A novel self-adaption macroscopic fundamental diagram considering network heterogeneity

Wenbin Yao, Nuo Chen, Hongyang Su, Youwei Hu, Sheng Jin and Donglei Rong

Physica A: Statistical Mechanics and its Applications, 2023, vol. 613, issue C

Abstract: The spatial homogeneity is the basic assumption of macroscopic fundamental diagram (MFD) analysis, however, this assumption cannot be satisfied in practice. The current solutions to this problem include the network partition and using the function form of MFD that considers heterogeneity of network, while both solutions have their limitations. This paper proposes a self-adaption macroscopic fundamental diagram (SAMFD) considering network heterogeneity, which optimizes the effective road section length coefficient based on differential evolution algorithm. The effectiveness of the SAMFD is verified on the simulation data and real world datasets. The method proposed in this paper provides a new idea to solve the network heterogeneity problem when constructing MFD, and this method can be combined with the network partitioning method to better solve the heterogeneity problem.

Keywords: Macroscopic fundamental diagram; Heterogeneity; Traffic state; Differential evolution algorithm; Network partition; Time stage division (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:613:y:2023:i:c:s0378437123000869

DOI: 10.1016/j.physa.2023.128531

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