Autonomous Driving-Based Traffic Carbon Emission Monitoring and Evaluation Model

With the rapid development of the automotive industry and the continuous growth of motor vehicle ownership, transportation has become a major component of urban pollution. In order to reduce vehicle emission pollution and monitor traffic carbon emissions, this study proposes a vehicle emission model based on the fuel carbon balance principle, using autonomous driving vehicles as monitoring vehicles. Key performance indicators (KPI), such as trip-specific emissions, average fuel consumption per trip (EPI), and energy consumption per trip (API), are introduced. A comprehensive assessment model for vehicle trips and road network emissions is constructed, enabling comparability of energy demands, braking, and acceleration characteristics among all vehicles in the road network. The vehicle mobile emission tracking system, based on autonomous driving vehicles, not only tracks and monitors individual vehicles but also dynamically scans and monitors emissions from urban road traffic. Quantitative and refined accounting of urban transportation emissions through road network research can assess the environmental impact of transportation policies, serving as an important basis for evaluating policy implementation and effectiveness.