Energy Efficient Traffic Control Network
Objective: Intelligent Traffic Lights Planner to Reduce Vehicle Emissions
Abstract: Vehicles with petrol-driven internal combustion engines are a major source of air pollution, producing gases like CO2, CO. We present a system capable of improving traffic fluency in intersections with the goal of minimizing vehicle emissions.
Solution: We propose the use of a smart traffic signal network at intersections equipped with traffic lights. By prioritizing certain lanes (based on data from traffic images and statistics), waiting time for vehicles will be minimized, and hence the fuel consumption. Additionally, neighbouring traffic signals will also modify its timings based on oncoming traffic from previous intersections. We also present a methodology for evaluating the impact on the reduction of pollution, using modelling and simulation. Traffic data will be analysed using Image Classification models based on Machine Learning. Traffic signals will also adapt to trends in vehicle traffic during peak and non-peak hours.
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