Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data |
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| Affiliation: | 1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing, 210096, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing, 210096, China;3. Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, NWQ4414, P.O. Box 784, Milwaukee, WI 53201, United States;4. Department of Civil Engineering, Auburn University, 238 Harbert Engineering Center, Auburn, AL 36849-5337, United States;1. Guangdong Key Laboratory of Urban Informatics, Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, and Research Institute of Smart Cities, Shenzhen University, Shenzhen 518060, China;2. Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China;3. Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China;4. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong;1. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;2. School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73, Huanghe Road, Nangang District, Harbin, China |
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| Abstract: | Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions. |
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