Identification of risky locations based on the CAS device-based alerts generated from moving vehicles: A Case study

Abstract

Abstract Increase in traffic accidents is one of the major concerns that affects economy of the country. To reduce traffic crashes, an advanced artificial intelligence technology for road safety known as ADAS (Advanced Driver Assistance System) is being adopted. Advanced driver assistance system is one of the groups of electronic technologies that assist drivers in driving and parking functions by giving an alert to drivers for safer decisions. The present study attempts to apply the CAS (Collision Avoidance System) in the city buses of Telangana state. The data generated from CAS device includes alert type like LDW (Lane Diversion Warning), HMW (Headway Monitoring Warning), FCW (Forward Collision Warning), PCW (Pedestrian Collision Warning) with location, date, time of the generated alert, and speed. The study uses the Geographical Information System-based platform for advanced level analysis. Total 190 km long road network is being used in this study. Alert data have been analysed to identify the risky locations and compared with the existing blackspots. These blackspots have been identified based on traditional methods of using FIR records. Results shows that higher number of alerts are generated at Kukatpally metro station. After obtaining unsafe locations through CAS analysis, the locations are then validated with existing blackspot locations.