Increasing the efficiency of vehicle ad-hoc network to enhance the safety status of highways by artificial neural network and fuzzy inference system

Abstract

The future of traffic control and management may manifest itself in the form of digital and automatic methods, which could help to decrease driver errors. One of the most important technologies in this area is the Vehicle Ad-Hoc Network (VANET), in which each vehicle is a node that can receive and transmit information from and to other vehicles. Such systems can be used for safety or nonsafety purposes. This study investigates the safety applications of this system and aims to develop a method to detect and record dangerous situations for each vehicle based on microscopic traffic data. To detect danger, VANET must transmit data between vehicles and traffic safety indicators must be applied. To increase the efficiency of VANET, artificial neural network (ANN) and fuzzy inference system (FIS) were used. This study focuses on rear-end collisions in car-following situations. For this purpose, microscopic traffic data were collected along a 400-meter long segment of the Modares highway in Tehran, Iran. Based on the analysis, applying FIS to develop a new safety index was found to help detect rear-end collisions. Results also indicate that the average mean of errors based on the motion predictions generated by ANN is negligible and that an adequate history of motion for a vehicle diminishes such errors. Therefore, applying artificial intelligence can improve the workability of VANET in detecting a dangerous car-following situation that might lead to a rear-end collision.