Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data
The rapid growth in data collection, storage, and transformation technologies offered new approaches that can be effectively utilized to improve traffic crash prediction. Considering the probability of traffic crash occurrence vary due to the spatiotemporal heterogeneity, this study proposes a state...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-09-01
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| Series: | International Journal of Transportation Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043022000648 |
| _version_ | 1797779182437007360 |
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| author | Mohammad Tamim Kashifi Mohammed Al-Turki Abdul Wakil Sharify |
| author_facet | Mohammad Tamim Kashifi Mohammed Al-Turki Abdul Wakil Sharify |
| author_sort | Mohammad Tamim Kashifi |
| collection | DOAJ |
| description | The rapid growth in data collection, storage, and transformation technologies offered new approaches that can be effectively utilized to improve traffic crash prediction. Considering the probability of traffic crash occurrence vary due to the spatiotemporal heterogeneity, this study proposes a state-of-the-art deep learning-based model that incorporates spatiotemporal information for the short-term crash prediction, named as Deep Spatiotemporal Hybrid Network (DSHN). The model integrates Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Artificial Neural Network (ANN) to incorporate the synergistic power of individual models. The study utilizes different data sources such as big traffic data collected from Paris road network sensors, weather conditions, infrastructure, holidays, and crash data. The results indicated that the proposed DSHN model outperforms the baseline models with an Area Under Curve (AUC) of about 0.800, an accuracy of 0.757, and a false alarm rate of 0.217. In addition, the importance of each data type is evaluated to investigate their impacts on the prediction performance of models. The sensitivity analysis results indicate that the road sensor data that includes average speed, vehicle kilometer traveled (VKT), and weighted average occupancy has the highest impact on the prediction accuracy. |
| first_indexed | 2024-03-12T23:26:59Z |
| format | Article |
| id | doaj.art-3db0cc5aa4614a61a6d89d7b807815c3 |
| institution | Directory Open Access Journal |
| issn | 2046-0430 |
| language | English |
| last_indexed | 2024-03-12T23:26:59Z |
| publishDate | 2023-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj.art-3db0cc5aa4614a61a6d89d7b807815c32023-07-16T04:18:22ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302023-09-01123793808Deep hybrid learning framework for spatiotemporal crash prediction using big traffic dataMohammad Tamim Kashifi0Mohammed Al-Turki1Abdul Wakil Sharify2Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Eastern Province, Saudi Arabia; Corresponding author.Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Eastern Province, Saudi ArabiaUniversity of Technology Malaysia, Civil Engineering Department, Skudai, Johor, MalaysiaThe rapid growth in data collection, storage, and transformation technologies offered new approaches that can be effectively utilized to improve traffic crash prediction. Considering the probability of traffic crash occurrence vary due to the spatiotemporal heterogeneity, this study proposes a state-of-the-art deep learning-based model that incorporates spatiotemporal information for the short-term crash prediction, named as Deep Spatiotemporal Hybrid Network (DSHN). The model integrates Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Artificial Neural Network (ANN) to incorporate the synergistic power of individual models. The study utilizes different data sources such as big traffic data collected from Paris road network sensors, weather conditions, infrastructure, holidays, and crash data. The results indicated that the proposed DSHN model outperforms the baseline models with an Area Under Curve (AUC) of about 0.800, an accuracy of 0.757, and a false alarm rate of 0.217. In addition, the importance of each data type is evaluated to investigate their impacts on the prediction performance of models. The sensitivity analysis results indicate that the road sensor data that includes average speed, vehicle kilometer traveled (VKT), and weighted average occupancy has the highest impact on the prediction accuracy.http://www.sciencedirect.com/science/article/pii/S2046043022000648Traffic CrashDeep Hybrid LearningBig DataCrash Prediction |
| spellingShingle | Mohammad Tamim Kashifi Mohammed Al-Turki Abdul Wakil Sharify Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data International Journal of Transportation Science and Technology Traffic Crash Deep Hybrid Learning Big Data Crash Prediction |
| title | Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data |
| title_full | Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data |
| title_fullStr | Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data |
| title_full_unstemmed | Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data |
| title_short | Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data |
| title_sort | deep hybrid learning framework for spatiotemporal crash prediction using big traffic data |
| topic | Traffic Crash Deep Hybrid Learning Big Data Crash Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2046043022000648 |
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