, Volume 45, Issue 5, pp 1181–1206 | Cite as

On the role of bridges as anchor points in route choice modeling

  • Hamzeh Alizadeh
  • Bilal Farooq
  • Catherine Morency
  • Nicolas Saunier


This work builds upon the thought that individuals allocate higher levels of importance to some particular features of the route, so called anchor points. Previous route choice models have either ignored the effects of anchor points (route-based models), or have given an exclusive attention to their effects and ignored the behavioral accuracy and practicality of these models (anchor-based models). In this work we argue that the consideration of both route-level attributes and anchor points would enhance the behavioral aspect of route choice models as well as their estimation and prediction abilities. Global Positioning System traces have been used to investigate the effect of bridges as anchor points for trips between Montreal and its Northern suburb, Laval. A classic Nested Logit and a nested Logit Kernel model have been estimated, in which interdependencies among routes crossing the same bridge are captured through the nested structure and the adopted factor analytic approach, respectively. A Metropolis–Hastings path-sampling algorithm is applied, for the first time, on a large road network with more than 40,000 nodes and 19,000 links to provide the consideration choice set. Estimates are then compared to three alternate models, representing route-based and anchor-based formulations; namely Path-Size Logit, Extended Path-Size Logit, and Independent Availability Logit models. Empirical results showed that the proposed nested structures with MH sampling provide better estimates and also perform better in the validation step with respect to comparative models. Findings underscore the importance of considering anchor points in conjunction with route level attributes in route choice decisions.


Route choice Bridge choice Anchor points Discrete choice models Nested Logit GPS Metropolis–Hastings 



The authors are grateful to Gunnar Flötteröd for his invaluable help with the implementation of MH algorithm. We also acknowledge collaborators from Taxi Diamond who provided access to data for research purpose, as well as the anonymous reviewers for their helpful comments and suggestions. We would also thank the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) for providing the possibility of using their cluster computer for our research funded by the Natural Sciences and Engineering Council of Canada (NSERC) Research Tools and Instruments grant.


  1. Arifin, Z.N.: Route choice modeling based on GPS tracking data. Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 20400, (2012)Google Scholar
  2. Azevedo, J., Costa, M.E.O.S., Madeira, J.J.E.S., Martins, E.Q.V.: An algorithm for the ranking of shortest paths. Eur. J. Oper. Res. 69(1), 97–106 (1993)CrossRefGoogle Scholar
  3. Bekhor, S., Ben-Akiva, M., Scott Ramming, M.: Adaptation of logit kernel to route choice situation. Transp. Res. Rec. J. Transp. Res. Board 1805, 78–85 (2002)CrossRefGoogle Scholar
  4. Ben-Akiva, M.: Structure of Passenger Travel Demand Models. Massachusetts Institute of Technology, Cambridge (1973)Google Scholar
  5. Ben-Akiva, M., Bergman, M., Daly, A.J., Ramaswamy, R.: Modeling inter-urban route choice behaviour. In: Volmuller, J., Hamerslag, R. (eds.) Proceedings of the 9th International Symposium on Transportation and Traffic Theory, pp. 299–330. VNU Press, Utrecht (1984)Google Scholar
  6. Ben-Akiva, M., Bierlaire, M.: Discrete choice methods and their applications to short term travel decisions. In: Hall, R.W. (ed.) Handbook of Transportation Science, pp. 5–33. Springer (1999)Google Scholar
  7. Ben-Akiva, M., Bierlaire, M.: Discrete choice models with applications to departure time and route choice. In: Hall, R.W. (ed.) Handbook of transportation science, p 32 (2003)Google Scholar
  8. Ben-Akiva, M., Bolduc, D., Walker, J.: Specification, Identification and Estimation of the Logit Kernel (or Continuous Mixed Logit) Model. Department of Civil Engineering Manuscript, MIT, Cambridge (2001)Google Scholar
  9. Bierlaire, M.: BIOGEME: a free package for the estimation of discrete choice models. In: Swiss Transport Research Conference, vol. TRANSP-OR-CONF-2006-048 (2003)Google Scholar
  10. Bierlaire, M.: BisonBiogeme 2.4: estimating a first model. Series on Biogeme TRANSP-OR 150720 (2015)Google Scholar
  11. Bierlaire, M., Bolduc, D., McFadden, D.: The estimation of generalized extreme value models from choice-based samples. Transp. Res. Part B Methodol. 42(4), 381–394 (2008). doi: 10.1016/j.trb.2007.09.003 CrossRefGoogle Scholar
  12. Bierlaire, M., Fetiarison, M.: Estimation of discrete choice models: extending BIOGEME. In: Swiss Transport Research Conference (STRC) (2009)Google Scholar
  13. Bierlaire, M., Frejinger, E.: Route choice models with subpath components. In: Swiss Transportation Research Conference, vol. TRANSP-OR-CONF-2006-032 (2005)Google Scholar
  14. Bierlaire, M., Frejinger, E.: Route choice modeling with network-free data. Transp. Res. Part C Emerg. Technol. 16(2), 187–198 (2008)CrossRefGoogle Scholar
  15. Bolduc, D., Ben-Akiva, M.: A multinomial probit formulation for large choice sets. In: Proceedings of the 6th International Conference on Travel Behaviour, pp. 243–258 (1991)Google Scholar
  16. Cascetta, E., Nuzzolo, A., Russo, F., Vitetta, A.: A modified logit route choice model overcoming path overlapping problems: specification and some calibration results for interurban networks. In: Proceedings of the 13th International Symposium on Transportation and Traffic Theory, pp. 697–711. Pergamon Oxford, NY, USA (1996)Google Scholar
  17. Cascetta, E., Russo, F., Viola, F.A., Vitetta, A.: A model of route perception in urban road networks. Transp. Res. Part B Methodol. 36(7), 577–592 (2002)CrossRefGoogle Scholar
  18. Communauté métropolitaine de montréal, C. (2012) An attractive, competitive and sustainable greater Montreal. In. Library and Archives Canada Montréal, QuébecGoogle Scholar
  19. Connors, R.D., Sumalee, A.: A network equilibrium model with travellers’ perception of stochastic travel times. Transp. Res. Part B Methodol. 43(6), 614–624 (2009). doi: 10.1016/j.trb.2008.12.002 CrossRefGoogle Scholar
  20. Couclelis, H., Golledge, R.G., Gale, N., Tobler, W.: Exploring the anchor-point hypothesis of spatial cognition. J. Environ. Psychol. 7(2), 99–122 (1987)CrossRefGoogle Scholar
  21. de la Barra, T., Perez, B., Anez, J.: Multidimensional path search and assignment. In: PTRC Summer Annual Meeting, 21st, 1993, University of Manchester, United Kingdom (1993)Google Scholar
  22. Dhakar, N., Srinivasan, S.: Route choice modeling using GPS-based travel surveys. Transp. Res. Rec. J. Transp. Res. Board 2413, 65–73 (2014)CrossRefGoogle Scholar
  23. Dougherty, M.: A review of neural networks applied to transport. Transp. Res. Part C Emerg. Technol. 3(4), 247–260 (1995). doi: 10.1016/0968-090X(95)00009-8 CrossRefGoogle Scholar
  24. Duan, Z., Wei, Y.: Revealing taxi driver route choice characteristics based on GPS data. In: CICTP 2014@ sSafe, Smart, and Sustainable Multimodal Transportation Systems, pp. 565–573 (2014)Google Scholar
  25. Elgar, I., Farooq, B., Miller, E.: Modeling location decisions of office firms: introducing anchor points and constructing choice sets in the model system. Transp. Res. Rec. J. Transp. Res. Board 2133, 56–63 (2009)CrossRefGoogle Scholar
  26. Elgar, I., Farooq, B., Miller, E.J.: Simulations of firm location decisions: replicating office location choices in the Greater Toronto Area. J. Choice Modell. 17, 39–51 (2015). doi: 10.1016/j.jocm.2015.12.003 CrossRefGoogle Scholar
  27. Flötteröd, G., Bierlaire, M.: Metropolis-Hastings sampling of paths. Transp. Res. Part B Methodol. 48, 53–66 (2013)CrossRefGoogle Scholar
  28. Foo, P., Warren, W.H., Duchon, A., Tarr, M.J.: Do humans integrate routes into a cognitive map? Map-versus landmark-based navigation of novel shortcuts. J. Exp. Psychol. Learn. Mem. Cogn. 31(2), 195 (2005)CrossRefGoogle Scholar
  29. Fosgerau, M., Frejinger, E., Karlstrom, A.: A link based network route choice model with unrestricted choice set. Transp. Res. Part B Methodol. 56, 70–80 (2013). doi: 10.1016/j.trb.2013.07.012 CrossRefGoogle Scholar
  30. Frejinger, E., Bierlaire, M.: On path generation algorithms for route choice models. In: Hess, S., Daly, A. (eds.) Choice Modelling: The State-of-the-Art and the State-of-Practice, pp. 307–315 (2010)Google Scholar
  31. Frejinger, E., Bierlaire, M., Ben-Akiva, M.: Sampling of alternatives for route choice modeling. Transp. Res. Part B Methodol. 43(10), 984–994 (2009)CrossRefGoogle Scholar
  32. Gao, S., Frejinger, E., Ben-Akiva, M.: Adaptive route choices in risky traffic networks: a prospect theory approach. Transp. Res. Part C Emerg. Technol. 18(5), 727–740 (2010). doi: 10.1016/j.trc.2009.08.001 CrossRefGoogle Scholar
  33. Golledge, R.G., Smith, T.R., Pellegrino, J.W., Doherty, S., Marshall, S.P.: A conceptual model and empirical analysis of children’s acquisition of spatial knowledge. J. Environ. Psychol. 5(2), 125–152 (1985)CrossRefGoogle Scholar
  34. Guevara, C.A., Ben-Akiva, M.E.: Sampling of alternatives in multivariate extreme value (MEV) models. Transp. Res. Part B Methodol. 48, 31–52 (2013)CrossRefGoogle Scholar
  35. Habib, K.N., Morency, C., Trépanier, M., Salem, S.: Application of an independent availability logit model (IAL) for route choice modelling: considering bridge choice as a key determinant of selected routes for commuting in Montreal. J. Choice Modell. 9, 14–26 (2013)CrossRefGoogle Scholar
  36. Henn, V.: Route choice making under uncertainty: a fuzzy logic based approach. In: Verdegay, J.-L. (ed.) Fuzzy Sets Based Heuristics for Optimization, vol. 126. Studies in Fuzziness and Soft Computing, pp. 277–292. Springer, Berlin Heidelberg (2003)CrossRefGoogle Scholar
  37. Hess, S., Daly, A.J.: Choice Modelling: The State-of-the-Art and the State-of-Practice. In: Proceedings from the Inaugural International Choice Modelling Conference: The State-of-the-art and the State-of-practice: Proceedings from the Inaugural International Choice Modelling Conference. Emerald Group Publishing (2010)Google Scholar
  38. Hess, S., Quddus, M., Rieser-Schüssler, N., Daly, A.: Developing advanced route choice models for heavy goods vehicles using GPS data. Transp. Res. Part E Logist. Transp. Rev. 77, 29–44 (2015)CrossRefGoogle Scholar
  39. Hirtle, S.C., Jonides, J.: Evidence of hierarchies in cognitive maps. Mem. Cognit. 13(3), 208–217 (1985)CrossRefGoogle Scholar
  40. Holding, C.S.: Further evidence for the hierarchical representation of spatial information. J. Environ. Psychol. 14(2), 137–147 (1994). doi: 10.1016/S0272-4944(05)80167-7 CrossRefGoogle Scholar
  41. Kahneman, D., Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econom. J. Econom. Soc. 263–291Google Scholar
  42. Kaplan, S., Prato, C.G.: Closing the gap between behavior and models in route choice: the role of spatiotemporal constraints and latent traits in choice set formation. Transp. Res. Part F Traffic Psychol. Behav. 15(1), 9–24 (2012). doi: 10.1016/j.trf.2011.11.001 CrossRefGoogle Scholar
  43. Kazagli, E., Bierlaire, M.: A route choice model based on mental representations. In: Proceedings of the 15th Swiss Transport Research Conference, vol. EPFL-CONF-208979 (2015)Google Scholar
  44. Kim, J., Sung, S., Namgung, M., Jang, Y.: Development of dynamic route choice behavioral applied intelligent system theory. In: Proceedings of the Eastern Asia Society for Transportation Studies, pp. 1615–1630 (2005)Google Scholar
  45. Lai, X., Bierlaire, M.: Specification of the cross-nested logit model with sampling of alternatives for route choice models. Transp. Res. Part B Methodol. 80, 220–234 (2015). doi: 10.1016/j.trb.2015.07.005 CrossRefGoogle Scholar
  46. Luisa De Maio, M., Vitetta, A. Route choice on road transport system: a fuzzy approach. J. Intell. Fuzzy Syst. 28(5), 2015–2027 (2015)Google Scholar
  47. Lynch, K.: The Image of the City, vol. 11. MIT press, Cambridge (1960)Google Scholar
  48. Manley, E., Addison, J., Cheng, T.: Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London. J. Transp. Geogr. 43, 123–139 (2015a)CrossRefGoogle Scholar
  49. Manley, E., Orr, S., Cheng, T.: A heuristic model of bounded route choice in urban areas. Transp. Res. Part C Emerg. Technol. 56, 195–209 (2015b)CrossRefGoogle Scholar
  50. Manski, C.F.: The structure of random utility models. Theor. Decis. 8(3), 229–254 (1977)CrossRefGoogle Scholar
  51. McFadden, D.: Modelling the Choice of Residential Location. Institute of Transportation Studies, University of California, Berkeley (1978)Google Scholar
  52. McFadden, D., Train, K.: Mixed MNL models for discrete response. J. Appl. Econom. 15(5), 447–470 (2000)Google Scholar
  53. McFadden, D.L.: Econometric analysis of qualitative response models. Handb. Econom. 2, 1395–1457 (1984)CrossRefGoogle Scholar
  54. Murat, Y.S., Uludag, N.: Route choice modelling in urban transportation networks using fuzzy logic and logistic regression methods. J. Sci. Ind. Res. 67(1), 19 (2008)Google Scholar
  55. Prato, C., Bekhor, S.: Applying branch-and-bound technique to route choice set generation. Transp. Res. Rec. J. Transp. Res. Board 1985, 19–28 (2006)CrossRefGoogle Scholar
  56. Prato, C., Bekhor, S.: Modeling route choice behavior: how relevant is the composition of choice set? Transp. Res. Rec. J. Transp. Res. Board 2003, 64–73 (2007)Google Scholar
  57. Prato, C.G.: Route choice modeling: past, present and future research directions. J. Choice Modell. 2(1), 65–100 (2009). doi: 10.1016/S1755-5345(13)70005-8 CrossRefGoogle Scholar
  58. Prato, C.G.: Expanding the applicability of random regret minimization for route choice analysis. Transportation 41(2), 351–375 (2014)CrossRefGoogle Scholar
  59. Prato, C.G., Bekhor, S., Pronello, C.: Latent variables and route choice behavior. Transportation 39(2), 299–319 (2012)CrossRefGoogle Scholar
  60. Quattrone, A., Vitetta, A.: Random and fuzzy utility models for road route choice. Transp. Res. Part E Logist. Transp. Rev. 47(6), 1126–1139 (2011). doi: 10.1016/j.tre.2011.04.007 CrossRefGoogle Scholar
  61. Ramming, M.S.: Network Knowledge and Route Choice. Massachusetts Institute of Technology, Cambridge (2001)Google Scholar
  62. Sun, H., Wu, J., Ma, D., Long, J.: Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies. Appl. Math. Model. 38(2), 496–505 (2014). doi: 10.1016/j.apm.2013.06.027 CrossRefGoogle Scholar
  63. Swait, J., Ben-Akiva, M.: Incorporating random constraints in discrete models of choice set generation. Transp. Res. Part B Methodol. 21(2), 91–102 (1987)CrossRefGoogle Scholar
  64. Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  65. Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertain. 5(4), 297–323 (1992)CrossRefGoogle Scholar
  66. Vitetta, A.: A quantum utility model for route choice in transport systems. Travel Behav. Soc. 3, 29–37 (2016). doi: 10.1016/j.tbs.2015.07.003 CrossRefGoogle Scholar
  67. Vovsha, P., Bekhor, S.: Link-nested logit model of route choice: overcoming route overlapping problem. Transp. Res. Rec. J. Transp. Res. Board 1645, 133–142 (1998)CrossRefGoogle Scholar
  68. Walker, J., Ben-Akiva, M., Bolduc, D. (2004) Identification of the logit kernel (or mixed logit) model. In: 10th International Conference on Travel Behavior Research, Lucerne, SwitzerlandGoogle Scholar
  69. White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transp. Res. Part C Emerg. Technol. 8(1), 91–108 (2000)CrossRefGoogle Scholar
  70. Wiener, J.M., Mallot, H.A.: ‘Fine-to-coarse’route planning and navigation in regionalized environments. Spat. Cogn. Comput. 3(4), 331–358 (2003)CrossRefGoogle Scholar
  71. Woo, C.K., Cheng, Y.S., Li, R., Shiu, A., Ho, S.T., Horowitz, I.: Can Hong Kong price-manage its cross-harbor-tunnel congestion? Transp. Res. Part A Pol. Pract. 82, 94–109 (2015). doi: 10.1016/j.tra.2015.09.002 CrossRefGoogle Scholar
  72. Xu, H., Zhou, J., Xu, W.: A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory. Transp. Res. Part C Emerg. Technol. 19(2), 218–228 (2011). doi: 10.1016/j.trc.2010.05.009 CrossRefGoogle Scholar
  73. Zhou, J., Golledge, R.: A three-step general map matching method in the GIS environment: travel/transportation study perspective. Int. J. Geogr. Inf. Syst. 8(3), 243–260 (2006)Google Scholar

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© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Civil, Geological and Mining EngineeringPolytechnique MontréalMontrealCanada

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