Factor Recognition of Regional Serious Pedestrian-vehicle Crash Using Big Data for Intelligent Vehicles

Abstract

Pedestrian safety is one of the research focuses all over the world. Intelligent decision-making makes it possible to provide dangerous risk prediction. This paper aims to serve as a stepping stone for avoiding serious fatal vehicle - pedestrian crash. It provides a method for intelligent vehicles to identify the factors. Business and education Point of Information (POI) data in Beijing were collected and processed to partition traffic zones into high economic zones and low economic zones used the method of k-means clustering algorithm. Then a binary logistic regression was utilized for recognition of contributing factors. The result takes several important factors into account in low economic zones needed special attention, such as fourth class road and general city road. As a result, the findings of this study could assist to design the hardware module and programming of intelligent vehicle to enable pedestrian safety be improved over the long term.