Khan, Zawar H.Imran, WaheedGulliver, Thomas AaronKhattak, Khurram S.Din, Ghayas UdMinallah, NasruKhan, Mushtaq A.2024-02-092024-02-0920232023Khan, 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.2023.100208https://doi.org/10.1016/j.treng.2023.100208http://hdl.handle.net/1828/15989A 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.enZhang modelmacroscopic traffic flowdriver reactiondistance headwaydriver memoryflow stabilitynumerical stabilityArticleDepartment of Electrical and Computer Engineering