Investigations on deep learning approaches for traffic density estimation and optimal path selection

Abstract

The rapid growth of urbanization has led to increased vehicular traffic, necessitating effective traffic management systems. Traditional approaches based on manual intervention and rule based systems are proving inadequate to handle the complexity and scale of modern traffic scenarios. This research addresses these challenges by proposing novel methods that enhance the capabilities of deep Convolutional Neural Networks (CNNs) and advanced optimization algorithms. Furthermore, this thesis presents a comprehensive exploration of intelligent techniques for vehicular traffic analysis, counting, flow estimation, and prediction using deep learning methodologies. newlineThe first proposed method presents an Intelligent Traffic Analysis and Prediction System using an improved Single Shot Multibox Detector (SSD) approach. Using the power of deep learning, the proposed method overcomes limitations in traditional SSD approaches by incorporating interactive multi-scale attention mechanisms. The architecture employs ResNet50 as a backbone network, enabling accurate detection and classification of vehicles, especially small ones. The attention block enhances feature focus and representation, while parallel detection and shared multi scale layers improves the detection efficiency for vehicles of various sizes. The system further estimates traffic density in various environmental conditions, contributing to effective traffic prediction in terms of accuracy. newline