Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data
Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6939328 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849690446496792576 |
|---|---|
| author | He Bing Xu Zhifeng Xu Yangjie Hu Jinxing Ma Zhanwu |
| author_facet | He Bing Xu Zhifeng Xu Yangjie Hu Jinxing Ma Zhanwu |
| author_sort | He Bing |
| collection | DOAJ |
| description | Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset. |
| format | Article |
| id | doaj-art-ebadfec5c04e49a2aa914b67ff4ffb84 |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ebadfec5c04e49a2aa914b67ff4ffb842025-08-20T03:21:18ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/69393286939328Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS DataHe Bing0Xu Zhifeng1Xu Yangjie2Hu Jinxing3Ma Zhanwu4Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaGannan Normal University, School of Geography and Environmental Engineering, Ganzhou 341000, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaUniversity of Science and Technology Liaoning, School of Civil Engineering, Anshan 114051, ChinaRoad link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.http://dx.doi.org/10.1155/2020/6939328 |
| spellingShingle | He Bing Xu Zhifeng Xu Yangjie Hu Jinxing Ma Zhanwu Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data Complexity |
| title | Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data |
| title_full | Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data |
| title_fullStr | Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data |
| title_full_unstemmed | Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data |
| title_short | Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data |
| title_sort | integrating semantic zoning information with the prediction of road link speed based on taxi gps data |
| url | http://dx.doi.org/10.1155/2020/6939328 |
| work_keys_str_mv | AT hebing integratingsemanticzoninginformationwiththepredictionofroadlinkspeedbasedontaxigpsdata AT xuzhifeng integratingsemanticzoninginformationwiththepredictionofroadlinkspeedbasedontaxigpsdata AT xuyangjie integratingsemanticzoninginformationwiththepredictionofroadlinkspeedbasedontaxigpsdata AT hujinxing integratingsemanticzoninginformationwiththepredictionofroadlinkspeedbasedontaxigpsdata AT mazhanwu integratingsemanticzoninginformationwiththepredictionofroadlinkspeedbasedontaxigpsdata |