GINSER: Geographic Information System Based Optimal Route Recommendation via Optimized Faster R-CNN

Abstract

Traffic congestion and accident-prone zones present significant challenges to urban transportation by causing delays, pollution, and safety hazards. However, the existing techniques do not provide real-time recommendations for minimizing congestion leaving travelers and planners with suboptimal solutions. These systems often fail to integrate accident-prone zone detection with traffic congestion management and cannot provide a real time congestion-free route. This research aims to address these challenges by developing a geographic information system (GIS)-based optimal route recommendation model GINSER using advanced deep learning techniques. The primary objectives are to detect accident-prone zones, classify traffic congestion levels, and recommend efficient routes using GIS. The proposed GINSER model utilized CCTV camera as an input image are preprocessed using an adaptive Gaussian bilateral filter (AGBF) to remove noise and enhance image quality. Faster R-CNN is used for identifying and localizing objects in accident-prone areas. Particle swarm optimization (PSO) is used to hyperparameter tuning for improving an accuracy. A CNN-BiGRU model is utilized to classify traffic congestion levels into low, moderate, high, and congestion-free categories. GIS analyzes spatial data and traffic patterns to recommend the most efficient and congestion-free routes. The effectiveness of the proposed GINSER approach was assessed utilizing F1 score, accuracy, precision, recall, and specificity. The noise-free images using AGBF effectively enhances image quality by reducing noise leading to improved classification accuracy. PSO is utilized for hyperparameter tuning achieving a high accuracy of 95.24%. The GINSER model achieved a classification accuracy of 99.16%. The GINSER improved overall accuracy by 3.90%, 6.71%, 4.13%, and 0.70% better than TSANet, TCEVis, Ising-traffic, and AID, respectively. The proposed GINSER model offers a novel solution to urban transportation challenges by integrating deep learning and GIS technologies. Its ability to detect accident-prone zones classify congestion levels and recommend optimal routes ensures safer and more efficient mobility.

Topics & Concepts