Unravelling travellers’ route choice behaviour at full-scale urban network by focusing on representative OD pairs in computer experiments

Autoři: Humberto González Ramírez aff001;  Ludovic Leclercq aff001;  Nicolas Chiabaut aff001;  Cécile Becarie aff001;  Jean Krug aff001
Působiště autorů: Univ. Lyon, Univ. Gustave Eiffel, IFSTTAR, ENTPE, LICIT, Lyon, France aff001
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225069


In a city-scale network, trips are made in thousands of origin-destination (OD) pairs connected by multiple routes, resulting in a large number of alternatives with diverse characteristics that influence the route choice behaviour of the travellers. As a consequence, to accurately predict user choices at full network scale, a route choice model should be scalable to suit all possible configurations that may be encountered. In this article, a new methodology to obtain such a model is proposed. The main idea is to use clustering analysis to obtain a small set of representative OD pairs and routes that can be investigated in detail through computer route choice experiments to collect observations on travellers behaviour. The results are then scaled-up to all other OD pairs in the network. It was found that 9 OD pair configurations are sufficient to represent the network of Lyon, France, composed of 96,096 OD pairs and 559,423 routes. The observations, collected over these nine representative OD pair configurations, were used to estimate three mixed logit models. The predictive accuracy of the three models was tested against the predictive accuracy of the same models (with the same specification), but estimated over randomly selected OD pair configurations. The obtained results show that the models estimated with the representative OD pairs are superior in predictive accuracy, thus suggesting the scaling-up to the entire network of the choices of the participants over the representative OD pair configurations, and validating the methodology in this study.

Klíčová slova:

Behavior – Computer networks – Decision making – Network analysis – Roads – Statistical distributions – Transportation


1. Manski C, McFadden D. Structural Analysis of Discrete Data with Econometric Applications. The MIT Press; 1981.

2. Train KE. Discrete Choice Methods with Simulation. Cambridge University Press; 2003.

3. Walker J, Ben-Akiva M. Generalized random utility model. Mathematical Social Sciences. 2002;43(3):303–343. doi: 10.1016/S0165-4896(02)00023-9

4. Wardrop JG. Road paper. Some theoretical aspects of road traffic research. Proceedings of the Institution of Civil Engineers. 1952;1(3):325–362. doi: 10.1680/ipeds.1952.11259

5. Yildirimoglu M, Kahraman O. Searching for empirical evidence on traffic equilibrium. PLoS ONE. 2018;13(5):1–16. doi: 10.1371/journal.pone.0196997

6. Iida Y, Akiyama T, Uchida T. Experimental analysis of dynamic route choice behavior. Transp Res B. 1992;26(1):17–32. doi: 10.1016/0191-2615(92)90017-Q

7. Bogers EaI. Joint modeling of ATIS, habit and learning impacts on route choice by laboratory simulator experiments. Delft University of Technology; 2005.

8. Selten R, Chmura T, Pitz T, Kube S, Schreckenberg M. Commuters route choice behaviour. Games and Economic Behavior. 2007;58(2):394–406. doi: 10.1016/j.geb.2006.03.012

9. Adler JL, McNally MG. In-laboratory experiments to investigate driver behavior under advanced traveler information systems. Transportation Research Part C. 1994;2(3):149–164. doi: 10.1016/0968-090X(94)90006-X

10. Lotan T. Effects of familiarity on route choice behavior in the presence of information. Transportation Research Part C: Emerging Technologies. 1997;5(3-4):225–243. doi: 10.1016/S0968-090X(96)00028-9

11. Mahmassani HS, Liu YH. Dynamics of commuting decision behaviour under Advanced Traveller Information Systems. Transportation Research Part C: Emerging Technologies. 1999;7(2-3):91–107. doi: 10.1016/S0968-090X(99)00014-5

12. Ben-Elia E, Shiftan Y. Which road do I take? A learning-based model of route-choice behavior with real-time information. Transportation Research Part A: Policy and Practice. 2010;44(4):249–264.

13. Ben-Elia E, Avineri E. Response to Travel Information: A Behavioural Review. Transport Reviews. 2015;35(3):352–377. doi: 10.1080/01441647.2015.1015471

14. Abdel-Aty MA, Kitamura R, Jovanis PP. Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice. Transportation Research Part C: Emerging Technologies. 1997;5(1):39–50. doi: 10.1016/S0968-090X(96)00023-X

15. Srinivasan K, Mahmassani H. Modeling Inertia and Compliance Mechanisms in Route Choice Behavior Under Real-Time Information. Transportation Research Record: Journal of the Transportation Research Board. 2000;1725(January):45–53. doi: 10.3141/1725-07

16. De Moraes Ramos G, Daamen W, Hoogendoorn S. Modelling travellers’ heterogeneous route choice behaviour as prospect maximizers. Journal of Choice Modelling. 2013;6:17–33. doi: 10.1016/j.jocm.2013.04.002

17. Avineri E, Prashker JN. Sensitivity to travel time variability: Travelers learning perspective. Transportation Research Part C: Emerging Technologies. 2005;13(2):157–183. doi: 10.1016/j.trc.2005.04.006

18. de Palma A, Picard N. Route choice decision under travel time uncertainty. Transportation Research Part A: Policy and Practice. 2005;39(4 SPEC. ISSS.):295–324.

19. Sheffi Y. Urban transportation networks. Prentice-Hall, Inc.; 1985.

20. Mahmassani HS, Chang GL. On Boundedly Rational User Equilibrium in Transportation Systems. Transportation Science. 1987;21(2):89–99. doi: 10.1287/trsc.21.2.89

21. Bovy PHL, Stern E. Route Choice: Wayfinding in Transport Networks. Kluwer Academic Publishers; 1990.

22. Ramming MS. Network knowledge and route choice. Massachusetts Institute of Technology; 2002.

23. Bekhor S, Ben-Akiva ME, Ramming MS. Evaluation of choice set generation algorithms for route choice models. Annals of Operations Research. 2006;144(1):235–247. doi: 10.1007/s10479-006-0009-8

24. Papinski D, Scott DM, Doherty ST. Exploring the route choice decision-making process: A comparison of planned and observed routes obtained using person-based GPS. Transportation Research Part F: Traffic Psychology and Behaviour. 2009;12(4):347–358. doi: 10.1016/j.trf.2009.04.001

25. Zhu S, Levinson D. Do people use the shortest path? An empirical test of wardrop’s first principle. PLoS ONE. 2015;10(8):1–18. doi: 10.1371/journal.pone.0134322

26. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. vol. 1 of Springer Series in Statistics. 2nd ed. New York, NY: Springer New York; 2009. Available from: http://www.springerlink.com/index/10.1007/b94608.

27. McFadden D, Train K. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics. 2000;15(5):447–470. doi: 10.1002/1099-1255(200009/10)15:5%3C447::AID-JAE570%3E3.0.CO;2-1

28. Bhat CR, Castelar S. A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area. Transportation Research Part B: Methodological. 2002;36(7):593–616. doi: 10.1016/S0191-2615(01)00020-0

29. Brownstone D, Bunch DS, Train K. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Research Part B: Methodological. 2000;34(5):315–338. doi: 10.1016/S0191-2615(99)00031-4

30. Institut national de la statistique et des études économiques. Découpage infracommunal: Table d’appartenance géographique des IRIS; 2018. Available from: https://www.insee.fr/fr/information/2017499.

31. Leclercq L. Hybrid approaches to the solutions of the “Lighthill-Whitham-Richards” model. Transportation Research Part B: Methodological. 2007;41(7):701–709. doi: 10.1016/j.trb.2006.11.004

32. Ben-Akiva M, Morikawa T. Estimation of switching models from revealed preferences and stated intentions. Transportation Research Part A: General. 1990;24(6):485–495. doi: 10.1016/0191-2607(90)90037-7

33. Bradley M, Daly A. Estimation of logit choice models using mixed stated preference and revealed preference information. Paper presented to the 6th International Conference on Travel Behavior, Quebec. 1991.

34. Earnhart D. Combining Revealed and Stated Data to Examine Housing Decisions Using Discrete Choice Analysis. Journal of Urban Economics. 2002;51(1):143–169. doi: 10.1006/juec.2001.2241

35. Adamowicz W, Boxall P, Williams M, Louviere J. Stated Preference Approaches for Measuring Passive Use Values: Choice Experiments and Contingent Valuation. American Journal of Agricultural Economics. 1998;80(1):64–75. doi: 10.2307/3180269

36. Adamowicz W, Louviere J, Williams M. Combining Revealed and Stated Preference Methods for Valuing Environmental Amenities. Journal of Environmental Economics and Management. 1994;26(3):271–292. doi: 10.1006/jeem.1994.1017

37. Hensher DA, Bradley M. Using stated response choice data to enrich revealed preference discrete choice models. Marketing Letters. 1993;4(2):139–151. doi: 10.1007/BF00994072

38. Regier DA, Ryan M, Phimister E, Marra CA. Bayesian and classical estimation of mixed logit: An application to genetic testing. Journal of Health Economics. 2009;28(3):598–610. doi: 10.1016/j.jhealeco.2008.11.003 19345433

39. Balcombe K, Chalak A, Fraser I. Model selection for the mixed logit with Bayesian estimation. Journal of Environmental Economics and Management. 2009;57(2):226–237. doi: 10.1016/j.jeem.2008.06.001

40. Levin DA, Peres Y. Markov Chains and Mixing Times. 2nd ed. American Mathematical Society; 2017.

41. Plummer M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling JAGS: Just Another Gibbs Sampler. DSC 2003 Working Papers (Draft Versions). 2003; p. 1–8.

42. R Core Team. R: A Language and Environment for Statistical Computing; 2018. Available from: https://www.R-project.org/.

Článek vyšel v časopise


2019 Číslo 11