIT Press.

Bockstael, N. E. 1995. Travel Cost Models. Handbook of Environmental Economics . Cambridge, MA: Blackwell Publishers.

Bockstael, N. E., and I. E. Strand. 1987. The Effect of Common Sources of Regression Error on Benefit Estimates. Land Economics 63(1):11–20.

Bockstael, N., W. M. Hanemann, and C. L. Kling. 1987. Estimating the Value of Water Quality Improvements in a Recreational Demand Framework. Water Resources Research 23(5):951–960.

Bockstael, N. E., W. M. Hanemann, and I. E. Strand, Jr. 1984. Measuring the Benefits of Water Quality Improvements Using Recreation Demand Models. Report to the U.S. Environmental Protection Agency. College Park, MD: University of Maryland.

Bockstael, N. E., K. E. McConnell, and I. E. Strand. 1988. Benefits from Improvements in Chesapeake Bay Water Quality. Report to the U.S. Environmental Protection Agency. College Park, MD: University of Maryland.

Bockstael, N. E., K.E. McConnell, and I. E. Strand. 1989. A Random Utility Model for Sportfishing: Some Preliminary Results for Florida. Marine Resource Economics 6:245–260.

Bockstael, N. E., K. E. McConnell, and I. E. Strand, Jr. 1991. Recreation: Measuring the Demand for Environmental Quality. New York: North Holland.

Bockstael, N. E., I. E. Strand, and W. M. Hanemann. 1987. Time and the Recreational Demand Model. American Journal of Agricultural Economics :293–302.

Brown, G. M., R. Congar, and E. A. Wilman. 1983. Recreation: Tourists and Residents. In Assessing the Social Costs of Oil Spills: The Amoco Cadiz Case Study . U.S. National Ocean Service, National Oceanic and Atmospheric Administration Report (NTIS PB83-100536). Washington, DC.

Brown, G., and R. Mendelsohn. 1984. The Hedonic Travel Cost Method. Review of Economics and Statistics 66:427–433.

Burt, O., and D. Brewer. 1971. Estimation of net Social Benefits from Outdoor Recreation. Econometrica 39(5):813–828.

Chen, H. Z., and S. R. Cosslett. 1998. Environmental Quality Preference and Benefit Estimation in Multinomial Probit Models: A Simulation Approach. American Journal of Agricultural Economics 80(3):512–520.

Cicchetti, C. J., A. C. Fisher, and V. K. Smith. 1976. An Econometric Evaluation of a Generalized Consumer Surplus Measure: The Mineral King Controversy. Econometrica 44(6):1259–1276.

Creel, M. D., and J. B. Loomis. 1990. Theoretical and Empirical Advantages of Truncated Count Data Estimators for Analysis of Deer Hunting in California. American Journal of Agricultural Economics 72:434–441.

Dilman, D.A. 2000. Mail and Internet Surveys: The Tailored Design Method (2 nd edition). New York: Wiley.

Feather, P., and W. D. Shaw. 1999. Estimating the Cost of Leisure Time for Recreation Demand Models. Journal of Environmental Economics and Management 38:49–65.

Freeman, A. M. III. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington, DC: Resources for the Future.

Greene, W. H. 1997. Econometric Analysis (3rd Ed.). Upper Saddle River, NJ: Prentice Hall.

Haab, T. C., and K. E. McConnell. 1996. Count Data Models and the Problem of Zeros in Recreation Demand Analysis. American Journal of Agricultural Economics 78:89–102.

Haab, T. C, and K. E. McConnell. 2002. Valuing Environmental and Natural Resources . Cheltenham, UK: Edward Elgar.

Hackett, S. C. 2000. The Recreational Economic Value of the Eastern Trinity Alps Wilderness. School of Business and Economics, Humbolt State University.

Hanemann, W. M. 1999. Welfare Analysis with Discrete Choice Models. In Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Edited by Herriges, J. A., and C. L. Kling. Cheltenham, UK: Edward Elgar.

Hauber, A. H. IV, and G. R. Parsons. 2000. The Effect of nesting Structure on Welfare Estimation in Random Utility Models: An Application to the Demand for Recreational Fishing. American Journal of Agricultural Economics 82:501–514.

Hausman, J. A., G K. Leonard, and D. McFadden. 1995. A Utility-Consistent, Combined Discrete Choice and Count Data Model: Assessing Recreational Use Losses Due to Natural Resource Damage. Journal of Public Economics 56:1–30.

Hellerstein, D. 1999. Can We Count on Count Models? In Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Edited by Herriges, J. A., and C. L. Kling. Cheltenham, UK: Edward Elgar.

Hellerstein, D., and R. Mendelsohn. 1993. A Theoretical Foundation for Count Data Models. American Journal of Agricultural Economics 75:604–11.

Herriges, J. A., and C. L. Kling, eds. 1999. Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Aldershot, UK: Edward Elgar.

Herriges, J. A., C. L. Kling, and D. J. Phaneuf. 1999. Corner Solution Models of Recreation Demand: A Comparison of Competing Frameworks. In Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Edited by Herriges, J. A., and C. L. Kling. Cheltenham, UK: Edward Elgar.

Hicks, R. L. and I. Strand. 2000. The Extent of Information: Its Relevance for Random Utility Models. Land Economics 76(3):374–385.

Hoehn, J. P., T. Tomasi, F. Lupi, and H. Z. Chen. 1996. An Economic Model for Valuing Recreational Angling Resources in Michigan. Michigan State University, Report to the Michigan Department of Environmental Quality.

Karou, Y. S1995. Measuring Marine Recreation Benefits of Water Quality Improvements by the Nested Random Utility Model. Resource and Energy Economics 17:119–136.

Kolstad, C. D. 2000. Environmental Economics . New York: Oxford University Press.

Laitila, T. 1999. Estimation of Combined Site-Choice and Trip-Frequency Models of Recreational Demand Using Choice-Based and On site Samples. Economic Letters 64:17–23.

Layman, R. C, J. R. Boyce, and K. R. Criddle. 1996. Economic Valuation of the Chinook Salmon Sport Fishery of the Gulkana River, Alaska, under Current and Alternate Management Plans. Land Economics 72(1):113–128.

Loomis, J. B., and R. G. Walsh. 1997. Recreation Economic Decisions: Comparing Benefits and Costs . State College, PA: Venture.

Lupi, F., and P. M. Feather. 1998. Using Partial Aggregation to Reduce Bias in Random Utility Travel Cost Models. Water Resources Research 34(12):3595–3603.

McConnell, K. E. 1986. The Damages to Recreational Activities from PCB’s in New Bedford Harbor. University of Maryland, Department of Agricultural and Resource Economics. Report prepared for the Ocean Assessment Division, National Oceanic and Atmospheric Administration.

McConnell, K. E. 1992. On site Time in the Demand for Recreation. American Journal of Agricultural Economics 74:918–925.

McConnell, K E., I. Strand, and L. Blake-Hedges. 1995. Random Utility Models of Recreational Fishing: Catching Fish Using a Poisson Process. Marine Resource Economics 10:247–261.

McConnell, K. E., and I. E. Strand. 1981. Measuring the Cost of Time in Recreation Demand Analysis. American Journal of Agricultural Economics 63:153–156.

McConnell, K. E. and I. E. Strand. 1994. The Economic Value of Mid and South Atlantic Sportfishing. University of Maryland, Report to the U.S. EPA and NOAA. Department of Agricultural and Resource Economics, University of Maryland.

McFadden, D. 2001. Economic Choices. American Economic Review 91(3):351–378.

Mendelsohn, R., J. Hof, G. Peterson, and R. Johnson. 1992. Measuring Recreation Values with Multiple Destination Trips. American Journal of Agricultural Economics 74:926–933.

Montgomery, M. and M. Needelman. 1997. The Welfare Effects of Toxic Contamination in Freshwater Fish. Land Economics 73:211–223.

Morey, E. 1981. The Demand for Site-Specific Recreational Activities: A Characteristics Approach. Journal of Environmental Economics and Management 8:345–371.

Morey, E. 1999. Two Rums Uncloaked: Nested-Logit Models of Site Choice and Nested-Logit Models of Participation and Site Choice. Chapter 3 in Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Edited by Herriges, J. A., and C. L. Kling. Cheltenham, UK: Edward Elgar.

Morey, E., R. D. Rowe, and M. Watson. 1993. A Repeated Nested-Logit Model of Atlantic Salmon Fishing. American Journal of Agricultural Economics 75:578–592.

Murray, C, B. Sohngen, and L. Pendelton. 2001. Valuing Water Quality Advisories and Beach Amenities in the Great Lakes. Water Resources Research 37(10):2583–2590.

Ozuna, T., and I. A. Gomez 1994. Estimating a System of Recreation Demand Function Using a Seemingly Unrelated Poisson Regression Approach. Review of Economics and Statistics 76:356–360.

Parsons, G. R. 1991. A Note on Choice of Residential Location in Travel Cost Demand Models. Land Economics 67(3):360–364.

Parsons, G. R., and M J. Kealy. 1992. Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation. Land Economics 68(1):93–106.

Parsons, G. R., and M. Needelman. 1992. Site Aggregation in a Random Utility Model of Recreation. Land Economics 68(4):418–433.

Parsons, G. R., and A. J. Wilson. 1997. Incidental and Joint Consumption in Recreation Demand. Agricultural and Resource Economics Review 26:1–6.

Parsons, G. R., and A. B. Hauber. 1998. Spatial Boundaries and Choice Set Definition in a Random Utility Model of Recreation Demand. Land Economics 74(1):32–48.

Parsons, G R., P. M. Jakus, and T. Tomasi. 1999. A Comparison of Welfare Estimates from Four Models for Linking Seasonal Recreational Trips to Multinomial Logit Models of Site Choice. Journal of Environmental Economics and Management 38:143–157.

Parsons, G. R., D. M. Massey, and T. Tomasi. 1999. Familiar and Favorite Sites in a Random Utility Model of Beach Recreation. Marine Resource Economics 14:299–315.

Parsons, G. R., and D. M. Massey. 2003. A RUM Model of Beach Recreation. In The New Economics of Outdoor Recreation . Edited by Hanely, N., W. D. Shaw, R. E. Wright, and J. C. Coleman. Cheltenham, UK: Edward Elgar.

Parsons, G. R., A. J. Plantinga, and K. J. Boyle. 2000. Narrow Choice Sets in a Random Utility Model of Recreation Demand. Land Economics 76(1):86–99.

Peters, T., W. L. Adamowicz, and P. C. Boxall. 1995. Influence of Choice Set Considerations in Modeling the Benefits from Improved Water Quality. Water Resources Research 31(7):T781–1787.

Phaneuf, D. J., C. L. Kling, and J. Herriges. 2000. Estimation and Welfare Calculation in a Generalized Corner Solution Model With an Application to Recreation Demand. Review of Economics and Statistics 82:83–92.

Phaneuf, D. J. and V. K. Smith. 2002. Recreation Demand Models. Unpublished manuscript, North Carolina State University.

Randall, A. 1994. A Difficulty with the Travel Cost Method. Land Economics 70(1):88–96.

Shaw, D. 1988. ‘On site Samples’ Regression Problems of Nonnegative Integers, Truncation, and Enodgenous Stratification. Journal of Econometrics 37:211–223.

Shaw, W. D., and P. Jakus. 1996. Travel Cost Models of the Demand for Rock Climbing. Agricultural and Resource Economics Review 25(2):133–142.

Shaw, W. D., and M. Ozog. 1999. Modeling Overnight Recreation Trip Choice: Application of a Repeated Multinomial Logit Model. Environmental and Resource Economics 13(4):397–414.

Shonkwiler, J. S. 1999. Recreation Demand Systems for Multiple Site Count Data Travel Cost Models. Chapter 9 in Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Edited by Herriges, J. A., and C. L. Kling. Cheltenham, UK: Edward Elgar.

Shonkwiler, J. S. and W. D. Shaw. 1996. Hurdle Count-Data Models in recreation Demand Analysis. Journal of Agricultural and Resource Economics 21(2):210–219.

Siderelis, C, G. Brothers, and P. Rea. 1995. A Boating Choice Model for the Valuation of Lake Access. Journal of Leisure Research 27(3):264–282.

Siderelis, C, and R. Moore. 1995. Outdoor Recreation Net Benefits of Rail-Trails. Journal of Leisure Research 27(4):344–359.

Smith, V. K., W. H. Desvousges, and M. P. McGivney. 1983. The Opportunity Cost of Travel Time in Recreation Demand Models. Land Economics 59(3):259–277.

Smith, V. K, and W. H. Desvousges. 1985. The Generalized Travel Cost Model and Water Quality Benefits: A Reconsideration. Southern Economics Journal 52(2):371–381.

Smith, V. K., W. H. Desvousges, and M. P. McGivney. 1983. Estimating Water Quality Benefits: An Econometric Analysis. Southern Economic Journal 50(2):422–437.

Sohngen, B. 2000. The Value of Day Trips to Lake Erie Beaches. Unpublished report, Dept. of Agricultural, Environmental, and Development Economics, Ohio State University.

Train, K. E. 1999. Mixed Logit Models for Recreation Demand. Chapter 4 in Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice . Edited by Herriges, J. A., and C. L. Kling. Cheltenham, UK: Edward Elgar.

Ward, F. A., and D. Beal. 2000. Valuing Nature with Travel Cost Models: A Manuel. Cheltenham, UK: Edward Elgar.

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Parsons, G.R. (2003). The Travel Cost Model. In: Champ, P.A., Boyle, K.J., Brown, T.C. (eds) A Primer on Nonmarket Valuation. The Economics of Non-Market Goods and Resources, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0826-6_9

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Travelmath provides an online cost calculator to help you determine the cost of driving between cities. You can use this data to figure out a budget for a road trip. The driving calculation is based on the average fuel efficiency of your vehicle, and you can change the gas mileage in mpg or L/100 km to match your exact make and model. Gas prices are automatically estimated based on current fluctuations, and again you can adjust these to fit your local gas station prices. Both U.S. and international units are available to make the calculations easier to use, and the output is given for both one-way and round trip travel routes.

Check the driving distance for your planned route, and see if the total driving time requires an overnight stay. If it's a long trip, you may want to research some hotels along the way . Or compare whether it's better to fly or drive to your destination.

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Calculate Travel Cost (Raster Analysis)

Calculates the least accumulative cost distance from or to the least-cost source, while accounting for surface distance along with horizontal and vertical cost factors.

This tool is deprecated and will be removed in a future release.

The Distance Accumulation or Distance Allocation tools provide enhanced functionality or performance.

• Illustration

This raster analysis portal tool is available when you are signed in to an ArcGIS Enterprise portal that has ArcGIS Image Server configured for Raster Analysis . When the tool is run, ArcGIS Pro serves as a client and the processing occurs in the servers federated with ArcGIS Enterprise . The portal tool accepts layers from your portal as input and creates output in your portal.

The input raster layer supports a layer from the portal, a URI or URL to an image service, or the output from the Make Image Server Layer tool. The input feature layer can be a layer from the portal or a URI or URL to a feature service. This tool does not support local raster data or layers. While you can use local feature data and layers as input to this portal tool, best practice is to use layers from your portal as input.

One example application of this tool is to identify the cheapest route to construct a new road to a proposed school.

When the input source data is an image service, the set of source cells consists of all cells in the source raster that have valid values. Cells that have NoData values are not included in the source set. The value 0 is considered a legitimate source.

When the input source data is a feature service, the source locations are converted internally to a raster before performing the analysis. The resolution of the raster can be controlled with the Output cell size parameter or the Cell Size environment. By default, the resolution will be determined by the shorter of the width or height of the extent of input feature, in the input spatial reference, divided by 250.

Cell locations with NoData in the Input cost raster act as barriers. Any cell location that is assigned NoData on the input cost surface will receive NoData on all output rasters

For the Output distance image service, the least-cost distance (or minimum accumulative cost distance) of a cell from or to a set of source locations is the lower bound of the least-cost distances from the cell to all source locations.

The cost raster cannot contain values of zero since the algorithm is a multiplicative process. If your cost raster does contain values of zero, and these values represent areas of lowest cost, change values of zero to a small positive value (such as 0.01) before running Calculate Travel Cost. If areas with a value of zero represent areas that should be excluded from the analysis, these values should be turned to NoData before running Calculate Travel Cost.

The default values for the Horizontal factor modifiers are the following:

The default values for the Vertical factor modifiers are the following:

The characteristics of the source, or the movers from or to a source, can be controlled by specific parameters. The Source cost multiplier parameter determines the mode of travel or magnitude at the source, Source start cost sets the starting cost before the movement begins, Source resistance rate is a dynamic adjustment accounting for the impact of accumulated cost, for example, simulating how much a hiker is getting fatigued, and Source capacity sets how much cost a source can assimilate before reaching its limit. The Travel direction identifies if the mover is starting at a source and moving to non-source locations, or is starting at non-source locations and moving back to a source.

If Source start cost is specified and the Travel direction is Travel from source , the source locations on the output cost distance surface will be set to the value of Source start cost ; otherwise, the source locations on the output cost distance surface will be set to zero.

If any of the source characteristics parameters are specified using a field, the source characteristic will be applied on a source-by-source basis, according to the information in the given field for the source data. When a keyword or a constant value is given, it will be applied to all sources.

Derived Output

Name Explanation Data Type inputSourceRasterOrFeatures The layer that defines the sources to calculate the distance to. The layer can be raster or feature.

Code sample

This example calculates the travel cost from a single source.

This example calculates the travel cost from a set of sources.

• Environments
• Licensing information
• Basic: Requires ArcGIS Image Server
• Standard: Requires ArcGIS Image Server
• Advanced: Requires ArcGIS Image Server

Related topics

• An overview of the Legacy Use Proximity toolset
• An overview of the Legacy Distance toolset
• Find a geoprocessing tool

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