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Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China

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  • Ningcheng Wang

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Yufan Liu

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Jinzi Wang

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Xingjian Qian

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Xizhi Zhao

    (Research Center of Government Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China)

  • Jianping Wu

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Bin Wu

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Shenjun Yao

    (Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, China)

  • Lei Fang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China)

Abstract

The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle–pedestrian and vehicle–vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle–pedestrian and vehicle–vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle–pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.

Suggested Citation

  • Ningcheng Wang & Yufan Liu & Jinzi Wang & Xingjian Qian & Xizhi Zhao & Jianping Wu & Bin Wu & Shenjun Yao & Lei Fang, 2019. "Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4739-:d:262429
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    References listed on IDEAS

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    Cited by:

    1. Bo Liu & Desheng Xue & Yiming Tan, 2019. "Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China," Sustainability, MDPI, vol. 11(23), pages 1-26, December.
    2. Muhan Lv & Ningcheng Wang & Shenjun Yao & Jianping Wu & Lei Fang, 2021. "Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    3. Jiangzhou Wu & Qing Zhang & Zhida Li, 2025. "Spatial Coupling Characteristics Between Tourism Point of Interest (POI) and Nighttime Light Data of the Changsha–Zhuzhou–Xiangtan Metropolitan Area, China," Sustainability, MDPI, vol. 17(6), pages 1-19, March.
    4. Hailing Xu & Jianghong Zhu & Zhanqi Wang, 2019. "Exploring the Spatial Pattern of Urban Block Development Based on POI Analysis: A Case Study in Wuhan, China," Sustainability, MDPI, vol. 11(24), pages 1-25, December.

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