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An improvement in MATSim computing time for large-scale travel behaviour microsimulation

Author

Listed:
  • Chengxiang Zhuge

    (University of Cambridge
    University of East Anglia
    The Hong Kong Polytechnic University)

  • Mike Bithell

    (University of Cambridge)

  • Chunfu Shao

    (Beijing Jiaotong University)

  • Xia Li

    (Beijing Institute of Technology)

  • Jian Gao

    (Beijing Jiaotong University)

Abstract

Coupling activity-based models with dynamic traffic assignment appears to form a promising approach to investigating travel demand. However, such an integrated framework is generally time-consuming, especially for large-scale scenarios. This paper attempts to improve the performance of these kinds of integrated frameworks through some simple adjustments using MATSim as an example. We focus on two specific areas of the model—replanning and time stepping. In the first case we adjust the scoring system for agents to use in assessing their travel plans to include only agents with low plan scores, rather than selecting agents at random, as is the case in the current model. Secondly, we vary the model time step to account for network loading in the execution module of MATSim. The city of Baoding, China is used as a case study. The performance of the proposed methods was assessed through comparison between the improved and original MATSim, calibrated using Cadyts. The results suggest that the first solution can significantly decrease the computing time at the cost of slight increase of model error, but the second solution makes the improved MATSim outperform the original one, both in terms of computing time and model accuracy; Integrating all new proposed methods takes still less computing time and obtains relatively accurate outcomes, compared with those only incorporating one new method.

Suggested Citation

  • Chengxiang Zhuge & Mike Bithell & Chunfu Shao & Xia Li & Jian Gao, 2021. "An improvement in MATSim computing time for large-scale travel behaviour microsimulation," Transportation, Springer, vol. 48(1), pages 193-214, February.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:1:d:10.1007_s11116-019-10048-0
    DOI: 10.1007/s11116-019-10048-0
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    References listed on IDEAS

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    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    2. Gunnar Flötteröd & Yu Chen & Kai Nagel, 2012. "Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation," Networks and Spatial Economics, Springer, vol. 12(4), pages 481-502, December.
    3. Chengxiang Zhuge & Chunfu Shao & Jian Gao & Meng Meng & Weiyang Xu, 2014. "An Initial Implementation of Multiagent Simulation of Travel Behavior for a Medium-Sized City in China," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, March.
    4. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
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