Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction
In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (C...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/3247847 |
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| author | Leilei Kang Guojing Hu Hao Huang Weike Lu Lan Liu |
| author_facet | Leilei Kang Guojing Hu Hao Huang Weike Lu Lan Liu |
| author_sort | Leilei Kang |
| collection | DOAJ |
| description | In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting. |
| format | Article |
| id | doaj-art-e310f8b8c7ba4cf38e7afa61b6649705 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e310f8b8c7ba4cf38e7afa61b66497052025-08-20T03:21:07ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/32478473247847Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature ExtractionLeilei Kang0Guojing Hu1Hao Huang2Weike Lu3Lan Liu4School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaDepartment of Mathematics and Statistical Sciences, Jackson State University, Jackson 39217, MS, USASchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaAlabama Transportation Institute, The University of Alabama, Tuscaloosa 35487, AL, USASchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan 610031, ChinaIn order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.http://dx.doi.org/10.1155/2020/3247847 |
| spellingShingle | Leilei Kang Guojing Hu Hao Huang Weike Lu Lan Liu Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction Journal of Advanced Transportation |
| title | Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction |
| title_full | Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction |
| title_fullStr | Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction |
| title_full_unstemmed | Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction |
| title_short | Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction |
| title_sort | urban traffic travel time short term prediction model based on spatio temporal feature extraction |
| url | http://dx.doi.org/10.1155/2020/3247847 |
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