Identifying the Impacted Area of Congestion Charging Based on Cumulative Prospect Theory

  • Qing-yu LuoEmail author
  • Xin-yu Guan
  • Wen-jing Wu
  • Hong-fei Jia
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


A precise quantitative framework needs to be developed to identify the impacted area of road network by congestion charging. In this paper, a framework is constructed, which includes modeling traffic network based on CPT as a foundation and proposing identifying method on the basis of defining traffic impact threshold. To study the road network performance under congestion charging, a route choice model with an improved reference point is set based on generalized travel cost and a stochastic user equilibrium model based on CPT is adopted. Two indicators, variation of total travel time and the degree of saturation increase, are chosen as determination parameters in identifying method. Finally, a case study shows that the impacted area can be identified quantitatively. This paper puts forward a new thought and quantitative method to analyze the impacted area of traffic management policies.


Congestion charging Traffic impact Reference point Route choice Impacted threshold 



This research is funded by the National Natural Science Foundation of China 70901032.


  1. 1.
    Ge YE, Stewart K, Sun B et al (2016) Investigating undesired spatial and temporal boundary effects of congestion charging. Transportmetrica B 4(2):1–23Google Scholar
  2. 2.
    Guo W, Yao DY, Jian-Ming HU (2006) Study on network performance evaluation modeling under congestion pricing. J Highw Transp Res Dev 01:105–109Google Scholar
  3. 3.
    Xu H, Lou Y, Yin Y et al (2011) A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing. Transp Res Part B: Methodol 45(2):311–328 CrossRefGoogle Scholar
  4. 4.
    Sabounchi NS, Triantis KP, Sarangi S, Liu S (2014) A framework for evaluating the dynamic impacts of a congestion pricing policy for a transportation socioeconomic system. Transp Res Part A: Policy Pract 59(1):357–383Google Scholar
  5. 5.
    Avineri E, Prashker J (2004) Violations of expected utility theory in route-choice stated preferences: certainty effect and inflation of small probabilities. Transp Res Rec J Transp Res Board 1894(1):222–229CrossRefGoogle Scholar
  6. 6.
    Avineri E, Prashker JN, Avineri E et al (2003) Sensitivity to uncertainty: the need for a paradigm shift. Ann Meet Transp Res Board 11(7):2406–2419Google Scholar
  7. 7.
    Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertainty 5(4):297–323CrossRefGoogle Scholar
  8. 8.
    Jou RC, Chen KH (2013) An application of cumulative prospect theory to freeway drivers’ route choice behaviours. Transp Res Part A: Policy Pract 49(C):123–131Google Scholar
  9. 9.
    Yang J, Jiang G (2014) Development of an enhanced route choice model based on cumulative prospect theory. Transp Res Part C: Emerg Technol 47:168–178CrossRefGoogle Scholar
  10. 10.
    Avineri E (2006) The effect of reference point on stochastic network equilibrium. Transp Sci 40(4):409–420CrossRefGoogle Scholar
  11. 11.
    Sang N, Dupuis C (1984) An efficient method for computing traffic equilibria in networks with asymmetric transportation costs. Transp Sci 18(2):185–202CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qing-yu Luo
    • 1
    Email author
  • Xin-yu Guan
    • 1
  • Wen-jing Wu
    • 1
  • Hong-fei Jia
    • 1
  1. 1.School of TransportationJilin UniversityChangchunChina

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