Macroscopic traffic characterization based on driver memory and traffic stimuli

Date

2023

Authors

Khan, Zawar H.
Imran, Waheed
Gulliver, Thomas Aaron
Khattak, Khurram S.
Din, Ghayas Ud
Minallah, Nasru
Khan, Mushtaq A.

Journal Title

Journal ISSN

Volume Title

Publisher

Transportation Engineering

Abstract

A new macroscopic traffic flow model is proposed which incorporates traffic alignment behavior at transitions. In this model, velocity is a function of the distance headway and driver response time. It can be used to characterize the traffic flow for both uniform and non uniform headways. The well-known Zhang model characterizes this flow based on driver memory which can produce unrealistic results. The performance of the proposed Khan-Imran-Gulliver (KIG) and Zhang models is evaluated for an inactive bottleneck on a 2000 m circular road. The results obtained show that the traffic behavior with the KIG model is more realistic.

Description

Keywords

Zhang model, macroscopic traffic flow, driver reaction, distance headway, driver memory, flow stability, numerical stability

Citation

Khan, Z. H., Imran, W., Gulliver, T. A., Khattak, K. S., Din, G. U., Minallah, N., & Khan, M. A. (2023). Macroscopic traffic characterization based on driver memory and traffic stimuli. Transportation Engineering, 14, 100208. https://doi.org/10.1016/j.treng.2023.100208