Predicting Real-Time Crash Risk for Urban Expressways in China

Miaomiao Liu,Yongsheng Chen

doi:10.1155/2017/6263726

Abstract

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

Highlights

Because of the rapid increase of traffic flow and frequent crash occurrence, traffic safety has become a severe problem for rural roads and urban expressways in China [1]
To obtain the appropriate data training period, the data during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined
Modeling real-time crash risk prediction is an important approach to identifying traffic condition causing crash, which can be used in the active traffic management control to reduce traffic accidents and ensure traffic safety

Summary

Introduction

Because of the rapid increase of traffic flow and frequent crash occurrence, traffic safety has become a severe problem for rural roads and urban expressways in China [1]. Modeling real-time crash risk prediction is an important approach to identifying traffic condition causing crash, which can be used in the active traffic management control to reduce traffic accidents and ensure traffic safety. For most of urban expressways in China, traffic detection devices, such as loop detector, microwave sensor, and video detection system, have been well installed. This makes it easier to detect and extract the traffic flow data. In this study, we mainly focused on urban expressways in China and established the real-time crash risk prediction model for these roads

Objectives

Methods

Findings

Conclusion