Traffic Accident Prediction Techniques in Vehicular Ad-hoc Network: A Survey

Nowadays, development in transportation is significantly rising. Vehicular Ad Hoc Network (VANET) is an essential part of advanced transportation system framework. In recent years, numbers of vehicles and their uses have increased rapidly due to their high need and demand by people. This reason somewhere also leads to increase in rate of traffic accidents. In today's stressful lifestyle, road accidents are a major threat to human life and harm the safety of today's human beings. Thus it is necessary to provide a prominent solution to this problem. Traffic accident prediction and prevention are important steps for vehicular safety purpose. Various traffic accident prediction techniques using machine learning, deep learning algorithms and fuzzy system are being tested by researcher. In this study, we have focused on using specific methods from available sources. Also, after proper analysis of each of these methods, work has been done to increase the scope of further development and create space for new research.

I. Introduction

Vehicular Ad-hoc Network (VANET) is a special type of wireless networking that is used for intelligent transport systems. VANET consists of cars, base stations and pedestrians, who work to communicate with each other. The main purpose of the Vehicular Ad-hoc Network is to help every passenger get from one place to another safely. The economic development of any country depends on the availability and availability of means of transportation. In the context of speedy evolution of modern transportation, due to big contradiction between human, roads and vehicles, the issues in traffic management are increasingly evident.