Advanced Transportation Safety Using Real-Time GIS-Based Alarming System for Animal-Prone Zones and Pothole Areas
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
The transportation system undergoes severe impacts due to potholes and the presence of stray animals on the roads resulting in accidents and fatal injuries. The utilization of intelligent transportation systems would reduce accidents and impart safety to the overall transportation network. This research aims to impart transportation safety through a real-time alert warning system for avoiding accidents due to potholes and the presence of stray animals. The study incorporates real-time detection of transportation entities like vehicles, animals, and pedestrians through a YOLO v3 computer vision algorithm processed on the GPU environment for a higher frame rate. The potholes and animal hotspots are mapped to form a geospatial database on which the buffer tool of geographic information system (GIS) is applied. The buffer zone was implemented on the geospatial layer to alert the driver in real-time, while the vehicle approaches the buffer zone. The system yields high precision of 0.976 mean average precision (mAP) score of entity detection and the real-time alert warning alerts the driver to ensure transportation safety while avoiding any possible accidents or fatal crashes.
Practical Applications
Increasing population density gives rise to traffic congestion, haphazard movement of vehicles, and accident-prone scenarios. This results in the loss of countless lives, which is undesirable. Animals and potholes are the major contributors to accidents, and minimizing them is necessary for maintaining smooth and safe transportation. Advanced driver assistance systems (ADAS) play a crucial role in reducing accidents and increasing safety in the transportation network. This paper presents an integrated system of enhanced ADAS functionalities like the detection of animals and alerting the driver about potential animal hotpots and pothole areas in real-time. The system utilizes deep learning techniques for real-time object detection and GIS-based techniques for warning the drivers of potholes and animal zones in advance. This alert the drivers making them in control of the vehicle for avoiding any probable crash. The system induces safety in the transportation network and opens a strong scope for self-driving cars and connected vehicle infrastructure.
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Data Availability Statement
There was no new data generated in the study.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 8, 2022
Accepted: Nov 8, 2022
Published online: Jan 17, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 17, 2023
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