A Modelling Framework for estimating Road Segment Based On-Board Vehicle Emissions

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Yu, Lin-Jun^1 ; Liu, Ya-Lan^1 ; Peng, Zhong-Ren^2,3 ; Liu, Mengmeng^3 ; Ren, Yu-Huan^1

State Laboratory for Spatial Information Technology, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China^1

State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China^2

Department of Urban and Regional Planning, University of Florida, Gainesville, FL 32611-5706, United States^3

关键词: Analysis frameworks;    Floating car data;    Light-duty vehicle emissions;    Modelling framework;    Pollutant emission;    Road segments;    Speed flow;    Temporal spatial distribution;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012253/pdf
DOI  :  10.1088/1755-1315/17/1/012253

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Traditional traffic emission inventory models aim to provide overall emissions at regional level which cannot meet planners' demand for detailed and accurate traffic emissions information at the road segment level. Therefore, a road segment-based emission model for estimating light duty vehicle emissions is proposed, where floating car technology is used to collect information of traffic condition of roads. The employed analysis framework consists of three major modules: the Average Speed and the Average Acceleration Module (ASAAM), the Traffic Flow Estimation Module (TFEM) and the Traffic Emission Module (TEM). The ASAAM is used to obtain the average speed and the average acceleration of the fleet on each road segment using FCD. The TFEM is designed to estimate the traffic flow of each road segment in a given period, based on the speed-flow relationship and traffic flow spatial distribution. Finally, the TEM estimates emissions from each road segment, based on the results of previous two modules. Hourly on-road light-duty vehicle emissions for each road segment in Shenzhen's traffic network are obtained using this analysis framework. The temporal-spatial distribution patterns of the pollutant emissions of road segments are also summarized. The results show high emission road segments cluster in several important regions in Shenzhen. Also, road segments emit more emissions during rush hours than other periods. The presented case study demonstrates that the proposed approach is feasible and easy-to-use to help planners make informed decisions by providing detailed road segment-based emission information.