GREE-COCO: Green artificial intelligence powered cost pricing models for congestion control

The objective of the proposed research is to design a system called Green Artificial Intelligence Powered Cost Pricing Models for Congestion Control (GREE-COCO) for road vehicles that address the issue of congestion control through the concept of cost pricing. The motivation is to facilitate smooth traffic flow among densely congested roads by incorporating static and dynamic cost pricing models. The other objective behind the study is to reduce pollution and fuel consumption and encourage people towards positive usage of the public transport system (e.g., bus, train, metro, and tram). The system will be implemented by charging the vehicles driven on a particular congested road during a specific time. The pricing will differ according to the location, type of vehicle, and vehicle count. The cost pricing model incorporates an incentive approach for rewarding the usage of electric/non-fuel vehicles. The system will be tested with analytics gathered from cameras installed for testing purposes in some of the Indian and Irish cities. One of the challenges that will be addressed is to develop sustainable and energy-efficient Artificial Intelligence (AI) models that use less power consumption which results in low carbon emission. The GREE-COCO model consists of three modules: vehicle detection and classification, license plate recognition, and cost pricing model. The AI models for vehicle detection and classification are implemented with You Only Look Once (YOLO) v3, Faster-Region based Convolutional Neural Network (F-RCNN), and Mask-Region based Convolutional Neural Network (Mask RCNN). The selection of the best model depends upon their performance concerning accuracy and energy efficiency. The dynamic cost pricing model is tested with both the Support Vector Machine (SVM) classifier and the Generalised Linear Regression Model (GLM). The experiments are carried out on a custom-made video dataset of 103 videos of different time duration. The initial results obtained from the experimental study indicate that YOLOv3 is best suited for the system as it has the highest accuracy and is more energy-efficient.