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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2017-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2017/6263726 |
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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 |
work_keys_str_mv |
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