Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics
Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this rese...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/9695433 |
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| _version_ | 1849471322437976064 |
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| author | Liping Zhao Feng Li Dongye Sun Fei Dai |
| author_facet | Liping Zhao Feng Li Dongye Sun Fei Dai |
| author_sort | Liping Zhao |
| collection | DOAJ |
| description | Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data. |
| format | Article |
| id | doaj-art-ea11297decb64d94a156538fe4af52a3 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ea11297decb64d94a156538fe4af52a32025-08-20T03:24:52ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/9695433Highway Traffic Crash Risk Prediction Method considering Temporal Correlation CharacteristicsLiping Zhao0Feng Li1Dongye Sun2Fei Dai3Institute of Systems EngineeringInstitute of Systems EngineeringNational Engineering Research Center for Transportation Safety and Emergency InformaticsInstitute of Systems EngineeringCrash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data.http://dx.doi.org/10.1155/2023/9695433 |
| spellingShingle | Liping Zhao Feng Li Dongye Sun Fei Dai Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics Journal of Advanced Transportation |
| title | Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics |
| title_full | Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics |
| title_fullStr | Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics |
| title_full_unstemmed | Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics |
| title_short | Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics |
| title_sort | highway traffic crash risk prediction method considering temporal correlation characteristics |
| url | http://dx.doi.org/10.1155/2023/9695433 |
| work_keys_str_mv | AT lipingzhao highwaytrafficcrashriskpredictionmethodconsideringtemporalcorrelationcharacteristics AT fengli highwaytrafficcrashriskpredictionmethodconsideringtemporalcorrelationcharacteristics AT dongyesun highwaytrafficcrashriskpredictionmethodconsideringtemporalcorrelationcharacteristics AT feidai highwaytrafficcrashriskpredictionmethodconsideringtemporalcorrelationcharacteristics |