Publication Type : Journal Article
Publisher : Elsevier
Source : Procedia Computer Science
Url : https://doi.org/10.1016/j.procs.2024.04.217
Campus : Coimbatore
School : School of Computing
Year : 2024
Abstract : Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly.
Cite this Research Publication : Gannina, Aswin Ram Kumar, Aadhil Ahamed Jaffarullah, Tiyyagura Mohit Reddy, Sabbella Manoj Subba Reddy, Ambati Sai Vikas, Senthilkumar Mathi, and Venkadeshan Ramalingam. "A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation." Procedia Computer Science 235 (2024): 2288-2296.