Quantification and management of urban traffic emissions based on individual vehicle data

Urban traffic pollution poses a serious threat to the environment and human health, especially in urban centres with high population density. Traditional traffic pollution quantification and management methods can be improved based on fine-grained individual vehicle data provided by intelligent transportation systems. Traditional traffic emission quantification and management are often based on simulated or relatively coarse-grained measured data. Such data lack a comprehensive reflection of the actual conditions of all vehicles travelling on roads, which leads to deviations in emission quantification; thus, they cannot support the delicate control policy of traffic pollution. This paper presents a high-resolution individual vehicle emission quantification method based on real-time, real-world individual vehicle data, with a combination of automatic licence plate recognition data and vehicle registration data currently used for traffic management. In this study, we quantified the emissions of each vehicle driving in the urban centre of the case city and analysed regional traffic emission characteristics. We found that there was an apparent uneven distribution of vehicle emissions; that is, the emissions from a small number of high-emission vehicles accounted for a large proportion of the regional traffic emissions. Different pollutants and vehicle types had different emission distribution characteristics. Furthermore, we explored emission reduction policies based on the management of high-emission vehicles identified by individual vehicle data and conduct fine-scale analysis of the link-level hourly emission reduction effects. In addition, a comparison between traditional methods and the method used in this paper for emission quantification was performed. This paper provides a basis for the accurate analysis of regional traffic emission characteristics, individual-based emission reduction policy formulation, and refined policy effect analysis, which has great significance for the control of traffic pollution.

Automated summary

Urban traffic pollution is a serious threat to the environment and human health, and traditional quantification methods can be improved with fine-grained data from intelligent transportation systems, enabling more precise control policies to be implemented.