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Traffic Simulation with Dynameq

In: Fundamentals of Traffic Simulation

Author

Listed:
  • Michael Mahut

    (INRO Consultants Inc.)

  • Michael Florian

    (INRO Consultants Inc.
    University of Montreal)

Abstract

Dynameq is a simulation-based dynamic traffic assignmentdynamic traffic assignment (DTADTA ) model. This model employs an iterative solutioniterative solution method to find the user-optimal assignment of time-varying origin–destination demands to paths through a road network where the path travel times – which depend on the assigned path flows – are time-varying and determined using a detailed traffic simulationtraffic simulation model. Increasing congestion and the use of increasingly sophisticated measures to manage it – such as adaptive traffic control, reserved, reversible and tolled lanes, and time-varying congestion pricing – have created a need for models that are more detailed and realistic than static assignment models traditionally used in transportation planning. DTA models have begun to fill that need and have been successfully applied on real-world networks of significant size. This chapter provides a description of the assignment and simulation models that comprise the software, a discussion of fundamental concepts such as user-equilibriumuser-equilibrium and stabilitystability , an introduction to calibration methodology for simulation-based DTA, simulation-based DTA and a brief description of a typical project.

Suggested Citation

  • Michael Mahut & Michael Florian, 2010. "Traffic Simulation with Dynameq," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 323-361, Springer.
  • Handle: RePEc:spr:isochp:978-1-4419-6142-6_9
    DOI: 10.1007/978-1-4419-6142-6_9
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    Citations

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    Cited by:

    1. Piyapong Suwanno & Rattanaporn Kasemsri & Kaifeng Duan & Atsushi Fukuda, 2021. "Application of Macroscopic Fundamental Diagram under Flooding Situation to Traffic Management Measures," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    2. Lv, Wei & Song, Wei-guo & Liu, Xiao-dong & Ma, Jian, 2013. "A microscopic lane changing process model for multilane traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1142-1152.
    3. Jiancheng Long & Hai-Jun Huang & Ziyou Gao & W. Y. Szeto, 2013. "An Intersection-Movement-Based Dynamic User Optimal Route Choice Problem," Operations Research, INFORMS, vol. 61(5), pages 1134-1147, October.
    4. Piyapong Suwanno & Chaiwat Yaibok & Noriyasu Tsumita & Atsushi Fukuda & Kestsirin Theerathitichaipa & Manlika Seefong & Sajjakaj Jomnonkwao & Rattanaporn Kasemsri, 2023. "Estimation of the Evacuation Time According to Different Flood Depths," Sustainability, MDPI, vol. 15(7), pages 1-23, April.
    5. van Lint, J.W.C. & Calvert, S.C., 2018. "A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modelling driver distraction," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 63-86.
    6. Babak Javani & Abbas Babazadeh, 2020. "Path-Based Dynamic User Equilibrium Model with Applications to Strategic Transportation Planning," Networks and Spatial Economics, Springer, vol. 20(2), pages 329-366, June.

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