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

Chengxiang Zhuge (), Mike Bithell (), Chunfu Shao (), Xia Li () and Jian Gao ()
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Chengxiang Zhuge: University of Cambridge
Mike Bithell: University of Cambridge
Chunfu Shao: Beijing Jiaotong University
Xia Li: Beijing Institute of Technology
Jian Gao: Beijing Jiaotong University

Transportation, 2021, vol. 48, issue 1, No 7, 193-214

Abstract: 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.

Keywords: Activity-based model; Dynamic traffic assignment; MATSim; Computing time; Agent-based model; Varying time step-based approach; Large-scale simulation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11116-019-10048-0

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