Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

Waheed Imran (National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar,)
Zawar H. Khan (Department of Electrical Engineering, University of Engineering and Technology, Peshawar,)
T. A. Gulliver (Department of Electrical and Computer Engineering, University of Victoria, PO Box 1700, STN CSC, Victoria, BC,)
Khurram S. Khattak (Department of Computer System Engineering, University of Engineering and Technology, Peshawar,)
Salman Saeed (National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar,)
M. Sagheer Aslam (National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar,)

Civil Engineering Journal
Vol 7, No 1 (2021): January

The design and management of infrastructure is a significant challenge for traffic engineers and planners. Accurate traffic characterization is necessary for effective infrastructure utilization. Thus, models are required that can characterize a variety of conditions and can be employed for homogeneous, heterogeneous, equilibrium and non-equilibrium traffic. The Lighthill-Whitham-Richards (LWR) model is widely used because of its simplicity. This model characterizes traffic behavior with small changes over a long idealized road and so is inadequate for typical traffic conditions. The extended LWR model considers driver types based on velocity to characterize traffic behavior in non lane discipline traffic but it ignores the stimuli for changes in velocity. In this paper, an improved model is presented which is based on driver reaction to forward traffic stimuli. This reaction occurs over the forward distance headway during which traffic aligns to the current conditions. The performance of the proposed, LWR and extended LWR models is evaluated using the first order upwind scheme (FOUS). The numerical stability of this scheme is guaranteed by employing the Courant, Friedrich and Lewy (CFL) condition. Results are presented which show that the proposed model can characterize both small and large changes in traffic more realistically. Doi: 10.28991/cej-2021-03091632 Full Text: PDF