Networks and Spatial Economics

, Volume 14, Issue 1, pp 67–89 | Cite as

Location-Dependent Lane-Changing Behavior for Arterial Road Traffic

  • HongSheng Qi
  • DianHai Wang
  • Peng Chen
  • YiMing Bie


Lane-changing behavior plays an important role in characterizing urban arterial road traffic dynamics. This paper investigates efficiency-driven and objective-driven motives for drivers to change lanes on arterial roads. The former motive is determined by the circumstances of the surrounding traffic flow, whereas the latter depends on position. A location-dependent lane changing model is then established by weighing the two motives for lane changing. Both continuous and discrete versions of the arterial traffic model are obtained using the LWR (Lighthill–Whitham–Richards) model with the two types of lane changing. Simulations show that the proposed model can reproduce macroscopic traffic phenomena such as spillover and a decrease in the concomitant departure flow, which is the reason for capacity loss. It is concluded that 1) there exists a critical condition under which there is no capacity loss and that 2) a different flow composition could result in a different capacity loss, which varies according to the flow direction. Hence, traffic management and control should take this loss into account.


Traffic engineering Lane changing Channelized section Arterial roads Spillover 



This project is supported by the State Key Development Program for Basic Research of China (Grant No.2012CB725402); the National Science Foundation for Post-doctoral Scientists of China (Grant No. 2012M521175); and the Excellent Postdoctoral Science Foundation of Zhejiang Province(Bsh1202056)


  1. Ahmed K, Moshe E, Koutsopoulos H, Mishalani R (1996) Models of freeway lane changing and gap acceptance behavior. In: Proceedings of the 13th International symposium on the theory of traffic flow and transportation Lyon, FranceGoogle Scholar
  2. Cheu RL (2009) A cell transmission model with lane changing and vehicle tracking for port of entry simulations. Transp Res Rec J Transp Res Board 2124:241–248CrossRefGoogle Scholar
  3. Choudhury C, Toledo T, Ben-Akiva M (2004) NGSIM freeway lane selection model. Technical report. FHWA-HOP-06-103. Federal Highway Administration (FHWA)Google Scholar
  4. Del Castillo JM, Benítez FG (1995) On the functional form of the speed-density relationship—I: general theory. Transpn Res B 29:373–389CrossRefGoogle Scholar
  5. Flötteröd G, Yu Chen, Kai Nagel (2012)“Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation”, Networks and Spatial Economics, 12(4):481–502Google Scholar
  6. Hidas P (2002) Modelling lane changing and merging in microscopic traffic simulation. Transp Res Part C Emerg Technol 10(5–6):351–371CrossRefGoogle Scholar
  7. Hoogendoorn SP, Piet HL, Bovy (2001) Platoon-based multiclass modeling of multilane traffic flow. Networks and Spatial Economics 1(1):137–166Google Scholar
  8. Kachani S, Perakis G (2009) A dynamic travel time model for spillback. Netw Spat Econ 9(4):595–618CrossRefGoogle Scholar
  9. Kerner BS (2001) Complexity of synchronized flow and related problems for basic assumptions of traffic flow theories. Netw Spat Econ 1(1):35–76CrossRefGoogle Scholar
  10. Laval JA, Daganzo CF (2006) Lane-changing in traffic streams. Transp Res Part B Methodol 40(3):251–264CrossRefGoogle Scholar
  11. Li ZC (2011) Modeling arterial signal optimization with enhanced cell transmission formulations. J Transp Eng-ASCE 137(7):445–454CrossRefGoogle Scholar
  12. Liu Y, Chang G-L (2010) An arterial signal optimization model for intersections experiencing queue spillback and lane blockage. Transp Res Part C Emerg Technol 19(1):130–144CrossRefGoogle Scholar
  13. Long JC, Gao Z, Zhao X, Lian A, Orenstein P (2011) Urban Traffic Jam Simulation Based on the Cell Transmission Model. Netw Spat Econ 11(1):43–64CrossRefGoogle Scholar
  14. Michalopoulos PG, Beskos DE, Yamauchi Y (1984) Multilane traffic flow dynamics: some macroscopic considerations. Transp Res Part B Methodol 18(4–5):377–395CrossRefGoogle Scholar
  15. Sheu JB (2006) A composite traffic flow modeling approach for incident-responsive network traffic assignment. Physica a-Stat Mech Appl 367:461–478CrossRefGoogle Scholar
  16. Toledo T (2003) Modeling integrated lane-changing behavior. Transp Res Rec 1857:30–38CrossRefGoogle Scholar
  17. Toledo T, Koutsopoulos HN, Ben-Akiva M (2007) Integrated driving behavior modeling. Transp Res Part C Emerg Technol 15(2):96–112CrossRefGoogle Scholar
  18. Yang Q, Koutsopoulos HN (1996) A microscopic simulatior for evaluation of dynamic traffic management system. Trans Res Part C 4(3):113–129CrossRefGoogle Scholar
  19. Zhang HM (2001) New perspectives on continuum traffic flow models. Netw Spat Econ 9(1~2)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • HongSheng Qi
    • 1
  • DianHai Wang
    • 1
  • Peng Chen
    • 2
  • YiMing Bie
    • 3
  1. 1.Institute of Transportation Engineering, College of Civil Engineering ArchitectureZhejiang UniversityHangZhouChina
  2. 2.Department of Civil EngineeringNagoya UniversityNagoyaJapan
  3. 3.School of Transportation Science and EngineeringHarbin Institute of TechnologyHarbinChina

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