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Congestion behavior and tolling strategies in a bottleneck model with exponential scheduling preference

  • Received: 03 October 2022 Revised: 24 November 2022 Accepted: 29 November 2022 Published: 13 December 2022
  • The bottleneck model has been widely used in the past fifty years to analyze the morning commute. To reduce the complexity of analysis, most previous studies adopted discontinuous scheduling preference (DSP). However, this handling destroys the continuity in departure rate and differentiability in travel time and cumulative departures. This paper considers an exponential scheduling preference (ESP), which supposes the unit schedule delay cost for commuters exponentially changes with time. With this scheduling preference, we analytically derive solutions and economic properties of user equilibrium and social optimum in the bottleneck model. The first-best, time-varying toll and the optimal single-step toll scheme with ESP are also studied. Results indicate that ESP eliminates the discontinuity in departure rate and non-differentiability in travel time and cumulative departures, which makes the process of morning commute smooth. The ignorance of ESP will lead to underestimation in the queueing time and bias in travel behavior analysis and policymaking.

    Citation: Chuanyao Li, Yichao Lu, Yuqiang Wang, Gege Jiang. Congestion behavior and tolling strategies in a bottleneck model with exponential scheduling preference[J]. Electronic Research Archive, 2023, 31(2): 1065-1088. doi: 10.3934/era.2023053

    Related Papers:

  • The bottleneck model has been widely used in the past fifty years to analyze the morning commute. To reduce the complexity of analysis, most previous studies adopted discontinuous scheduling preference (DSP). However, this handling destroys the continuity in departure rate and differentiability in travel time and cumulative departures. This paper considers an exponential scheduling preference (ESP), which supposes the unit schedule delay cost for commuters exponentially changes with time. With this scheduling preference, we analytically derive solutions and economic properties of user equilibrium and social optimum in the bottleneck model. The first-best, time-varying toll and the optimal single-step toll scheme with ESP are also studied. Results indicate that ESP eliminates the discontinuity in departure rate and non-differentiability in travel time and cumulative departures, which makes the process of morning commute smooth. The ignorance of ESP will lead to underestimation in the queueing time and bias in travel behavior analysis and policymaking.



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    [1] R. Arnott, E. DePalma, The corridor problem: preliminary results on the no-toll equilibrium, Transp. Res. Part B Methodol., 45 (2011), 743–768. https://10.1016/j.trb.2011.01.004 doi: 10.1016/j.trb.2011.01.004
    [2] M. Fosgerau, J. Kim, A. Ranjan, Vickrey meets Alonso: Commute scheduling and congestion in a monocentric city, J. Urban Econ., 105 (2018), 40–53. https://doi.org/10.1016/j.jue.2018.02.003 doi: 10.1016/j.jue.2018.02.003
    [3] Y. Oh, J. Kwak, S. Kim, Time delay estimation of traffic congestion propagation due to accidents based on statistical causality, Electron. Res. Arch., 31 (2023), 691–707. https://doi.org/10.3934/era.2023034 doi: 10.3934/era.2023034
    [4] C. Li, Y. Lu, G. Jiang, Q. Sun, Social optimum for linear staggered shifts in a single-entry traffic corridor with no late arrivals, Transportmetrica B Transp. Dyn., 2022 (2022), 1–19. https://doi.org/10.1080/21680566.2022.2113176 doi: 10.1080/21680566.2022.2113176
    [5] R. Arnott, E. DePalma, R. Lindsey, Economics of a bottleneck, J. Urban Econ., 27 (1990), 111–130. https://doi.org/10.1016/0094-1190(90)90028-L doi: 10.1016/0094-1190(90)90028-L
    [6] A. Selmoune, Z. Y. Liu, J. W. Lee, To pay or not to pay? Understanding public acceptance of congestion pricing: A case study of Nanjing, Electron. Res. Arch., 30 (2022), 4136–4156. https://doi.org/10.3934/era.2022209 doi: 10.3934/era.2022209
    [7] W. S. Vickrey, Congestion theory and transport investment, Am. Econ. Rev., 59 (1969), 251–260.
    [8] K. A. Small, The bottleneck model: An assessment and interpretation, Econ. Transp., 4 (2015), 110–117. https://doi.org/10.1016/j.ecotra.2015.01.001 doi: 10.1016/j.ecotra.2015.01.001
    [9] G. G. Jiang, H. K. Lo, Q. R. Tang, Z. Liang, S. L. Wang, The impact of road pricing on travel time variability under elastic demand, Transportmetrica B Transp. Dyn., 9 (2021), 595–621. https://doi.org/10.1080/21680566.2021.1919938 doi: 10.1080/21680566.2021.1919938
    [10] Z. C. Li, W. H. Lam, S. C. Wong, Bottleneck model revisited: An activity-based perspective, Transp. Res. Part B Methodol., 68 (2014), 262–287. https://doi.org/10.1016/j.trb.2014.06.013 doi: 10.1016/j.trb.2014.06.013
    [11] L. L. Xiao, T. L. Liu, H. J. Huang, R. Liu, Temporal-spatial allocation of bottleneck capacity for managing morning commute with carpool, Transp. Res. Part B Methodol., 143 (2021), 177–200. https://doi.org/10.1016/j.trb.2020.11.007 doi: 10.1016/j.trb.2020.11.007
    [12] G. G. Jiang, S. L. Wang, H. K. Lo, Z. Liang, Modeling cost variability in a bottleneck model with degradable capacity, Transportmetrica B Transp. Dyn., 10 (2022), 84–110. https://doi.org/10.1080/21680566.2021.1962430 doi: 10.1080/21680566.2021.1962430
    [13] L. L. Xiao, H. J. Huang, R. H. Liu, Congestion behavior and tolls in a bottleneck model with stochastic capacity, Transp. Sci., 49 (2015), 46–65. https://doi.org/10.1287/trsc.2013.0483 doi: 10.1287/trsc.2013.0483
    [14] J. C. Long, H. Yang, W. Y. Szeto, Departure time choice equilibrium and tolling strategies for a bottleneck with stochastic capacity, Transp. Sci., 56 (2022), 79–102. https://doi.org/10.1287/trsc.2021.1039 doi: 10.1287/trsc.2021.1039
    [15] F. Xiao, Z. Qian, H. M. Zhang, Managing bottleneck congestion with tradable credits, Transp. Res. Part B Methodol., 56 (2013), 1–14. https://doi.org/10.1016/j.trb.2013.06.016 doi: 10.1016/j.trb.2013.06.016
    [16] R. Lamotte, E. DePalma, N. Geroliminis, Impacts of metering-based dynamic priority schemes, Transp. Sci., 56 (2022), 358–380. https://doi.org/10.1287/trsc.2021.1091 doi: 10.1287/trsc.2021.1091
    [17] K. A. Small, The scheduling of consumer activities: work trips, Am. Econ. Rev., 72 (1982), 467–479.
    [18] J. Knockaert, E. T. Verhoef, J. Rouwendal, Bottleneck congestion: Differentiating the coarse charge, Transp. Res. Part B Methodol., 83 (2016), 59–73. https://doi.org/10.1016/j.trb.2015.11.004 doi: 10.1016/j.trb.2015.11.004
    [19] C. Y. Li, H. J. Huang, User equilibrium of a single-entry traffic corridor with continuous scheduling preference, Transp. Res. Part B Methodol., 108 (2018), 21–38. https://doi.org/10.1016/j.trb.2017.12.010 doi: 10.1016/j.trb.2017.12.010
    [20] W. S. Vickrey, Pricing, Metering, and Efficiently Using Urban Transportation Facilities, Highway Research Board Press, Washington, USA, 1973.
    [21] Y. Y. Tseng, E. T. Verhoef, Value of time by time of day: A stated-preference study, Transp. Res. Part B Methodol., 42 (2008), 607–618. https://doi.org/10.1016/j.trb.2007.12.001 doi: 10.1016/j.trb.2007.12.001
    [22] E. Jenelius, L. G. Mattsson, D. Levinson, Traveler delay costs and value of time with trip chains, flexible activity scheduling and information, Transp. Res. Part B Methodol., 45 (2011), 789–807. https://doi.org/10.1016/j.trb.2011.02.003 doi: 10.1016/j.trb.2011.02.003
    [23] K. Hjorth, M. Börjesson, L. Engelson, M. Fosgerau, Estimating exponential scheduling preferences, Transp. Res. Part B Methodol., 81 (2015), 230–251. https://doi.org/10.1016/j.trb.2015.03.014 doi: 10.1016/j.trb.2015.03.014
    [24] C. Y. Li, H. J. Huang, Analysis of bathtub congestion with continuous scheduling preference, Res. Transp. Econ., 75 (2019), 45–54. https://doi.org/10.1016/j.retrec.2019.05.002 doi: 10.1016/j.retrec.2019.05.002
    [25] C. Hendrickon, E. Plank, The flexibility of departure times for work trips, Transp. Res. Part A Policy Pract., 18 (1984), 25–36. https://doi.org/10.1016/0191-2607(84)90091-8 doi: 10.1016/0191-2607(84)90091-8
    [26] L. Engelson, M. Fosgerau, Additive measures of travel time variability, Transp. Res. Part B Methodol., 45 (2011), 1560–1571. https://doi.org/10.1016/j.trb.2011.07.002 doi: 10.1016/j.trb.2011.07.002
    [27] Z. C. Li, W. H. Lam, S. C. Wong, Step tolling in an activity-based bottleneck model, Transp. Res. Part B Methodol., 101 (2017), 306–334. https://doi.org/10.1016/j.trb.2017.04.001 doi: 10.1016/j.trb.2017.04.001
    [28] Z. C. Li, L. Zhang, The two-mode problem with bottleneck queuing and transit crowding: How should congestion be priced using tolls and fares, Transp. Res. Part B Methodol., 138 (2020), 46–76. https://doi.org/10.1016/j.trb.2020.05.008 doi: 10.1016/j.trb.2020.05.008
    [29] T. T. Zhu, Y. Li, J. C. Long, Departure time choice equilibrium and tolling strategies for a bottleneck with continuous scheduling preference, Transp. Res. Part E Logist. Transp. Rev., 159 (2022), 102644. https://doi.org/10.1016/j.tre.2022.102644 doi: 10.1016/j.tre.2022.102644
    [30] Y. Xiao, N. Coulombel, A. De Palma, The valuation of travel time reliability: does congestion matter, Transp. Res. Part B Methodol., 97 (2017), 113–141. https://doi.org/10.1016/j.trb.2016.12.003 doi: 10.1016/j.trb.2016.12.003
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