Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold
Studying the time interval duration between the first accident and the second accident caused by it can provide decision makers with valuable information on how to effectively deal with high-risk second accidents. This paper is aimed to explore the potential influencing factors of the interval durat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6312139 |
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| _version_ | 1832565554778472448 |
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| author | Fang Liu Lanlan Zheng Mingyang Li Jinjun Tang |
| author_facet | Fang Liu Lanlan Zheng Mingyang Li Jinjun Tang |
| author_sort | Fang Liu |
| collection | DOAJ |
| description | Studying the time interval duration between the first accident and the second accident caused by it can provide decision makers with valuable information on how to effectively deal with high-risk second accidents. This paper is aimed to explore the potential influencing factors of the interval duration between the two accidents and predict it. First, the spatiotemporal definition method is applied to identify the cascaded first accident and the second accident. Then, on the basis of using Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s sphere test statistics to ensure the applicability of the data to the factor analysis method, the explanatory variables that can significantly affect the interval duration are obtained through the factor analysis method. Finally, the random forest model (RF), which combines the advantages of machine learning methods, is employed to predict the duration of the interval. Traffic accident data set collected in Los Angeles city from February 2016 to June 2020 is used to validate prediction performance in this study. Bayesian method is applied to optimize the hyperparameters in the RF, while three evaluation indicators, including the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), are used to estimate the prediction effect. The test results and comparative experiments confirm that RF is able to predict the interval well and has better prediction performance. This is of great significance for the prediction of the duration of the interval between one accident and the second accident. |
| format | Article |
| id | doaj-art-022841cdcf0541e0ab4a02709eac0cdb |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-022841cdcf0541e0ab4a02709eac0cdb2025-02-03T01:07:12ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6312139Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal ThresholdFang Liu0Lanlan Zheng1Mingyang Li2Jinjun Tang3School of Transportation EngineeringSmart Transportation Key Laboratory of Hunan ProvinceSmart Transportation Key Laboratory of Hunan ProvinceSmart Transportation Key Laboratory of Hunan ProvinceStudying the time interval duration between the first accident and the second accident caused by it can provide decision makers with valuable information on how to effectively deal with high-risk second accidents. This paper is aimed to explore the potential influencing factors of the interval duration between the two accidents and predict it. First, the spatiotemporal definition method is applied to identify the cascaded first accident and the second accident. Then, on the basis of using Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s sphere test statistics to ensure the applicability of the data to the factor analysis method, the explanatory variables that can significantly affect the interval duration are obtained through the factor analysis method. Finally, the random forest model (RF), which combines the advantages of machine learning methods, is employed to predict the duration of the interval. Traffic accident data set collected in Los Angeles city from February 2016 to June 2020 is used to validate prediction performance in this study. Bayesian method is applied to optimize the hyperparameters in the RF, while three evaluation indicators, including the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), are used to estimate the prediction effect. The test results and comparative experiments confirm that RF is able to predict the interval well and has better prediction performance. This is of great significance for the prediction of the duration of the interval between one accident and the second accident.http://dx.doi.org/10.1155/2022/6312139 |
| spellingShingle | Fang Liu Lanlan Zheng Mingyang Li Jinjun Tang Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold Journal of Advanced Transportation |
| title | Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold |
| title_full | Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold |
| title_fullStr | Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold |
| title_full_unstemmed | Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold |
| title_short | Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold |
| title_sort | analysis and prediction of the interval duration between the first and second accidents considering the spatiotemporal threshold |
| url | http://dx.doi.org/10.1155/2022/6312139 |
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