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

Ningcheng Wang, Yufan Liu, Jinzi Wang, Xingjian Qian, Xizhi Zhao, Jianping Wu, Bin Wu, Shenjun Yao and Lei Fang
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Ningcheng Wang: Key Laboratory of Geographic Information Science (Ministry of Education), 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
Jinzi Wang: Key Laboratory of Geographic Information Science (Ministry of Education), 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
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
Bin Wu: Key Laboratory of Geographic Information Science (Ministry of Education), 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
Lei Fang: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China

Sustainability, 2019, vol. 11, issue 17, 1-14

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.

Keywords: urban; road safety; nighttime lights; point of interest; collision; random forests; pedestrian (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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