Accident prediction in mesoscopic view: A CPSS-based social transportation approach

Abstract

In modern society, traffic accidents are becoming an essential social safety issue that cannot be ignored. Along with the convenience of the high-speed development of modernization, the prosperity of vehicles has also led to severe traffic accidents, which cause massive loss of lives and properties every year. Accident analysis and prediction based on big data have become increasingly important in social transportation. However, the traditional data analysis and prediction methods only focused on either macroscope(transportation) or microscope(vehicle), and the relationships between the two layers are missing. This paper proposes a novel analysis framework by introducing the mesoscopic factors to bridge the transportation and vehicle operation domain under the parallel driving setting. The feature aggregation and mesoscopic factors extraction are included in the proposed prediction strategy to improve the performance of various commonly used decision tree-based classifiers. Methods are compared under the most recent 5-year accident database, which stores over three million records. Insights are discussed based on the future application of connected autonomous vehicles and intelligent road infrastructures.