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Microsimulation models incorporating both demand and supply dynamics

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  • Liu, Ronghui
  • Van Vliet, Dirck
  • Watling, David

Abstract

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers' decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers' choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space-time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamics.

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  • Liu, Ronghui & Van Vliet, Dirck & Watling, David, 2006. "Microsimulation models incorporating both demand and supply dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 125-150, February.
    • Handle: RePEc:eee:transa:v:40:y:2006:i:2:p:125-150
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    References listed on IDEAS

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    Cited by:

    1. Yaron Hollander & Ronghui Liu, 2008. "The principles of calibrating traffic microsimulation models," Transportation, Springer, vol. 35(3), pages 347-362, May.
    2. Yavuz Duvarcı & Tan Yigitcanlar, 2019. "Can Tube Tunnel Crossings Relieve Urban Congestion Problems? Izmir Tube Tunnel Project Proposal Under Scrutiny," Sustainability, MDPI, vol. 11(9), pages 1-22, May.
    3. Toledo, Tomer & Cats , Oded & Burghout, Wilco & Koutsopoulos , Haris N., 2013. "Mesoscopic simulation for transit operations," Working papers in Transport Economics 2013:29, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    4. Cantarella, Giulio E. & Watling, David P., 2016. "A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 3-21.
    5. Liu, Ronghui & Smith, Mike, 2015. "Route choice and traffic signal control: A study of the stability and instability of a new dynamical model of route choice and traffic signal control," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 123-145.
    6. Smith, M.J. & Liu, R. & Mounce, R., 2015. "Traffic control and route choice: Capacity maximisation and stability," Transportation Research Part B: Methodological, Elsevier, vol. 81(P3), pages 863-885.
    7. Liu, Ronghui & May, Tony & Shepherd, Simon, 2011. "On the fundamental diagram and supply curves for congested urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 951-965, November.
    8. G. E. Cantarella & D. P. Watling, 2016. "Modelling road traffic assignment as a day-to-day dynamic, deterministic process: a unified approach to discrete- and continuous-time models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 69-98, March.
    9. David Watling & Giulio Cantarella, 2015. "Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems," Networks and Spatial Economics, Springer, vol. 15(3), pages 843-882, September.
    10. Saeed Asadi Bagloee & Majid Sarvi & Avishai Ceder, 2017. "Transit priority lanes in the congested road networks," Public Transport, Springer, vol. 9(3), pages 571-599, October.
    11. Crawford, F. & Watling, D.P. & Connors, R.D., 2018. "Identifying road user classes based on repeated trip behaviour using Bluetooth data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 55-74.
    12. Watling, David P. & Hazelton, Martin L., 2018. "Asymptotic approximations of transient behaviour for day-to-day traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 90-105.
    13. Barros, Carlos Pestana & Prieto-Rodriguez, Juan, 2008. "A revenue-neutral tax reform to increase demand for public transport services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(4), pages 659-672, May.
    14. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    15. Stefano de Luca & Roberta Di Pace & Silvio Memoli & Luigi Pariota, 2020. "Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design," Sustainability, MDPI, vol. 12(2), pages 1-20, January.

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