Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks
Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/9/307 |
| _version_ | 1797492018330468352 |
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| author | Wenyun Tang Jiahui Chen Chao Sun Hanbing Wang Gen Li |
| author_facet | Wenyun Tang Jiahui Chen Chao Sun Hanbing Wang Gen Li |
| author_sort | Wenyun Tang |
| collection | DOAJ |
| description | Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within ±12; whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model’s technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies. |
| first_indexed | 2024-03-10T00:57:33Z |
| format | Article |
| id | doaj.art-456112b137aa4428a833285d2a35666f |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T00:57:33Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-456112b137aa4428a833285d2a35666f2023-11-23T14:40:06ZengMDPI AGAlgorithms1999-48932022-08-0115930710.3390/a15090307Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested NetworksWenyun Tang0Jiahui Chen1Chao Sun2Hanbing Wang3Gen Li4College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaTraffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within ±12; whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model’s technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies.https://www.mdpi.com/1999-4893/15/9/307traffic networkdemand estimationcongested networkstrajectory reconstructionBayesian |
| spellingShingle | Wenyun Tang Jiahui Chen Chao Sun Hanbing Wang Gen Li Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks Algorithms traffic network demand estimation congested networks trajectory reconstruction Bayesian |
| title | Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks |
| title_full | Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks |
| title_fullStr | Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks |
| title_full_unstemmed | Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks |
| title_short | Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks |
| title_sort | traffic demand estimations considering route trajectory reconstruction in congested networks |
| topic | traffic network demand estimation congested networks trajectory reconstruction Bayesian |
| url | https://www.mdpi.com/1999-4893/15/9/307 |
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