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Bridging the user equilibrium and the system optimum in static traffic assignment: a review

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  • Valentina Morandi

    (University of Brescia)

Abstract

Solving the road congestion problem is one of the most pressing issues in modern cities since it causes time wasting, pollution, higher industrial costs and huge road maintenance costs. Advances in ITS technologies and the advent of autonomous vehicles are changing mobility dramatically. They enable the implementation of a coordination mechanism, called coordinated traffic assignment, among the sat-nav devices aiming at assigning paths to drivers to eliminate congestion and to reduce the total travel time in traffic networks. Among possible congestion avoidance methods, coordinated traffic assignment is a valuable choice since it does not involve huge investments to expand the road network. Traffic assignments are traditionally devoted to two main perspectives on which the well-known Wardropian principles are inspired: the user equilibrium and the system optimum. User equilibrium is a user-driven traffic assignment in which each user chooses the most convenient path selfishly. It guarantees that fairness among users is respected since, when the equilibrium is reached, all users sharing the same origin and destination will experience the same travel time. The main drawback in a user equilibrium is that the system total travel time is not minimized and, hence, the so-called Price of Anarchy is paid. On the other hand, the system optimum is an efficient system-wide traffic assignment in which drivers are routed on the network in such a way the total travel time is minimized, but users might experience travel times that are higher than the other users travelling from the same origin to the same destination, affecting the compliance. Thus, drawbacks in implementing one of the two assignments can be overcome by hybridizing the two approaches, aiming at bridging users’ fairness to system-wide efficiency. In the last decades, a significant number of attempts have been done to bridge fairness among users and system efficiency in traffic assignments. The survey reviews the state-of-the-art of these trade-off approaches.

Suggested Citation

  • Valentina Morandi, 2024. "Bridging the user equilibrium and the system optimum in static traffic assignment: a review," 4OR, Springer, vol. 22(1), pages 89-119, March.
    • Handle: RePEc:spr:aqjoor:v:22:y:2024:i:1:d:10.1007_s10288-023-00540-w
    DOI: 10.1007/s10288-023-00540-w
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    as
    1. Kaj Holmberg & Di Yuan, 2003. "A Multicommodity Network-Flow Problem with Side Constraints on Paths Solved by Column Generation," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 42-57, February.
    2. Grazia Speranza, M., 2018. "Trends in transportation and logistics," European Journal of Operational Research, Elsevier, vol. 264(3), pages 830-836.
    3. Levy, Nadav & Klein, Ido & Ben-Elia, Eran, 2018. "Emergence of cooperation and a fair system optimum in road networks: A game-theoretic and agent-based modelling approach," Research in Transportation Economics, Elsevier, vol. 68(C), pages 46-55.
    4. Watling, David, 2006. "User equilibrium traffic network assignment with stochastic travel times and late arrival penalty," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1539-1556, December.
    5. Liu, Haoxiang & Wang, David Z.W., 2015. "Global optimization method for network design problem with stochastic user equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 20-39.
    6. Olaf Jahn & Rolf H. Möhring & Andreas S. Schulz & Nicolás E. Stier-Moses, 2005. "System-Optimal Routing of Traffic Flows with User Constraints in Networks with Congestion," Operations Research, INFORMS, vol. 53(4), pages 600-616, August.
    7. Angelelli, E. & Arsik, I. & Morandi, V. & Savelsbergh, M. & Speranza, M.G., 2016. "Proactive route guidance to avoid congestion," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 1-21.
    8. José R. Correa & Andreas S. Schulz & Nicolás E. Stier-Moses, 2007. "Fast, Fair, and Efficient Flows in Networks," Operations Research, INFORMS, vol. 55(2), pages 215-225, April.
    9. Di, Xuan & Liu, Henry X., 2016. "Boundedly rational route choice behavior: A review of models and methodologies," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 142-179.
    10. Bayram, Vedat & Tansel, Barbaros Ç. & Yaman, Hande, 2015. "Compromising system and user interests in shelter location and evacuation planning," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 146-163.
    11. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
    12. Erel Avineri, 2006. "The Effect of Reference Point on Stochastic Network Equilibrium," Transportation Science, INFORMS, vol. 40(4), pages 409-420, November.
    13. Hosseininasab, Seyyed-Mohammadreza & Shetab-Boushehri, Seyyed-Nader & Hejazi, Seyed Reza & Karimi, Hadi, 2018. "A multi-objective integrated model for selecting, scheduling, and budgeting road construction projects," European Journal of Operational Research, Elsevier, vol. 271(1), pages 262-277.
    14. Xu, Hongli & Lou, Yingyan & Yin, Yafeng & Zhou, Jing, 2011. "A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 311-328, February.
    15. O'Hare, Steven J. & Connors, Richard D. & Watling, David P., 2016. "Mechanisms that govern how the Price of Anarchy varies with travel demand," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 55-80.
    16. Vincenzo Bonifaci & Tobias Harks & Guido Schäfer, 2010. "Stackelberg Routing in Arbitrary Networks," Mathematics of Operations Research, INFORMS, vol. 35(2), pages 330-346, May.
    17. Guo, Xiaolei & Yang, Hai, 2009. "User heterogeneity and bi-criteria system optimum," Transportation Research Part B: Methodological, Elsevier, vol. 43(4), pages 379-390, May.
    18. Hani S. Mahmassani & Gang-Len Chang, 1987. "On Boundedly Rational User Equilibrium in Transportation Systems," Transportation Science, INFORMS, vol. 21(2), pages 89-99, May.
    19. Bhaskar, Umang & Ligett, Katrina & Schulman, Leonard J. & Swamy, Chaitanya, 2019. "Achieving target equilibria in network routing games without knowing the latency functions," Games and Economic Behavior, Elsevier, vol. 118(C), pages 533-569.
    20. Di, Xuan & He, Xiaozheng & Guo, Xiaolei & Liu, Henry X., 2014. "Braess paradox under the boundedly rational user equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 86-108.
    21. Pitu Mirchandani & Hossein Soroush, 1987. "Generalized Traffic Equilibrium with Probabilistic Travel Times and Perceptions," Transportation Science, INFORMS, vol. 21(3), pages 133-152, August.
    22. Lo, Hong K. & Luo, X.W. & Siu, Barbara W.Y., 2006. "Degradable transport network: Travel time budget of travelers with heterogeneous risk aversion," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 792-806, November.
    23. Wu, Di & Yin, Yafeng & Lawphongpanich, Siriphong & Yang, Hai, 2012. "Design of more equitable congestion pricing and tradable credit schemes for multimodal transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1273-1287.
    24. Riccardo Colini-Baldeschi & Roberto Cominetti & Panayotis Mertikopoulos & Marco Scarsini, 2020. "When Is Selfish Routing Bad? The Price of Anarchy in Light and Heavy Traffic," Operations Research, INFORMS, vol. 68(2), pages 411-434, March.
    25. Meng, Q. & Yang, H. & Bell, M. G. H., 2001. "An equivalent continuously differentiable model and a locally convergent algorithm for the continuous network design problem," Transportation Research Part B: Methodological, Elsevier, vol. 35(1), pages 83-105, January.
    26. Lou, Yingyan & Yin, Yafeng & Lawphongpanich, Siriphong, 2010. "Robust congestion pricing under boundedly rational user equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 15-28, January.
    27. Yang, Hai & Wang, Xiaolei, 2011. "Managing network mobility with tradable credits," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 580-594, March.
    28. Di, Xuan & Liu, Henry X. & Pang, Jong-Shi & Ban, Xuegang (Jeff), 2013. "Boundedly rational user equilibria (BRUE): Mathematical formulation and solution sets," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 300-313.
    29. Takashi Akamatsu & Benjamin Heydecker, 2003. "Detecting Dynamic Traffic Assignment Capacity Paradoxes in Saturated Networks," Transportation Science, INFORMS, vol. 37(2), pages 123-138, May.
    30. Di, Xuan & Liu, Henry X. & Ban, Xuegang (Jeff), 2016. "Second best toll pricing within the framework of bounded rationality," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 74-90.
    31. Cortés, Pablo & Muñuzuri, Jesús & Guadix, José & Onieva, Luis, 2013. "Optimal algorithm for the demand routing problem in multicommodity flow distribution networks with diversification constraints and concave costs," International Journal of Production Economics, Elsevier, vol. 146(1), pages 313-324.
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