Research Article
AI-Driven Accident Minimization and Human Safety Enhancement in Transport System
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358088, author={Yedla Dinesh and Aditya Ankana and Syed Subhani and Mandlem Likhitha Mani}, title={AI-Driven Accident Minimization and Human Safety Enhancement in Transport System}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={artificial intelligence road safety accident prediction intelligent transport systems machine learning computer vision real-time monitoring smart cities driver assistance systems}, doi={10.4108/eai.28-4-2025.2358088} }- Yedla Dinesh
Aditya Ankana
Syed Subhani
Mandlem Likhitha Mani
Year: 2025
AI-Driven Accident Minimization and Human Safety Enhancement in Transport System
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358088
Abstract
Road accidents have become a major concern over the years owing to human errors and increasing traffic density [6], [14]. This research presents an AI based real-time predictive system to predict accident risks being faced by law enforcement officers and minimizes accidents [9], [18]. The approach advances smart transportation infrastructure and safety through a fusion of machine learning and vision- based analysis [4], [21]. This procedure works in real-time analysing behavioural and environmental cues, as well as traffic pattern by using complex algorithms to detect the anomalies and threats [3], [20]. Prior to accidents, the system can take proactive responses by providing drivers or autonomous systems with early warnings and corrective suggestions [7], [15]. By integrating with the smart city architectures, the proposed solution helps to make urban transportation safer, faster, and can be readily deployed in diverse urban traffic use-cases [11], [17].
