Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

Waheed Imran,T A Gulliver,M Sagheer Aslam,Khurram S Khattak,Zawar H Khan,Salman Saeed

doi:10.28991/cej-2021-03091632

Waheed Imran, T A Gulliver + Show 4 more

Open Access

https://doi.org/10.28991/cej-2021-03091632

Copy DOI

Abstract

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

Highlights

Traffic models are employed to predict vehicle behavior and are essential to the design of effective control strategies [1]
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
With M = 1.5, the density is 0.0021 at 49 m, increases to 0.17 at 55 m, and at 190 m and 200 m it is 0.031 and 0.14, respectively. These results show that a small traffic stimulus impedes density evolution, so it is slowest with M = 0.5

Summary

Introduction

Traffic models are employed to predict vehicle behavior and are essential to the design of effective control strategies [1]. Lighthill and Whitham and Richards proposed a first-order macroscopic traffic flow model which is called the The Lighthill-Witham-Richards (LWR) model [13, 14] It is based on vehicle conservation on a highway which can be characterized by temporal changes in traffic density and spatial changes in flow. The LWR model was modified in [26] to consider large traffic velocity changes at bottlenecks Another variation considers driver response as quick or sluggish in non lane discipline traffic [45]. Traffic aligns to spatial changes in density based on the forward and lateral distances headways between vehicles.

Traffic Flow Modeling

Performance Results

Conclusion