Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation
By analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatio-temporal correlation characteristics of traffi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8931597/ |
| _version_ | 1818428376326602752 |
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| author | Zhi Chen Yuan Jiang Dehui Sun |
| author_facet | Zhi Chen Yuan Jiang Dehui Sun |
| author_sort | Zhi Chen |
| collection | DOAJ |
| description | By analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatio-temporal correlation characteristics of traffic states based on the existing floating car data. At the same time, we extend the traffic conditions of urban road network from the upstream and downstream interaction to the global road network and complete the traffic congestion states discrimination of urban road network based on the spatio-temporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moran's I, a mixed forest prediction method considering the spatio-temporal correlation characteristics of urban road traffic state is constructed by improving the existing random forest algorithm. Finally, an example is given to verify the effect of the prediction method on the short-term prediction of urban road network traffic states. |
| first_indexed | 2024-12-14T15:00:38Z |
| format | Article |
| id | doaj.art-a85db708633242f98e0b6a6c6de751a3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:00:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a85db708633242f98e0b6a6c6de751a32022-12-21T22:56:51ZengIEEEIEEE Access2169-35362020-01-0183330334210.1109/ACCESS.2019.29591258931597Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal CorrelationZhi Chen0https://orcid.org/0000-0001-5945-5980Yuan Jiang1Dehui Sun2https://orcid.org/0000-0003-1362-1271Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing, ChinaBeijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing, ChinaBeijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing, ChinaBy analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatio-temporal correlation characteristics of traffic states based on the existing floating car data. At the same time, we extend the traffic conditions of urban road network from the upstream and downstream interaction to the global road network and complete the traffic congestion states discrimination of urban road network based on the spatio-temporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moran's I, a mixed forest prediction method considering the spatio-temporal correlation characteristics of urban road traffic state is constructed by improving the existing random forest algorithm. Finally, an example is given to verify the effect of the prediction method on the short-term prediction of urban road network traffic states.https://ieeexplore.ieee.org/document/8931597/Traffic congestionspatio-temporal correlationlocal Moran’s Ishort-term prediction |
| spellingShingle | Zhi Chen Yuan Jiang Dehui Sun Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation IEEE Access Traffic congestion spatio-temporal correlation local Moran’s I short-term prediction |
| title | Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation |
| title_full | Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation |
| title_fullStr | Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation |
| title_full_unstemmed | Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation |
| title_short | Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation |
| title_sort | discrimination and prediction of traffic congestion states of urban road network based on spatio temporal correlation |
| topic | Traffic congestion spatio-temporal correlation local Moran’s I short-term prediction |
| url | https://ieeexplore.ieee.org/document/8931597/ |
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