Trajectory-based vehicle emission evaluation for signalized intersection using roadside LiDAR data

Signalized intersections are the bottleneck of urban traffic network and often lead to traffic congestion and increased traffic emissions. Studying and analyzing the spatiotemporal emission patterns at intersections is the prerequisite for traffic emission reduction. This study develops a novel framework for vehicle emission estimation using high-resolution trajectory data based on the roadside light detection and ranging sensor. A meshing method was proposed to divide the area and the trajectory repair model was developed by considering multiple influenced factors to obtain refined LiDAR trajectory data. Then, the Virginia Tech microscopic model was used to perform traffic emission spatiotemporal analysis. In the experiments, trajectory repair results indicate that Root Mean Square Error and Mean Absolute Error are 1.1299m and 0.7816m within 3s, which has excellent prediction accuracy and provides a reliable data basis for accurately estimating emissions. Emission results show that vehicle speed and acceleration are positively correlated with emissions, with the highest emissions generated during acceleration, of which CO could reach 0.0037 g/s. From a temporal perspective, emissions gradually decrease during red light phases and increase when vehicles accelerate during green light phases, suggesting recommendations for signal optimization for areas where emissions exceed standards to reduce vehicle's start and stop. From a spatial perspective, emission rates are highest in the downstream area, with CO reaching more than 0.0043 g/s. It can be suggested that relevant authorities could install speed limit signs or plant trees in this area. Overall, these findings have the potential to alleviate emission pressures at intersections.