Predicting Real-Time Crash Risk for Urban Expressways in China

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...

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Main Authors: Miaomiao Liu, Yongsheng Chen
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2017/6263726
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spelling doaj-fe5253bf26ee4edd9c84959faca8350e2020-11-24T22:27:54ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472017-01-01201710.1155/2017/62637266263726Predicting Real-Time Crash Risk for Urban Expressways in ChinaMiaomiao Liu0Yongsheng Chen1Research Institute of Highway, Ministry of Transport, 8 Xitucheng Road, Haidian District, Beijing 100088, ChinaResearch Institute of Highway, Ministry of Transport, 8 Xitucheng Road, Haidian District, Beijing 100088, ChinaWe 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.http://dx.doi.org/10.1155/2017/6263726
collection DOAJ
language English
format Article
sources DOAJ
author Miaomiao Liu
Yongsheng Chen
spellingShingle Miaomiao Liu
Yongsheng Chen
Predicting Real-Time Crash Risk for Urban Expressways in China
Mathematical Problems in Engineering
author_facet Miaomiao Liu
Yongsheng Chen
author_sort Miaomiao Liu
title Predicting Real-Time Crash Risk for Urban Expressways in China
title_short Predicting Real-Time Crash Risk for Urban Expressways in China
title_full Predicting Real-Time Crash Risk for Urban Expressways in China
title_fullStr Predicting Real-Time Crash Risk for Urban Expressways in China
title_full_unstemmed Predicting Real-Time Crash Risk for Urban Expressways in China
title_sort predicting real-time crash risk for urban expressways in china
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2017-01-01
description 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.
url http://dx.doi.org/10.1155/2017/6263726
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AT yongshengchen predictingrealtimecrashriskforurbanexpresswaysinchina
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