Networks and Spatial Economics

, Volume 13, Issue 2, pp 183–203 | Cite as

A Hybrid Route Choice Model for Dynamic Traffic Assignment

Article

Abstract

Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.

Keywords

Dynamic traffic assignment Hybrid route choice Traveler heterogeneity 

References

  1. Bell MGH, Shield CM, Busch F, Kruse G (1997) A stochastic user equilibrium path flow estimator. Transp Res, Part C Emerg Technol 5(3–4):197–210CrossRefGoogle Scholar
  2. Bovy P, Stern E (1990) Wayfinding in transport networks. Kluwer Academic PublishersGoogle Scholar
  3. Bovy PH, Fiorenzo-Catalano S (2007) Stochastic route choice set generation: behavioral and probabilistic foundations. Transportmetrica 3(3):173–189CrossRefGoogle Scholar
  4. Carey M, Ge YE (2011) Comparison of methods for path flow reassignment for dynamic user equilibrium. Netw Spat Econ. doi: 10.1007/s11067-011-9159-6 Google Scholar
  5. Cascetta E, Nuzzolo A, Russo F, Vitetta A (1996) A modified logit route choice model overcoming path overlapping problems: specification and some calibration results for interurban networks. In: Proceedings from the thirteenth international symposium on transportation and traffic theoryGoogle Scholar
  6. Daganzo C (1998) Queue spillovers in transportation networks with a route choice. Transp Sci 32(1):3–11CrossRefGoogle Scholar
  7. Daganzo CF (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res, Part B 28:269–287CrossRefGoogle Scholar
  8. Daganzo CF (1995) The cell transmission model, part II: network traffic. Transp Res, Part B 29:79–93CrossRefGoogle Scholar
  9. Dia H, Harney D, Boyle A (2001) Dynamics of drivers’ route choice decisions under advanced traveler information systems. Roads and Transp Res 10:2–12Google Scholar
  10. Friesz TL, Bernstein D, Smith TE, Tobin RL, Wei BW (1993) A variational inequality formulation of the dynamic network equilibrium problem. Oper Res 41:179–191CrossRefGoogle Scholar
  11. Gao S, Frejinger E, Ben-Akiva M (2011) Cognitive cost in route choice with real-time information: an exploratory analysis. Transp Res, Part A Policy Pract 45(9):916–926CrossRefGoogle Scholar
  12. Ghali M (1995) A note on the minimum instantaneous cost path of the dynamic traffic assignment problem. Ann Oper Res 60:115–120CrossRefGoogle Scholar
  13. Hawas YE (2004) Development and calibration of route choice utility models: neuro-fuzzy approach. J Transp Eng 130(2):171–182CrossRefGoogle Scholar
  14. Jin W, Zhang HM (2003) On the distribution schemes for determining flows through a merge. Transp Res, Part B 37:521–540CrossRefGoogle Scholar
  15. Jin W, Zhang HM (2004) Multicommodity kinematic wave simulation model for network traffic flow. Transp Res Rec 1883:59–67CrossRefGoogle Scholar
  16. Kant P (2008) Route choice modelling in dynamic traffic assignment. Master thesis, University of Twente. http://essay.utwente.nl/58303/
  17. Kuwahara M, Akamatsu T (2001) Dynamic user optimal assignment with physical queues for many-to-many od pattern. Transp Res, Part B 35:461–479CrossRefGoogle Scholar
  18. Lebacque J (1996) The godunov scheme and what it means for first order traffic flow models. In: Procedings of international symposium of transport and traffic theory, pp 79–102Google Scholar
  19. Lee C, Ran B, Yang F, Loh WY (2010) A hybrid tree approach to modeling alternate route choice behavior with online information. J of Intell Transp Sys: Technology, Planning, and Operations 14(4):209–219Google Scholar
  20. Lighthill MJ, Whitham GB (1955) On kinematic waves. ii. a theory of traffic flow on long crowded roads. Proc Royal Soc 229:317–345CrossRefGoogle Scholar
  21. Mahmassani HS, Jayakrishnan R (1991) System performance and user response under real-time information in a congested traffic corridor. Transp Res, Part A 25(5):293–307CrossRefGoogle Scholar
  22. Mahmassani HS, Liu YH (1999) Dynamics of commuting decision behaviour under advanced traveller information systems. Transp Res, Part C 7(2–3):91–107CrossRefGoogle Scholar
  23. Merchant D, Nemhauser G (1978) A model and an algorithm for the dynamic traffic assignment problem. Transp Sci 12:183–199CrossRefGoogle Scholar
  24. Nie X, Zhang HM (2005) A comparative study of some macroscopic link models used in dynamic traffic assignment. Netw Spat Econ 5:89–115CrossRefGoogle Scholar
  25. Nie Y (2006) A variational inequality approach for inferring dynamic origin-destination travel demands. PhD thesis, University of California at DavisGoogle Scholar
  26. Nie Y, Zhang HM (2010) Solving the dynamic user optimal assignment problem considering queue spillback. Netw Spat Econ 10(1):49–71CrossRefGoogle Scholar
  27. Nie YM, Wu X, de Mello TH (2011) Optimal path problems with second-order stochastic dominance constraints. Netw Spat Econ. doi: 10.1007/s11067-011-9167-6 Google Scholar
  28. Peeta S, Mahmassani HS (1995) Multiple user classes real-time traffic assignment for online operations: a rolling horizon solution framework. Transp Res, Part C Emerg Technol 3(2):83–98CrossRefGoogle Scholar
  29. Peeta S, Yu JW (2005) A hybrid model for driver route choice incorporating en-route attributes and real-time information effects. Netw Spat Econ 5:21–40CrossRefGoogle Scholar
  30. Pel AJ, Bliemer MC, Hoogendoorn SP (2009) Hybrid routing choice modeling in dynamic traffic assignment. Transp Res Rec 2091:100–107CrossRefGoogle Scholar
  31. Ramming S (2002) Network knowledge and route choice. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  32. Ran B, Boyce DE, Leblanc LJ (1993) A new class of instantaneous dynamic user-optimal traffic assignment models. Oper Res 41(1):192–202CrossRefGoogle Scholar
  33. Reddy PDVG, Yang H, Vaughn KM, Abdel-Aty MA, Kitamura R, Jovanis PP (1995) Design of an artificial simulator for analyzing route choice behavior in the presence of information system. Math Comput Model 22(4–7):119–147CrossRefGoogle Scholar
  34. Richards PI (1956) Shock waves on the highway. Proc R Soc 4:42–51Google Scholar
  35. Smith M (1993) A new dynamic traffic model and the existence and calculation of dynamic user equilibria on congested capacity-constrained road networks. Transp Res, Part B 26:49–36Google Scholar
  36. Ukkusuri S, Patil G (2007) Exploring user behavior in online network equilibrium problems. Transp Res Rec: Journal of the Transportation Research Board 2029:31–38CrossRefGoogle Scholar
  37. Vovsha P, Bekhor S (1998) The link-nested logit model of route choice: overcoming the route overlapping problem. Transp Res Rec 1645:133–142CrossRefGoogle Scholar
  38. Xu H, Zhou J, Xu W (2011) A decision-making rule for modeling travelers route choice behavior based on cumulative prospect theory. Transp Res, Part C Emerg Technol 19(2):218–228CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of California DavisDavisUSA

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