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Choice of mode of transport for long-distance trips: Solving the problem of sparse data

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  • Monzón, Andrés
  • Rodriguez-Dapena, Alvaro

Abstract

Transport planning is usually based on models' forecasts, but the reliability of their outputs depends so much on the quality of input-data they are fed with. Discrete-choice models are used to characterise travellers' behaviour in choosing their transport mode. Their calibration process is usually based on data stemming from household survey campaigns. However, the modelling in multimodal and intermodal transport on an interurban level is far more complicated and costly than in the case of an urban area. An alternative way to reduce costs is achieved by designing a choice-based sampling strategy where household surveys are replaced by specific surveys for each transport mode. This strategy generates a non-random sample that has to be treated correctly during the estimation process. In principle, the sample does not represent population market quotas for each different transport option. Moreover, as a result of both physical and functional constraints, the survey period cannot cover all origin-destination pairs (O-D pairs) in an optimal way and, consequently, the above-mentioned bias also affects each different individual O-D pair or, at least, group of pairs. In order to overcome this problem, this study presents a new procedure derived from the introduction of maximum likelihood estimators. These estimators assume the original mode options in terms of population quotas and in terms of O-D groups of pairs. The procedure is based on the optimisation of an objective-function to correct the above-mentioned bias in a way similar to the estimators of samples based on different choice options. The method named DWELT estimates the parameters corresponding to each explanatory variable using mode shares for each O-D pair or group of pairs. DWELT has been successfully validated in the case study of the Madrid-Barcelona interurban corridor in Spain. This result allows to achieve a more flexible cheaper survey procedure for interurban transport planning activities. Therefore transport policy strategies could be better designed and tested with lower costs.

Suggested Citation

  • Monzón, Andrés & Rodriguez-Dapena, Alvaro, 2006. "Choice of mode of transport for long-distance trips: Solving the problem of sparse data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 587-601, August.
  • Handle: RePEc:eee:transa:v:40:y:2006:i:7:p:587-601
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    References listed on IDEAS

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    1. Imbens, Guido W, 1992. "An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling," Econometrica, Econometric Society, vol. 60(5), pages 1187-1214, September.
    2. Jara-Díaz, Sergio R. & Farah, Marcelo, 1987. "Transport demand and users' benefits with fixed income: The goods/leisure trade off revisited," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 165-170, April.
    3. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
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    1. Xiaomei Lin & Yusak O. Susilo & Chunfu Shao & Chengxi Liu, 2018. "The Implication of Road Toll Discount for Mode Choice: Intercity Travel during the Chinese Spring Festival Holiday," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    2. Kvizda, Martin & Seidenglanz, Daniel, 2014. "Out of Prague: a week-long intermodal shift from air to rail transport after Iceland’s Eyjafjallajökull erupted in 2010," Journal of Transport Geography, Elsevier, vol. 37(C), pages 102-111.
    3. Kengpol, Athakorn & Meethom, Warapoj & Tuominen, Markku, 2012. "The development of a decision support system in multimodal transportation routing within Greater Mekong sub-region countries," International Journal of Production Economics, Elsevier, vol. 140(2), pages 691-701.
    4. Zhao, Hui & Yan, Xuedong & Gao, Ziyou, 2013. "Transportation serviceability analysis for metropolitan commuting corridors based on modal choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 270-284.

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