A New Driver Model Based on Driver Response

Abstract

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of vehicles for s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent.

The effect of visibility on road traffic during foggy weather conditions

“…Later, Bando et al [23] developed the optimal velocity model (OVM) that has a constant driver response regardless of traffic conditions. Thus, it does not consider the effects of velocity differences and results in unstable behaviour [5].…”

“…Helbing and Tilch [24] introduced a model that takes into account the response to velocity differences and accurately characterizes the time headway and velocity during congestion. However, it neglects the behaviour of slow and average drivers, as acceleration occurs over a short time, which is typical of aggressive drivers [5]. In contrast, Gipps [25] proposed a model that considers driver response to forward traffic with realistic acceleration.…”

“…Macroscopic models, on the other hand, consider aggregate traffic behaviour and are used to determine average density, velocity, and flow [16]. Mesoscopic models combine both individual and collective vehicle behaviour to characterize vehicles both individually and as a group [5,17].…”

“…When fog forms on the surface, it appears as a cloud that reduces visibility and can lead to accidents on roads that are typically safe [1]. Reduced visibility increases the risk of rear‐end crashes and multiple vehicle collisions [4], and results in long traffic queues that take more time to dissipate [5]. It is crucial that drivers be aware of weather conditions and adjust their driving behaviour accordingly.…”

Accumulating Distrust: A Dynamic Model of Automation Disuse

“…is the minimum spacing at standstill, is the spacing between the lead and following vehicle, is the desired time headway, is the relative velocity between the lead and following vehicle and is the maximum desired deceleration. is the acceleration exponent and influences the vehicle's response based on the ratio between the vehicle's current velocity and the desired velocity (Ali et al, 2022). ), or maintain a safe distance from the lead vehicle ( ) (Ciuffo et al, 2012;Vasconcelos et al, 2014).…”