Understanding space-time patterns of vehicular emission flows in urban areas using geospatial technique |
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| Affiliation: | 1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 181 Chatham Road South, Kowloon, Hong Kong;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China;3. Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Hong Kong;4. Senseable City Laboratory, Singapore-MIT Alliance for Research and Technology, Singapore;1. College of Geography and Environment, Shandong Normal University, Jinan, Shandong 250300, China;2. School of Geography, South China Normal University, Guangzhou, Guangdong 510631, China;1. School of Transportation and Logistics, East China Jiaotong University, Nanchang, China;2. Tsinghua University Suzhou Automotive Research Institute, Suzhou 215134, Jiangsu, China;3. Department of Mechanical Engineering, School of Engineering, Aalto University, Otakaari 4, 02150, Koneteknikka 1, Espoo, Finland;4. Faculty of Mechanical Engineering, Opole University of Technology, Opole 45758, Poland;5. JSTI Nanjing Design Center, Nanjing, China |
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| Abstract: | Traffic-related emissions are well-known factors in urban environment which may have adverse implication on human health. Estimating vehicular emissions in urban areas provides an understanding of the air pollution caused by traffic. However, existing microscopic approaches cannot simulate the traffic flows and emissions for an entire city and most of the macroscopic approaches are usually highly complex and require priori knowledge about vehicles' route options. This study, therefore, proposes a straightforward and robust approach to simulate vehicular flows and estimated transport emissions at a city scale via a deterministic approach and by applying the Cell Transmission Model (CTM) to simplify the modeling of vehicles' route selections. Under a space-time integrated framework, we firstly simulate a time-dependent distribution of urban vehicular flows and then estimate pollutant emissions of Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Violate Organic Compounds (VOC) for traffic flows on weekday and weekend. Finally, the spatiotemporal patterns of traffic flows as well as traffic emissions were visualized and illustrated under a space-time integrated framework. With accuracies of around 67.4% to 70%, the results demonstrated the feasibility of the proposed approach for estimating city-scale traffic flows and emissions from road transport. |
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