Abstract
The introduction of vehicular ad-hoc networks (VANETs) leads to the possibility to re-evaluate many traditional functions and views of road traffic networks. The ability for vehicles and infrastructure to communicate and collaborate will enable many novel solutions for problems as diverse as collision avoidance and traffic management with the view of reducing traffic congestion, increasing the effectiveness of logistics systems etc. In this paper we introduce a novel framework that utilises VANET information to share information about risk factors among road occupants and infrastructure. We introduce the concept of risk limits as a means of traffic accident risk mitigation, whereby vehicles need to adjust their behaviour to maintain a given level of risk. We discuss determination of risk values and detail this process using the NSW traffic accident database. We show how the effects on risk of particular vehicular behaviours such as speed and headway can be calculated and use these results to modify vehicle behaviour in real time to maintain a predefined risk limit. Experiments are carried out using the Paramics Microsimulator. Our results show that it is possible to reduce the accident rate among vehicles while at the same time increasing road network throughput by exploiting the variation in risk between vehicles.
Highlights
With the advent of vehicular ad-hoc networks (VANETs), vehicles are able to share information and respond in real-time, leading to the ability for traffic management systems to be more dynamic, rapidly responding to current circumstances, and to be tailored to individual vehicles
Vehicles would be able to behave in a way that optimises the utility of the road network—both in terms of the speed of individual vehicles and overall network throughput—as long as their risk does not increase above the set threshold
We propose a framework for managing risk levels with the aim of maximising the road network throughput
Summary
With the advent of vehicular ad-hoc networks (VANETs), vehicles are able to share information and respond in real-time, leading to the ability for traffic management systems to be more dynamic, rapidly responding to current circumstances, and to be tailored to individual vehicles. Vehicle and situation has its own constellation of risk factors that may be rapidly changing and the range of risk levels experienced either by a single vehicle and driver over time or between different vehicles and drivers is significant These differences can be exploited to obtain the maximum benefits to network utility while still maintaining an acceptable level of accident risk. We show through an experimental study that it is possible to achieve both lowered accident levels and increased throughput simultaneously by exploiting the variation in risk between situations, dynamically striking a balance between risk and utilisation
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