Transportation

, Volume 43, Issue 2, pp 337–355 | Cite as

Influence of the weather on mode choice in corridors with time-varying congestion: a mixed data study

  • Javier Anta
  • José B. Pérez-López
  • Ana Martínez-Pardo
  • Margarita Novales
  • Alfonso Orro
Article

Abstract

Studies that link human behaviour to the influence of weather have historically been conducted in such fields as tourism, marketing and leisure. In most studies that jointly examine weather and the mode of transport, only open-air transportation has been considered (for example, bicycle, motorcycle or walking). This focus, together with the habitual use of data collected with automatic devices and a lack of studies that analyse this issue using stated preference data, are the main reasons motivating this paper. This paper aims to analyse the influence of weather and the density of traffic on the choice of transport mode. A case study is conducted in an access/egress corridor located in the city of Barcelona (Spain). Two data sources were used: revealed preference and stated preference data. Modelling techniques using mixed data enabled the stronger features from both data sources to be captured. Finally, we discuss how the selection of different alternative specific constants in models estimated using mixed data could generate unrealistic forecasting results if environmental changes are expected in the actual market.

Keywords

Weather Congestion RP/SP data Mode choice survey Discrete choice models Forecasting market shares 

Notes

Acknowledgments

This research is affiliated with the Group of Railways and Transportation Engineering (University of A Coruña). It was supported by the Ministry of Development through the research programme SIMETRIA: Simulation models for the evaluation global and regional transports to multimodal scenarios; and by the Ministry of Economy and Competitiveness, in conjunction with European Regional Development Funds (ERDF), through the research scheme NASCI: Technological, economical and attract new users perspective comparison about new advances on metropolitan transport systems with intermediate capacity. Finally, the authors wish to thank the anonymous referees for their comments, which significantly improved this paper.

References

  1. Ben-Akiva, M., Lerman, S.: A discrete choice analysis. The MIT Press, Massachusetts (1985)Google Scholar
  2. Bergstrom, A., Magnusson, R.: Potential of transferring car trips to bicycle during winter. Transp. Res. Part A 37, 649–666 (2003)Google Scholar
  3. Bierlaire, M.: BIOGEME: a free package for the estimation of discrete choice models. In: Proceedings of the 3rd Swiss Transportation Research Conference. Ascona (2003)Google Scholar
  4. Bliemer, M.C.J., Rose, J.M.: Experimental design influences on stated choice outputs: an empirical study in air travel choice. Transp. Res. Part A 45, 63–79 (2011)Google Scholar
  5. Böcker, L., Dijst, M., Prillwitz, J.: Impact of everyday weather on individual daily travel behaviours in perspective: a literature review. Transp. Rev. 33, 71–91 (2013)CrossRefGoogle Scholar
  6. Bradley, M.A., Daly, A.J.: Estimation of logit choice models using mixed stated-preference and revealed preference information. In: Stopher, P.R., Lee-Gosselin, M. (eds.) Understanding Travel Behaviour in an Era of Change, pp. 219–231. Guildford, United Kingdom (1997)Google Scholar
  7. Cherchi, E., Ortuzar, J.D.: On fitting mode specific constants in the presence of new options in RP/SP models. Transp. Res. Part A 40, 1–18 (2006)CrossRefGoogle Scholar
  8. Cools, M., Moons, E., Creemers, L., Wets, G.: Changes in travel behavior in response to weather conditions: do type of weather and trip purpose matter? Transp. Res. Rec. 2157(1), 22–28 (2010)CrossRefGoogle Scholar
  9. De Palma, A., Rochat, D.: Understanding individual travel decisions: results from a commuters survey in Geneva. Transportation 26, 263–281 (1999)CrossRefGoogle Scholar
  10. Domencich, T., McFadden, D.: Urban Travel Demand: A Behavioural Analysis. North-Holland, Amsterdam (1975)Google Scholar
  11. EMQ—Enquesta de Mobilitat Quotidiana 06. Department of territorial policy and public works (DPTOP) and the Autority of metropolitan transport (ATM). http://www.20.gencat.cat/portal/site/territori/menuitem.2a0ef7c1d39370645f13ae92b0c0e1a0/?vgnextoid=ee9f3abec0a38210VgnVCM1000008d0c1e0aRCRD&vgnextchannel=ee9f3abec0a38210VgnVCM1000008d0c1e0aRCRD (2006). Accessed 09 Jan 2011
  12. Espino, R., Roman, C., Ortuzar, J.D.: Analysing demand for suburban trips: a mixed RP/SP model with latent variables and interaction effects. Transportation 33, 241–242 (2006)CrossRefGoogle Scholar
  13. Heinen, E., Wee, B., Maat, K.: Commuting by bicycle: an overview of the literature. Transp. Rev. 30, 59–96 (2010)CrossRefGoogle Scholar
  14. Hensher, D.A.: Stated preference analysis of travel choices—the state of practice. Transportation 21, 107–133 (1994)CrossRefGoogle Scholar
  15. Hensher, D.A.: Accounting for scale heterogeneity within and between pooled data sources. Transp. Res. Part A 46, 480–486 (2012)Google Scholar
  16. Hensher, D.A., Rose, J.M., Greene, W.H.: Applied choice analysis: a primer. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  17. Hensher, D.A., Rose, J.M., Greene, W.H.: Combining RP and SP data: biases in using the nested logit ‘trick’—contrasts with flexible mixed logit incorporating panel and scale effects. J. Transp. Geogr. 16, 126–133 (2008)CrossRefGoogle Scholar
  18. Hess, S., Train, K.E.: Recovery of inter- and intra-personal heterogeneity using mixed logit models. Transp. Res. Part B 45, 973–990 (2011)CrossRefGoogle Scholar
  19. Hidalgo, J., Pigeon, G., Masson, V.: Urban-breeze circulation during the CAPITOUL experiment: observational data analysis approach. Meteorol. Atmos. Phys. 102, 223–241 (2008)CrossRefGoogle Scholar
  20. Khattak, A.J., DePalma, A.: The impact of adverse weather conditions on the propensity to change travel decisions: a survey of Brussels commuters. Transp. Res. Part A 31, 181–203 (1997)Google Scholar
  21. Koetse, M.J., Rietveld, P.: The impact of climate change and weather on transport: an overview of empirical findings. Transp. Res. Part D 14, 205–221 (2009)CrossRefGoogle Scholar
  22. Lin, T.P.: Thermal perception, adaptation and attendance in a public square in hot and humid regions. Build. Environ. 44, 2017–2026 (2009)CrossRefGoogle Scholar
  23. Louviere, J.J., Hensher, D.A.: Using discrete choice models with experimental-design data to forecast consumer demand for a unique cultural event. J. Consum. Res. 10, 348–361 (1983)CrossRefGoogle Scholar
  24. Louviere, J.J., Hensher, D.A., Swait, J.D., Adamowicz, W.L.: Stated choice methods: analysis and applications. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  25. McFadden, D.: Disaggregate behavioural travel demand’s RUM side: a 30-year retrospective. In: Hensher, D.A. (ed.) Travel Behaviour Research: The Leading Edge, pp. 17–63. Elsevier, Amsterdam (2001)Google Scholar
  26. Mills, G.: Micro- and mesoclimatology. Prog. Phys. Geogr. 33, 711–717 (2009)CrossRefGoogle Scholar
  27. Nikolopoulou, M., Lykoudis, S.: Use of outdoor spaces and microclimate in a Mediterranean urban area. Build. Environ. 42, 3691–3707 (2007)CrossRefGoogle Scholar
  28. NRC: Where the Weather Meets the Road: A Research Agenda for Improving Road Weather Services. National Academies Press, Washington DC (2004)Google Scholar
  29. Ortúzar, J.D., Willumsen, G.: Modelling transport, 3rd edn. Wiley, Chichester (2001)Google Scholar
  30. Parkin, J., Wardman, M., Page, M.: Estimation of the determinants of bicycle mode share for the journey to work using census data. Transportation 35, 93–109 (2008)CrossRefGoogle Scholar
  31. RACC. Congestión en los corredores de acceso a Barcelona. Royal Club of the Automobile, Catalonia. http://www.racc.es/pub/ficheros/adjuntos/adjuntos_congestio_esp_versiondiciembre_ok_jzq_810fdcaf.pdf (2007). Accessed 17 March 2011
  32. Sabir, M.: Weather and travel behaviour. Vrije University, Amsterdam (2011)Google Scholar
  33. Swait, J., Louviere, J.J.: The role of the scale parameter in the estimation and comparison of multinomial logit-models. J. Mark. Res. 30, 305–314 (1993)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Javier Anta
    • 1
  • José B. Pérez-López
    • 1
  • Ana Martínez-Pardo
    • 1
  • Margarita Novales
    • 1
  • Alfonso Orro
    • 1
  1. 1.Group of Railways and Transportation EngineeringUniversity of A CoruñaA CoruñaSpain

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