Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches
Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the im...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/3463287 |
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| _version_ | 1850166423229300736 |
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| author | Xu Miao Bing Wu Yajie Zou Lingtao Wu |
| author_facet | Xu Miao Bing Wu Yajie Zou Lingtao Wu |
| author_sort | Xu Miao |
| collection | DOAJ |
| description | Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation. |
| format | Article |
| id | doaj-art-e11646729dfb4474a0347110dc323908 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e11646729dfb4474a0347110dc3239082025-08-20T02:21:28ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/34632873463287Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction ApproachesXu Miao0Bing Wu1Yajie Zou2Lingtao Wu3The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaTexas A&M Transportation Institute, Texas A&M University System, College Station, TX 77843-3135, USAFreeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.http://dx.doi.org/10.1155/2020/3463287 |
| spellingShingle | Xu Miao Bing Wu Yajie Zou Lingtao Wu Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches Journal of Advanced Transportation |
| title | Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches |
| title_full | Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches |
| title_fullStr | Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches |
| title_full_unstemmed | Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches |
| title_short | Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches |
| title_sort | examining the impact of different periodic functions on short term freeway travel time prediction approaches |
| url | http://dx.doi.org/10.1155/2020/3463287 |
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