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.
Highlights
People start and end most of their trips on foot in their daily lives
Some researchers employed traditional planar kernel density estimation (KDE) [16,17,18] that estimates density in two-dimensional space where traffic collisions are weighted based on the Euclidean distance, there has been a growing trend in applying network KDE (NKDE), which estimates density in a one-dimensional space where distance is calculated along the road network mainly because traffic collisions are a network-constrained phenomenon
The results indicate that the NKDE is more appropriate than standard planar KDE for density estimation of traffic collisions, since the latter is likely to overestimate the density values
Summary
People start and end most of their trips on foot in their daily lives. mainly due to the lack of awareness, pedestrians are often at high risk for death and injury. A preliminary step to improve pedestrian safety is to identify hazardous road locations for pedestrians. Kernel density estimation (KDE) is one of the most popular event-based approaches [15]. Some researchers employed traditional planar KDE [16,17,18] that estimates density in two-dimensional space where traffic collisions are weighted based on the Euclidean distance, there has been a growing trend in applying network KDE (NKDE), which estimates density in a one-dimensional space where distance is calculated along the road network mainly because traffic collisions are a network-constrained phenomenon. Xie and Yan [5] developed a novel NKDE approach to estimate the density of network-constrained point events and applied it to the analysis of 2005 traffic crash data in the Bowling Green, Kentucky, USA area. The results indicate that the NKDE is more appropriate than standard planar KDE for density estimation of traffic collisions, since the latter is likely to overestimate the density values
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
