Traffic speed prediction using GARCH-GRU hybrid model.
Traffic speed prediction is an integral part of an intelligent transportation system (ITS) because an advanced knowledge of traffic speed can help taking proactive preventive steps to avoid impending problems and it can also help in trip planning. Traffic speed data comprises a time series that may...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
John Wiley and Sons Inc.
2023
|
| Subjects: |
| _version_ | 1796867760240721920 |
|---|---|
| author | Ali, Muhammad Mohamad Yusof, Kamaludin Wilson, Benjamin Ziegelmueller, Carina |
| author_facet | Ali, Muhammad Mohamad Yusof, Kamaludin Wilson, Benjamin Ziegelmueller, Carina |
| author_sort | Ali, Muhammad |
| collection | ePrints |
| description | Traffic speed prediction is an integral part of an intelligent transportation system (ITS) because an advanced knowledge of traffic speed can help taking proactive preventive steps to avoid impending problems and it can also help in trip planning. Traffic speed data comprises a time series that may be modelled using any statistical or machine learning technique. In most of the cases, however, the performance of such models is bottlenecked due to heteroskedasticity usually present in such datasets. ARCH/GARCH family of models are generally used to model variance in such data. This paper presents a novel technique, termed as GARCH-GRU, based on additive decomposition that splits data into random (residual) and deterministic parts. Random part is normalized using rolling standard deviation. GARCH (1, 1) is used to predict conditional variance of the residual and the predicted variance is then used in the basic model equation along with normalized residual that mimic white noise as required by the model. The data other than residual is modelled using a GRU model. The approach is applied to two datasets corresponding to a downtown road and a motorway. For comparison, the same datasets are exposed to three classical techniques; seasonal ARIMA, CNN and GRU techniques. The results demonstrate that the GARCH-GRU technique outperforms others for random data of downtown road but fails to handle dynamic variations present in the motorway data. |
| first_indexed | 2024-04-09T03:45:42Z |
| format | Article |
| id | utm.eprints-104957 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-04-09T03:45:42Z |
| publishDate | 2023 |
| publisher | John Wiley and Sons Inc. |
| record_format | dspace |
| spelling | utm.eprints-1049572024-04-01T06:25:15Z http://eprints.utm.my/104957/ Traffic speed prediction using GARCH-GRU hybrid model. Ali, Muhammad Mohamad Yusof, Kamaludin Wilson, Benjamin Ziegelmueller, Carina TK Electrical engineering. Electronics Nuclear engineering TK7885-7895 Computer engineer. Computer hardware Traffic speed prediction is an integral part of an intelligent transportation system (ITS) because an advanced knowledge of traffic speed can help taking proactive preventive steps to avoid impending problems and it can also help in trip planning. Traffic speed data comprises a time series that may be modelled using any statistical or machine learning technique. In most of the cases, however, the performance of such models is bottlenecked due to heteroskedasticity usually present in such datasets. ARCH/GARCH family of models are generally used to model variance in such data. This paper presents a novel technique, termed as GARCH-GRU, based on additive decomposition that splits data into random (residual) and deterministic parts. Random part is normalized using rolling standard deviation. GARCH (1, 1) is used to predict conditional variance of the residual and the predicted variance is then used in the basic model equation along with normalized residual that mimic white noise as required by the model. The data other than residual is modelled using a GRU model. The approach is applied to two datasets corresponding to a downtown road and a motorway. For comparison, the same datasets are exposed to three classical techniques; seasonal ARIMA, CNN and GRU techniques. The results demonstrate that the GARCH-GRU technique outperforms others for random data of downtown road but fails to handle dynamic variations present in the motorway data. John Wiley and Sons Inc. 2023-11 Article PeerReviewed Ali, Muhammad and Mohamad Yusof, Kamaludin and Wilson, Benjamin and Ziegelmueller, Carina (2023) Traffic speed prediction using GARCH-GRU hybrid model. IET Intelligent Transport Systems, 17 (11). pp. 2300-2312. ISSN 1751-956X http://dx.doi.org/10.1049/itr2.12411 DOI: 10.1049/itr2.12411 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TK7885-7895 Computer engineer. Computer hardware Ali, Muhammad Mohamad Yusof, Kamaludin Wilson, Benjamin Ziegelmueller, Carina Traffic speed prediction using GARCH-GRU hybrid model. |
| title | Traffic speed prediction using GARCH-GRU hybrid model. |
| title_full | Traffic speed prediction using GARCH-GRU hybrid model. |
| title_fullStr | Traffic speed prediction using GARCH-GRU hybrid model. |
| title_full_unstemmed | Traffic speed prediction using GARCH-GRU hybrid model. |
| title_short | Traffic speed prediction using GARCH-GRU hybrid model. |
| title_sort | traffic speed prediction using garch gru hybrid model |
| topic | TK Electrical engineering. Electronics Nuclear engineering TK7885-7895 Computer engineer. Computer hardware |
| work_keys_str_mv | AT alimuhammad trafficspeedpredictionusinggarchgruhybridmodel AT mohamadyusofkamaludin trafficspeedpredictionusinggarchgruhybridmodel AT wilsonbenjamin trafficspeedpredictionusinggarchgruhybridmodel AT ziegelmuellercarina trafficspeedpredictionusinggarchgruhybridmodel |