Rethinking Traffic Management with Congestion Pricing and Vehicular Routing for Sustainable and Clean Transport

Rapid growth in vehicular congestion increases the challenges of traffic management concerning pollution and infrastructure. Efficient traffic governance can have a significant impact on a country’s economy. To alleviate these challenges, we propose an intelligent integrated traffic management system that manages congestion through cost pricing models to achieve smooth traffic flow. We propose a novel rerouting algorithm and ensemble architecture for vehicle detection and classification, tested on live traffic captured in several Indian cities. The ensemble architectures are designed on a combination of existing pre-trained models. Choice of the ensembles is based on accuracy, model interpretability, and energy efficiency. We show that the second-best ensemble produced operates with significantly less energy and better explainability than our best performer and is still within 3% accuracy of the best performer. Based on predefined road priorities, these ensemble models provide traffic and individual vehicle counts, further fed to our proposed rerouting algorithm as input. The rerouting algorithm then recommends alternative routes and estimated journey time to the user. The paper also presents the results obtained by testing the models on real-time traffic videos from Aurangabad (India) on a GPU/CPU cluster consisting of machines incorporating different GPU hardware.