Long Short-Term Memory-Based Impact of Traffic Incidents: Case Study of Seoul Expressway, Korea
A traffic incident is one of the major concerns about traffic congestion on an urban expressway. Therefore, understanding the impact of traffic incidents can provide more accurate information to traffic management and participants. This study proposes a new approach that can be used to enhance the t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
World Scientific Publishing
2025-05-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2196888825400044 |
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| Summary: | A traffic incident is one of the major concerns about traffic congestion on an urban expressway. Therefore, understanding the impact of traffic incidents can provide more accurate information to traffic management and participants. This study proposes a new approach that can be used to enhance the traffic incident impact in real situations. The approach combines the VISSIM simulation and machine learning algorithm. First, the fusion data were gathered from various sources, such as the global positioning system (GPS) data on the Seoul expressway, the traffic accident from the traffic accident analysis system (TAAS), and the traffic volume from the Seoul traffic information management system (TOPIS). Second, the speed, traffic volume, and accident information are matched together based on the national standard node link. Then, the VISSIM is used to analyze the speed change and expand incident data considering the incident severity. Finally, the long short-term memory (LSTM) is implemented for training the dataset and forecasting traffic incidents for the prediction model. The proposed approach could perform better accuracy prediction for the traffic incident impact to enhance the comfortable and safe operations of the urban expressway. Especially, the 10% VISSIM data penetration rate achieved the best performance with an average mean absolute percentage error (MAPE) of 24.80. |
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| ISSN: | 2196-8888 2196-8896 |