A Novel Macroscopic Traffic Model based on Distance Headway

Abstract

A new model is proposed to characterize changes in traffic at transitions. These changes are affected by driver response. The distance headway between vehicles is considered as it affects driver behavior. Driver response is quick with a small distance headway and slow when the distance headway is large. The variations in traffic are greater with a slow driver while traffic is smooth with a quick driver. A model is developed which characterizes traffic based on driver response and distance headway. This model is compared with the well-known and widely employed Zhang and PW models. The Zhang model characterizes driver response at transitions using an equilibrium velocity distribution and ignores distance headway and driver response. Traffic flow in the PW model is characterized using only a velocity constant. Roe decomposition is employed to evaluate the Zhang, PW, and proposed models over a 270 m circular (ring) road. Results are presented which show that Zhang model provides unrealistic results. The corresponding behavior with the proposed model has large variations in flow with a slow driver but is smooth with a quick driver. The PW model provides smooth changes in flow according to the velocity constant, but the behavior is unrealistic because it is not based on traffic physics. Doi: 10.28991/CEJ-SP2021-07-03 Full Text: PDF

Vehicular Flow Characterization: An Internet of Video Things-Based Solution

“…These parameters provide insight into local traffic flow behaviour (such as speed vs flow, density vs speed, and density vs flow, to name a few). Furthermore, these parameters are imperative for calibrating and validating mathematical traffic flow models and simulation software for better road network design and management [7,8,9,10,11]. In the existing literature, varying solutions have been proposed for vehicular flow characterization and are categorized as either intrusive or non-intrusive sensors [12,13].…”

A Microscopic Traffic Model Considering Driver Reaction and Sensitivity

“…This indicates that the traffic flow with the proposed model is based on driver reaction and sensitivity. A smaller distance headway results in a faster driver response as there are significant interactions between vehicles, i.e., during congestion [9], so the flow is low. Conversely, with a large distance headway, driver response is slow and there are few interactions between vehicles so the flow is high [9].…”

“…A smaller distance headway results in a faster driver response as there are significant interactions between vehicles, i.e., during congestion [9], so the flow is low. Conversely, with a large distance headway, driver response is slow and there are few interactions between vehicles so the flow is high [9]. Furthermore, a higher sensitivity results in larger changes in traffic flow.…”

“…Time headway, τ, is the time required to adjust to traffic conditions ahead, and the distance headway is traversed during τ [8]. A driver responds slowly with a large distance headway and quickly with a small distance headway [9]. Furthermore, driver response is affected by sensitivity, which is proportional to the change in velocity [9].…”

“…A driver responds slowly with a large distance headway and quickly with a small distance headway [9]. Furthermore, driver response is affected by sensitivity, which is proportional to the change in velocity [9]. Since driver response is not the same for all traffic conditions, it should be considered in a model for accurate and realistic traffic characterization.…”