Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China

Shenjun Yao, Jinzi Wang, Lei Fang and Jianping Wu
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Shenjun Yao: 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
Lei Fang: Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Jianping Wu: Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China

Sustainability, 2018, vol. 10, issue 12, 1-11

Abstract: The improvement of pedestrian safety plays a crucial role in developing a safe and friendly walking environments, which can contribute to urban sustainability. A preliminary step in improving pedestrian safety is to identify hazardous road locations for pedestrians. This study proposes a framework for the identification of vehicle-pedestrian collision hot spots by integrating the information about both the likelihood of the occurrence of vehicle-pedestrian collisions and the potential for the reduction in vehicle-pedestrian crashes. First, a vehicle-pedestrian collision density surface was produced via network kernel density estimation. By assigning a threshold value, possible vehicle-pedestrian hot spots were identified. To obtain the potential for vehicle-pedestrian collision reduction, random forests was employed to model the density with a set of variables describing vehicle and pedestrian flows. The potential for crash reduction was then measured as the difference between the observed vehicle-pedestrian crash density and the prediction produced by the random forests models. The final hotspots were determined by excluding those with a crash reduction value of no more than zero. The method was applied to the identification of hazardous road locations for pedestrians in a district in Shanghai, China. The result indicates that the method is useful for decision-making support.

Keywords: kernel density; random forests; pedestrians; crash; hotspots; safety; walking (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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