Macroscopic Traffic Flow Characterization for Stimuli Based on Driver Reaction

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

A Novel Macroscopic Traffic Model based on Distance Headway

“…The spatial and temporal characterization of traffic should be based on parameters such as driver response [2,5]. Real observations have been used to determine driver response to traffic conditions [25].…”

“…The macroscopic model created by Lighthill, Whitham and Richards (LWR) is based on small changes in density which are characterized by variations in traffic velocity [4]. This simple model is commonly employed for traffic modeling [1,5], but it ignores traffic alignment at transitions as well as driver response. Further, the traffic flow is symmetric when the capacity is 0.5 which is not realistic [6], and driver time to adjust to changes in traffic is ignored [7,8].…”

A Macroscopic Traffic Model Based on the Safe Velocity at Transitions

“…Then, the performance of the model is evaluated numerically. These results are used to modify the model as required to obtain an acceptable traffic characterization [18]. The LWR and proposed traffic models are presented below.…”

“…Further, the velocity during alignment to forward conditions should be included [17]. Driver reaction has been considered to provide more realistic behavior than existing models [18,19]. Further, realistic parameters were used to better characterize traffic [20].…”

Prohibitory traffic signs violation by well-educated young drivers based on situation scenarios

“…An investigation of road users' situation awareness in different road environments such as wet, slippery, debris, quiet and busy roads found that it can influence driver's awareness (Salmon et al, 2014). In addition, Imran et al (2021) observed that traffic flow characterization and driver reaction were related. Another opinion stated that a tired or fatigue driver has its potential to ignore traffic rules (Ho et al, 2015;Ho & Widaningrum, 2016).…”