A congestion prediction method based on trajectory mining algorithm
Abstract Nowadays, number of private cars is increasing rapidly. Traffic congestion becomes a serious problem in urban region. If traffic congestion can be predicted before it happens, it will be helpful for improving traffic condition. So many traffic congestion prediction methods have been propose...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-025-00163-3 |
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| _version_ | 1832586000706043904 |
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| author | Liu Dongjiang Li Leixiao Li Jie |
| author_facet | Liu Dongjiang Li Leixiao Li Jie |
| author_sort | Liu Dongjiang |
| collection | DOAJ |
| description | Abstract Nowadays, number of private cars is increasing rapidly. Traffic congestion becomes a serious problem in urban region. If traffic congestion can be predicted before it happens, it will be helpful for improving traffic condition. So many traffic congestion prediction methods have been proposed. Almost all these methods are based on traffic flow prediction algorithm. In these methods, historical traffic flow data is used while performing prediction. Obviously, information of sudden accidents like traffic accidents, road damage and bad weather that happened recently may be not contained in historical traffic flow data. But performance of traffic flow prediction algorithms will be affected by these factors. In this situation, performance of traffic congestion prediction method based on traffic flow prediction result will be affected as well. To solve the problem, a new traffic congestion prediction method based on trajectory mining algorithm is proposed in this paper. In this method, traffic controllers can set a threshold for each road according to the current situation of the road. The threshold represents the vehicle number that can be carried by the corresponding road in a short period. Besides, for each road, the proposed method tries to count the number of vehicles that will pass through the specific road at next time step by predicting next location for all the running vehicles based on their trajectories. If the vehicle number of a road surpasses the threshold of this road, it will be predicted as congested road. Otherwise, it will be predicted as non-congested road. |
| format | Article |
| id | doaj-art-87ee3043ea7b410595b92a27aff85bf3 |
| institution | Kabale University |
| issn | 2730-6852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj-art-87ee3043ea7b410595b92a27aff85bf32025-01-26T12:20:10ZengSpringerComputational Urban Science2730-68522025-01-015111110.1007/s43762-025-00163-3A congestion prediction method based on trajectory mining algorithmLiu Dongjiang0Li Leixiao1Li Jie2College of Data Science and Applications, Inner Mongolia University of Technology, Jinchuan Industrial ParkCollege of Data Science and Applications, Inner Mongolia University of Technology, Jinchuan Industrial ParkCollege of Data Science and Applications, Inner Mongolia University of Technology, Jinchuan Industrial ParkAbstract Nowadays, number of private cars is increasing rapidly. Traffic congestion becomes a serious problem in urban region. If traffic congestion can be predicted before it happens, it will be helpful for improving traffic condition. So many traffic congestion prediction methods have been proposed. Almost all these methods are based on traffic flow prediction algorithm. In these methods, historical traffic flow data is used while performing prediction. Obviously, information of sudden accidents like traffic accidents, road damage and bad weather that happened recently may be not contained in historical traffic flow data. But performance of traffic flow prediction algorithms will be affected by these factors. In this situation, performance of traffic congestion prediction method based on traffic flow prediction result will be affected as well. To solve the problem, a new traffic congestion prediction method based on trajectory mining algorithm is proposed in this paper. In this method, traffic controllers can set a threshold for each road according to the current situation of the road. The threshold represents the vehicle number that can be carried by the corresponding road in a short period. Besides, for each road, the proposed method tries to count the number of vehicles that will pass through the specific road at next time step by predicting next location for all the running vehicles based on their trajectories. If the vehicle number of a road surpasses the threshold of this road, it will be predicted as congested road. Otherwise, it will be predicted as non-congested road.https://doi.org/10.1007/s43762-025-00163-3Intelligent transportation systemCongestion predictionTraffic congestionTrajectory mining, Next location prediction |
| spellingShingle | Liu Dongjiang Li Leixiao Li Jie A congestion prediction method based on trajectory mining algorithm Computational Urban Science Intelligent transportation system Congestion prediction Traffic congestion Trajectory mining, Next location prediction |
| title | A congestion prediction method based on trajectory mining algorithm |
| title_full | A congestion prediction method based on trajectory mining algorithm |
| title_fullStr | A congestion prediction method based on trajectory mining algorithm |
| title_full_unstemmed | A congestion prediction method based on trajectory mining algorithm |
| title_short | A congestion prediction method based on trajectory mining algorithm |
| title_sort | congestion prediction method based on trajectory mining algorithm |
| topic | Intelligent transportation system Congestion prediction Traffic congestion Trajectory mining, Next location prediction |
| url | https://doi.org/10.1007/s43762-025-00163-3 |
| work_keys_str_mv | AT liudongjiang acongestionpredictionmethodbasedontrajectoryminingalgorithm AT lileixiao acongestionpredictionmethodbasedontrajectoryminingalgorithm AT lijie acongestionpredictionmethodbasedontrajectoryminingalgorithm AT liudongjiang congestionpredictionmethodbasedontrajectoryminingalgorithm AT lileixiao congestionpredictionmethodbasedontrajectoryminingalgorithm AT lijie congestionpredictionmethodbasedontrajectoryminingalgorithm |