Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network
Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing...
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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/10.1155/2020/8845804 |
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| _version_ | 1832567916076204032 |
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| author | Xiaolei Ru Xiangdong Xu Yang Zhou Chao Yang |
| author_facet | Xiaolei Ru Xiangdong Xu Yang Zhou Chao Yang |
| author_sort | Xiaolei Ru |
| collection | DOAJ |
| description | Predicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout. |
| format | Article |
| id | doaj-art-0e52476d5b3448adb1ead596d0300bb3 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0e52476d5b3448adb1ead596d0300bb32025-02-03T01:00:07ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88458048845804Critical Segments Identification for Link Travel Speed Prediction in Urban Road NetworkXiaolei Ru0Xiangdong Xu1Yang Zhou2Chao Yang3Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai 201804, ChinaPredicting traffic operational condition is crucial to urban transportation planning and management. A large variety of algorithms were proposed to improve the prediction accuracy. However, these studies were mainly based on complete data and did not discuss the vulnerability of massive data missing. And applications of these algorithms were in high-cost under the constraints of high quality of traffic data collecting in real-time on the large-scale road networks. This paper aims to deduce the traffic operational conditions of the road network with a small number of critical segments based on taxi GPS data in Xi’an city of China. To identify these critical segments, we assume that the states of floating cars within different road segments are correlative and mutually representative and design a heuristic algorithm utilizing the attention mechanism embedding in the graph neural network (GNN). The results show that the designed model achieves a high accuracy compared to the conventional method using only two critical segments which account for 2.7% in the road networks. The proposed method is cost-efficient which generates the critical segments scheme that reduces the cost of traffic information collection greatly and is more sensible without the demand for extremely high prediction accuracy. Our research has a guiding significance on cost saving of various information acquisition techniques such as route planning of floating car or sensors layout.http://dx.doi.org/10.1155/2020/8845804 |
| spellingShingle | Xiaolei Ru Xiangdong Xu Yang Zhou Chao Yang Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network Journal of Advanced Transportation |
| title | Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network |
| title_full | Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network |
| title_fullStr | Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network |
| title_full_unstemmed | Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network |
| title_short | Critical Segments Identification for Link Travel Speed Prediction in Urban Road Network |
| title_sort | critical segments identification for link travel speed prediction in urban road network |
| url | http://dx.doi.org/10.1155/2020/8845804 |
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