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01.09.2016

Analyzing time sensitivity to discomfort in the Paris subway: an interval data model approach

verfasst von: Matthieu de Lapparent, Martin Koning

Erschienen in: Transportation | Ausgabe 5/2016

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Abstract

We analyze individual travel discomfort-time tradeoffs in the Paris subway using stated choice experiments. The survey design allows to set up in a willingness-to-pay space to estimate the distributions of elasticities of values of travel time to crowd density and time multipliers. Several formulations of a generalized travel cost function are tested. Accounting for heterogeneity in preferences, the econometric models all take the form of an ordered probit with known bounds. We derive several estimates that could be used for fine-tuning of traffic simulation systems and more general transportation policy analysis.

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Crowding in PT systems can be estimated according to different metrics. Load factors correspond to the ratio between the number of users in the trains and the number of seats proposed. Because this measure does not consider the varying in-vehicle design of the rolling-stock, passenger density is generally preferred. This will be our measure of crowding in this article. Note also that, in Japan, crowding is measured through the “Japan Industrial Standard” that merges together the seat and the standing capacities of the vehicles see Kato (2014).

For instance, Haywood and Koning (2013) use the same data set to value crowding costs in a RUM framework.

d is crowd density, \(\Delta d\) is variation of crowd density.

t is travel time, \(\Delta t\) is variation of travel time.

We here recognize there is an important shortfall in the survey design. Because the showcards only represents the central part of the carriages where the doors are located, we are not able to study the value of seat-crowding, as done by Kroes et al. (2013) and Whelan and Crockett (2009) who present the whole vehicle to users.

Of course, we would have preferred a larger sample. As compared to the few existing studies, it however seems that the sample size is not that small (Li and Hensher 2011).

We used color cards to incite travelers to report their net monthly incomes.

Results are available on request.

This result may also come from different rolling-stocks. Thus line 1 carriages are larger and propose to users less places where to hold on. In crowded situations, this may increase the risks of falls and lead to more unpleasant journeys.

This is why the sample size is different as compared to the other specifications.

Ben-Akiva, M., Lerman, S.: Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge (1985)

Bradley, M., Daly, A.: Use of the logit scaling approach to test for the rank-order and fatigue effects in stated preference data. Transportation 21, 167–184 (1994)CrossRef

Bradley, M., Daly, A.: New analysis issues in stated preference research. In: Ortuzar, J.D.D. (ed.) Stated Preference Modelling Techniques. Planning and Transport Research and Computation, London (2000)

Cameron, T.A., Quiggin, J.: Estimation using contingent valuation data from a “dichotomous choice with follow-up” questionnaire. J. Environ. Econ. Manag. 27(3), 218–234 (1994)CrossRef

Cox, T., Houdmont, J., Griffiths, A.: Rail passenger crowding, stress, health and safety in britain. Transp. Res. Part A 40(3), 244–258 (2006)

CRCFRI.: A literature review of passenger crowding on metropolitan railways: psychological, cultural human and economic factors. Project R2, 104 (2012)

de Palma, A., Kilani, M., Proost, S.: Discomfort in mass transit and its application for scheduling and pricing. Transp. Res. Part B 71, 1–18 (2015)CrossRef

Evans, G., Wener, R.: Crowding and personal space invasion on the train: Please don’t make me sit in the middle. J. Environ. Psychol. 27(1), 90–94 (2007)CrossRef

Flachaire, E., Hollard, G.: Starting-point bias and respondent uncertainty in dichotomous choice valuation surveys. Resour. Energy Econ. 29(3), 183–194 (2007)CrossRef

Haab, T., McConnel, K.: Valuing Environmental and Natural Resources: The Econometrics of Non-Market Valuation. Edward Elgar, Worcester (2003)

Hanemann, W., Loomis, J., Kanninen, B.: Statistical efficiency of double bounded dichotomous choice contingent valuation. Am. J. Agric. Econ. 73, 1255–1263 (1991)CrossRef

Haywood, L., Koning, M.: Estimating crowding costs in public transport. DIW Discussion Paper (1293). (2013)

Hensher, D., Rose, J., Collins, A.: Indentifying commuter preferences for existing modes and a proposed metro in sydney, australia with special reference to crowding. Public Transp. 3(2), 109–147 (2011)CrossRef

Hensher, D., Rose, J., Greene, W.: Applied Choice Analysis: A Primer. Cambridge University Press, Cambridge (2005)CrossRef

Jara-Diaz, S., Gschwender, A.: Towards a general microeconomic model for the operation of public transport. Transp. Rev. 4(23), 453–469 (2003)CrossRef

Kato, H.: Valuation of urban rail service-experiences from Tokyo, Japan. OECD/ITF Discussion Papers 01, 1–30 (2014)

Kato, H., Kaneko, Y., Inoue, M.: Comparative analysis of transit assignment: evidence from urban railway system in the tokyo metropolitan area. Transportation 37(5), 775–799 (2010)CrossRef

Kraus, M.: Discomfort externalities and marginal cost transit fares. J Urban Econ. 29(2), 249–259 (1991)CrossRef

Kroes, E., Kouwenhoven, M., Debrincat, L., Pauget, N.: On the value of crowding in public transport for Île-de-France. OECD/ITF Discussion Papers 18, 1–37 (2013)

Li, Z., Hensher, D.: Crowding and public transport: a review of willingness to pay evidence and its relevance in project appraisal. Transp. Policy 18, 880–887 (2011)CrossRef

Long, J.: Regression Models for Categorical and Limited Dependent Variables, vol. Advanced Quantitative Techniques in the Social Sciences. SAGE Publications, Washington (1997)

Maddala, G.: Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge (1986)

Mohd Mahudin, N., Cox, T., Griffiths, A.: The measurement of rail passenger crowding: scale development and psychometric properties. Transp. Res. Part F 15, 38–51 (2012)CrossRef

Observatoire des déplacements à Paris. Le bilan des déplacements en 2008 à paris. Technical report (2008)

OECD-ITF. Valuing convenience in public transport. Technical report (2014)

Parry, I., Small, K.: Should urban transit subsidies be reduced? Am. Econ. Rev. 99, 700–724 (2009)CrossRef

Tirachini, A., Hensher, D., Jara-Diaz, S.: Comparing operator and users costs of light rail, heavy rail and bus rapid transit over a radial public transport network. Res. Transp. Econ. 29, 231–242 (2010)CrossRef

Tirachini, A., Hensher, D., Rose, J.: Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transp. Res. Part A 53, 36–52 (2013)

Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2009)CrossRef

Tversky, A., Kahneman, D.: Loss aversion in riskless choice: a reference dependent model. Quart. J. Econ. 106, 1039–1061 (1991)CrossRef

Wardman, M., Whelan, G.: Twenty years of rail crowding valuation studies: evidence and lessons from british experience. Transp. Rev. 31(3), 379–398 (2011)CrossRef

Wener, R., Evans, G., Boately, P.: Commuting stress: psychological effects of a trip and spillover into the workplace. Transp. Res. Board 1924(2005), 112–117 (2005)CrossRef

Whelan, G., Crockett, J.: An investigation of the willingness to pay to reduce rail overcrowding. Proceeding of the First International Conference on Choice Modelling, Harrogate (2009)

Wilkinson, L.: The Grammar of Graphics. Springer, New York (1999)CrossRef

Titel: Analyzing time sensitivity to discomfort in the Paris subway: an interval data model approach
verfasst von: Matthieu de Lapparent
Martin Koning
Publikationsdatum: 01.09.2016
Verlag: Springer US
Erschienen in: Transportation / Ausgabe 5/2016
Print ISSN: 0049-4488
Elektronische ISSN: 1572-9435
DOI: https://doi.org/10.1007/s11116-015-9629-7

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