A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources
Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on highways is usually estimated by employing naïve methods and using limited sources of data. This could result in unreliable and inaccurate...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | Transportation Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X20300269 |
| _version_ | 1818850812683616256 |
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| author | Homa Taghipour Amir Bahador Parsa Abolfazl (Kouros) Mohammadian |
| author_facet | Homa Taghipour Amir Bahador Parsa Abolfazl (Kouros) Mohammadian |
| author_sort | Homa Taghipour |
| collection | DOAJ |
| description | Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on highways is usually estimated by employing naïve methods and using limited sources of data. This could result in unreliable and inaccurate travel time prediction which causes inconvenience for travelers. Despite abundant effort on predicting travel time, the dynamic nature of travel time was neglected in many studies. Therefore, the main objective of this study is to predict the short-term travel time of highways dynamically, using multiple data sources including loop detectors, probe vehicles, weather condition, geometry, accidents, road works, special events, and sun glare. To this end, three well known machine learning methods, Artificial Neural Network, K-Nearest Neighbors and Random Forest, are employed to predict and compare short-term travel time for prediction horizons of 5 min in one hour ahead. The results confirm satisfying performance of models, especially Random Forest, in short-term travel time prediction. Our analyses also show that traffic variables, especially occupancy, are the most effective variables in predicting the travel time. Finally, we proposed an approach for a dynamic prediction of travel time for a corridor. Interestingly, the results of our dynamic approach improved the accuracy of travel time prediction over the snapshot travel time prediction. |
| first_indexed | 2024-12-19T06:55:05Z |
| format | Article |
| id | doaj.art-20a9bceca64a48aea58e3986e978b6b2 |
| institution | Directory Open Access Journal |
| issn | 2666-691X |
| language | English |
| last_indexed | 2024-12-19T06:55:05Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Engineering |
| spelling | doaj.art-20a9bceca64a48aea58e3986e978b6b22022-12-21T20:31:34ZengElsevierTransportation Engineering2666-691X2020-12-012100025A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sourcesHoma Taghipour0Amir Bahador Parsa1Abolfazl (Kouros) Mohammadian2Corresponding author.; Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, 842W Taylor St, Chicago, IL 60607, United StatesDepartment of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, 842W Taylor St, Chicago, IL 60607, United StatesDepartment of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, 842W Taylor St, Chicago, IL 60607, United StatesHaving access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on highways is usually estimated by employing naïve methods and using limited sources of data. This could result in unreliable and inaccurate travel time prediction which causes inconvenience for travelers. Despite abundant effort on predicting travel time, the dynamic nature of travel time was neglected in many studies. Therefore, the main objective of this study is to predict the short-term travel time of highways dynamically, using multiple data sources including loop detectors, probe vehicles, weather condition, geometry, accidents, road works, special events, and sun glare. To this end, three well known machine learning methods, Artificial Neural Network, K-Nearest Neighbors and Random Forest, are employed to predict and compare short-term travel time for prediction horizons of 5 min in one hour ahead. The results confirm satisfying performance of models, especially Random Forest, in short-term travel time prediction. Our analyses also show that traffic variables, especially occupancy, are the most effective variables in predicting the travel time. Finally, we proposed an approach for a dynamic prediction of travel time for a corridor. Interestingly, the results of our dynamic approach improved the accuracy of travel time prediction over the snapshot travel time prediction.http://www.sciencedirect.com/science/article/pii/S2666691X20300269Dynamic travel time predictionMachine learningArtificial neural networkK-nearest neighborsRandom forest |
| spellingShingle | Homa Taghipour Amir Bahador Parsa Abolfazl (Kouros) Mohammadian A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources Transportation Engineering Dynamic travel time prediction Machine learning Artificial neural network K-nearest neighbors Random forest |
| title | A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources |
| title_full | A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources |
| title_fullStr | A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources |
| title_full_unstemmed | A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources |
| title_short | A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources |
| title_sort | dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources |
| topic | Dynamic travel time prediction Machine learning Artificial neural network K-nearest neighbors Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2666691X20300269 |
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