Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling
Abstract A two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12576 |
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| _version_ | 1846139788770410496 |
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| author | Junwoo Lim Juyeob Lee Chaehee An Eunil Park |
| author_facet | Junwoo Lim Juyeob Lee Chaehee An Eunil Park |
| author_sort | Junwoo Lim |
| collection | DOAJ |
| description | Abstract A two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance. |
| format | Article |
| id | doaj-art-9ab1cb28f52c4956aad044fa87abb59a |
| institution | Kabale University |
| issn | 1751-956X 1751-9578 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj-art-9ab1cb28f52c4956aad044fa87abb59a2024-12-06T05:51:13ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-12-0118122744275810.1049/itr2.12576Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modellingJunwoo Lim0Juyeob Lee1Chaehee An2Eunil Park3Department of Applied Artificial IntelligenceSungkyunkwan UniversitySeoulSouth KoreaDepartment of Applied Artificial IntelligenceSungkyunkwan UniversitySeoulSouth KoreaDepartment of Applied Artificial IntelligenceSungkyunkwan UniversitySeoulSouth KoreaDepartment of Applied Artificial IntelligenceSungkyunkwan UniversitySeoulSouth KoreaAbstract A two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.https://doi.org/10.1049/itr2.12576artificial intelligenceobject detectionreal‐time systemsroad traffictime seriestraffic management and control |
| spellingShingle | Junwoo Lim Juyeob Lee Chaehee An Eunil Park Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling IET Intelligent Transport Systems artificial intelligence object detection real‐time systems road traffic time series traffic management and control |
| title | Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling |
| title_full | Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling |
| title_fullStr | Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling |
| title_full_unstemmed | Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling |
| title_short | Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling |
| title_sort | enhancing real time traffic volume prediction a two step approach of object detection and time series modelling |
| topic | artificial intelligence object detection real‐time systems road traffic time series traffic management and control |
| url | https://doi.org/10.1049/itr2.12576 |
| work_keys_str_mv | AT junwoolim enhancingrealtimetrafficvolumepredictionatwostepapproachofobjectdetectionandtimeseriesmodelling AT juyeoblee enhancingrealtimetrafficvolumepredictionatwostepapproachofobjectdetectionandtimeseriesmodelling AT chaeheean enhancingrealtimetrafficvolumepredictionatwostepapproachofobjectdetectionandtimeseriesmodelling AT eunilpark enhancingrealtimetrafficvolumepredictionatwostepapproachofobjectdetectionandtimeseriesmodelling |