A Multilevel Traffic Incidents Detection Approach: Identifying Traffic Patterns and Vehicle Behaviours using real-time GPS data

Abstract

This paper presents a multilevel approach for detecting traffic incidents causing congestion on major roads. It incorporates algorithms to detect unusual traffic patterns and vehicle behaviours on different road segments by utilising the real-time GPS data obtained from vehicles. The incident detection process involves two phases: (1) Identifies of road segments where abnormal traffic pattern is observed and further divides the 'abnormal segments' into smaller segments in order to isolate the potential incident area; (2) Performs a hierarchical analysis of the vehicles' GPS data, using predefined rules to detect any occurrence of abnormal behaviour within the 'abnormal' road section identified in phase 1. The strength of such approach lays in isolating road segments sequentially and then analysing vehicle data specific to the identified road segment. In this way, the processing of vast data is avoided which is an essential requirement for the better performance of such complex systems. The approach is demonstrated using a simulation of motorway segments near Coventry, UK.