Detection Algorithms of Intentional Car Following on Smart Networks: A Primary Methodology

Abstract

This paper explores the possibility of detecting certain movements of vehicles that might provide useful information for crime investigations. It is known that existing car following models are interested in microscopic interactions between vehicles in randomly formed pairs. The present work, however, introduces the concept of macroscopic analysis of vehicle positions on a network and the idea of seeking if these movements exhibit any meaningful relationships. First of all detection algorithms are produced for two possible types of detection: (a) was a particular vehicle followed by any vehicle? and (b) did a particular vehicle follow any vehicle? These algorithms assume that every link in the network is equipped with some sort of vehicle identification or tracking device and the identities of all vehicles, such as their number plates, are fed into the program. Then a simulation program is developed to implement the first algorithm (Type (a)), as an example, to visualise the concept. Since the present paper is a preliminary and basic approach to the problem, a number of issues and details requiring further research, together with the directions which could be taken, are also identified and discussed.