Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity |
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| Institution: | 1. University of California, Los Angeles, Department of Atmospheric and Oceanic Sciences, 405 Hilgard Ave., Los Angeles, CA 90095, USA;2. University of California, Los Angeles, Institute of the Environment and Sustainability, La Kretz Hall, Suite 300, Los Angeles, CA 90095, USA;3. University of California, Los Angeles, Luskin Center for Innovation, Luskin School of Public Affairs, 3250 Public Affairs Bldg., Los Angeles, CA 90095, USA;4. Center for Environmental Sciences, Faculty of Sciences, University of Chile, Las Palmeras 3425 Ñuñoa, Santiago, Chile;5. University of California, Los Angeles, Fielding School of Public Health, Environmental Health Sciences Department, 650 Charles Young Dr., Los Angeles, CA 90095, USA;1. International Center for Adaptation Planning and Design, School of Landscape Architecture and Planning, College of Design, Construction and Planning, University of Florida, PO Box 115706, Gainesville, USA;2. China Institute of Urban Governance, Shanghai Jiao Tong University, Shanghai, China;1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China;3. School of Transportation, Southeast University, Nanjing 211189, China;4. School of Automobile, Chang''an University, Xi''an 710064, China;1. Université Paris 8, LADYSS, UMR 7533 CNRS, France;2. Equipe de Recherche en Epidémiologie Nutritionnelle, U1153 Inserm, INRA, CNAM, Université Paris 13, Sorbonne Paris Cité, Centre de Recherche en Epidémiologie et Biostatistiques, CRNH IdF, Bobigny, France;3. Université Paris 1, Géographie-Cités, UMR 8504 CNRS, France;4. Université de Strasbourg, LIVE, UMR 7562 CNRS, France;5. Luxembourg Institute of Socio-Economic Research, Esch/Alzette, Luxembourg;6. Institut des Systèmes Complexes de Paris Île-de-France, France;7. CRNH Rhône-Alpes, Lyon University, Laboratoire CarMeN, INSERM U1060, INRA U1235, Université Claude Bernard Lyon 1, INSA-Lyon, France;8. Université Paris Est, Lab Urba, UPEC, Créteil, France;9. Department of Nutrition, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital (AP-HP), Pierre et Marie Curie University — Sorbonne Université, Paris, France |
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| Abstract: | Traffic state in the urban network is a direct reflection of the operational efficiency of the urban transportation system. As the busiest period of the day, traffic states during evening peak hours can effectively measure the capacity and efficiency of the transportation system. The primary objective of this study is to investigate how the potential factors affect traffic states during evening peak hours on weekdays. The geographically weighted regression (GWR) approach was proposed to model the spatial heterogeneity of traffic states and visualize the spatial distributions of parameter estimations. Four types of data including traffic state index (TSI) data, point of interests (POIs) data, road features data, and public transport facilities data were obtained from Shanghai in China to illustrate the procedure. According to the results, the GWR model outperformed the ordinary least square (OLS) model in the explanatory accuracy as well as the goodness of fit. The urban form was revealed to have a significant influence on traffic states and strong local variability for parameter estimations was observed. The number of public and commercial POIs, residential POIs, bus routes, bus stops, the average number of lanes, as well as average traffic volumes can significantly affect the traffic states spatially, and the estimated coefficients of each traffic analysis zone (TAZ) vary across regions. The conclusions of this study may contribute to making the planning and management strategies more efficient for alleviating traffic congestion. |
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