Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data |
| |
| Institution: | 1. State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai 200240, China;2. China Institute of Urban Governance, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai 200240, China;3. Shanghai Urban Construction Design & Research Institute (Group) Co., Ltd, No. 3447 Dongfang Road, Pu-Dong New District, Shanghai 200215, China;4. Department of Urban and Regional Planning, University of Hawaii, Manoa, No. 2424 Maile Way, Honolulu, HI 96822, United States;1. SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology(SUSTech), Shenzhen, China;2. Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan;3. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai, 201804, PR China;4. College of Computer Science and Technology, Qingdao University, Ningxia Road No. 308, Qingdao, 266071, PR China;5. Institute of Smart City and Big Data Technology, Qingdao, Ningxia Road No. 308, Qingdao, 266071, PR China;1. Laboratory of Applied Thermodynamics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;2. CNH Industrial, Via Puglia 35, 10156 Torino, Italy;1. CDM Smith Inc., Transportation Services Group, 9430 Research Blvd., Suite 1-200, Austin, TX, 78759, USA;2. Indian Institute of Technology Delhi, Department of Civil Engineering, Hauz Khas, New Delhi, 110 016, India;3. University of Minnesota, Department of Civil, Environmental, and Geo- Engineering, 500 Pillsbury Drive S.E., Minneapolis, MN, 55455-0116, USA;1. Tongji University, No. 1239, Siping Road, Shanghai, PR China;2. Shanghai Tongji Urban Planning & Design Institute, No. 1111, North Zhongshan Road, Shanghai, PR China;3. Institute of Environmental Sciences, CML, Leiden University, Einsteinweg 2, 2333 CC Leiden, The Netherlands;4. Center for Social and Environmental Systems Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba-City, Ibaraki 305-8506, Japan;5. Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan;6. Department of Economics, Jinan University, Guangzhou, Guangdong 510632, PR China;7. Institute of Resource, Environment and Sustainable Development, Jinan University, Guangzhou, Guangdong 510632, PR China;8. Centre for Engineering Operations Management, Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark;9. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Department of Traffic Engineering, Tongji University, 4800 Cao''an Road, Shanghai 201804, PR China;10. Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8563, Japan;11. School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang City, PR China;1. School of Engineering, Sun Yat-Sen University, Guangzhou 510275, China;2. Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou 510275, China;3. Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control, Guangzhou 510275, China;4. Institute of Advanced Technology, Sun Yat-Sen University, Guangzhou 510275, China |
| |
| Abstract: | Nowadays, the massive car-hailing data has become a popular source for analyzing traffic operation and road congestion status, which unfortunately has seldom been extended to capture detailed on-road traffic emissions. This study aims to investigate the relationship between road traffic emissions and the related built environment factors, as well as land uses. The Computer Program to Calculate Emissions from Road Transport (COPERT) model from European Environment Agency (EEA) was introduced to estimate the 24-h NOx emission pattern of road segments with the parameters extracted from Didi massive trajectory data. Then, the temporal Fuzzy C-Means (FCM) Clustering was used to classify road segments based on the 24-h emission rates, while Geographical Detector and MORAN’s I were introduced to verify the impact of built environment on line source emissions and the similarity of emissions generated from the nearby road segments. As a result, the spatial autoregressive moving average (SARMA) regression model was incorporated to assess the impact of selected built environment factors on the road segment emission rate based on the probabilistic results from FCM. It was found that short road length, being close to city center, high density of bus stations, more ramps nearby and high proportion of residential or commercial land would substantially increase the emission rate. Finally, the 24-h atmospheric NO2 concentrations were obtained from the environmental monitor stations, to calculate the time variational trend by comparing with the line source traffic emissions, which to some extent explains the contribution of on-road traffic to the overall atmospheric pollution. Result of this study could guide urban planning, so as to avoid transportation related built environment attributes which may contribute to serious atmospheric environment pollutions. |
| |
| Keywords: | On-line car-hailing service Fuzzy C-means clustering Spatial analysis Built environment |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
