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