JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism
Accurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/1213221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565453952647168 |
|---|---|
| author | Alfateh M. Tag Elsir Alkilane Khaled Pengfei Wang Yanming Shen |
| author_facet | Alfateh M. Tag Elsir Alkilane Khaled Pengfei Wang Yanming Shen |
| author_sort | Alfateh M. Tag Elsir |
| collection | DOAJ |
| description | Accurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on traffic patterns, and therefore, it is important to develop a mechanism that can jointly capture correlations of these components. However, spatial sparsity issues make travel time prediction very challenging, especially when dealing with the origin-destination (OD) method, since the trajectory data may not be available. In this paper, we introduce a unified deep learning-based framework named joint spatial-temporal correlation (JSTC) mechanism to improve the accuracy of OD travel time prediction. First, we design a spatiotemporal correlation block that combines two modules: self-convolutional attention integrated with a temporal convolutional network (TCN) to capture the spatial correlations along with the temporal dependencies. Then, we enhance our model performance through adopting a multi-head attention module to learn the attentional weights of the spatial, temporal, and external features based on their contributions to the output and speed up the training process. Extensive experiments on three large-scale real-world traffic datasets (NYC, Chengdu, and Xi’an) show the efficiency of our model and its superiority compared to other methods. |
| format | Article |
| id | doaj-art-82b141ab66d94af899c9f2caa630e85f |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-82b141ab66d94af899c9f2caa630e85f2025-02-03T01:07:37ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1213221JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation MechanismAlfateh M. Tag Elsir0Alkilane Khaled1Pengfei Wang2Yanming Shen3School of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer Science and TechnologyAccurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on traffic patterns, and therefore, it is important to develop a mechanism that can jointly capture correlations of these components. However, spatial sparsity issues make travel time prediction very challenging, especially when dealing with the origin-destination (OD) method, since the trajectory data may not be available. In this paper, we introduce a unified deep learning-based framework named joint spatial-temporal correlation (JSTC) mechanism to improve the accuracy of OD travel time prediction. First, we design a spatiotemporal correlation block that combines two modules: self-convolutional attention integrated with a temporal convolutional network (TCN) to capture the spatial correlations along with the temporal dependencies. Then, we enhance our model performance through adopting a multi-head attention module to learn the attentional weights of the spatial, temporal, and external features based on their contributions to the output and speed up the training process. Extensive experiments on three large-scale real-world traffic datasets (NYC, Chengdu, and Xi’an) show the efficiency of our model and its superiority compared to other methods.http://dx.doi.org/10.1155/2022/1213221 |
| spellingShingle | Alfateh M. Tag Elsir Alkilane Khaled Pengfei Wang Yanming Shen JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism Journal of Advanced Transportation |
| title | JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism |
| title_full | JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism |
| title_fullStr | JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism |
| title_full_unstemmed | JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism |
| title_short | JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism |
| title_sort | jstc travel time prediction with a joint spatial temporal correlation mechanism |
| url | http://dx.doi.org/10.1155/2022/1213221 |
| work_keys_str_mv | AT alfatehmtagelsir jstctraveltimepredictionwithajointspatialtemporalcorrelationmechanism AT alkilanekhaled jstctraveltimepredictionwithajointspatialtemporalcorrelationmechanism AT pengfeiwang jstctraveltimepredictionwithajointspatialtemporalcorrelationmechanism AT yanmingshen jstctraveltimepredictionwithajointspatialtemporalcorrelationmechanism |