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Estimation of vehicular emissions by capturing traffic variations
Institution:1. MOE Key Laboratory for Urban Transportation Complex Systems, Theory and Technology, Beijing Jiaotong University, 3 Shangyuan Cun, Haidian District, Beijing 100044, PR China;2. Chongqing Transport Planning Institute, 18 Yanghe 2nd Cun, Jiangbei District, Chongqing 400020, P. R. China;3. College of Science and Technology, Texas Southern University, 3100 Cleburne Avenue, Houston, TX 77004, United States;1. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;2. Environmental Science Research & Design Institute of Zhejiang Province, Hangzhou 310007, China;3. Unit 94865 of PLA, Hangzhou 310021, China
Abstract:Increase in traffic volumes and changes in travel-related characteristics increase vehicular emissions significantly. It is difficult, however, to accurately estimate emissions with current practice because of the reliance on travel forecasting models that are based on steady state hourly averages and, thus, are incapable of capturing the effects of traffic variations in the transportation network. This paper proposes an intermediate model component that can provide better estimates of link speeds by considering a set of Emission Specific Characteristics (ESC) for each link. The intermediate model is developed using multiple linear regression; it is then calibrated, validated, and evaluated using a microscopic traffic simulation model. The improved link speed data can then be used to provide better estimates of emissions. The evaluation results show that the proposed emission estimation method performs better than current practice and is capable of estimating time-dependent emissions if traffic sensor data are available as model input.
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