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Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations

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Cited by:

  1. Yu, Xinyao & Ma, Shoufeng & Zhu, Ning & Lam, William H.K. & Fu, Hao, 2023. "Ensuring the robustness of link flow observation systems in sensor failure events," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
  2. Lijun Sun & Xinyu Chen & Zhaocheng He & Luis F. Miranda-Moreno, 2023. "Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior," Networks and Spatial Economics, Springer, vol. 23(2), pages 407-428, June.
  3. Li, Xiaopeng & Ouyang, Yanfeng, 2011. "Reliable sensor deployment for network traffic surveillance," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 218-231, January.
  4. Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W. & Lam, William H.K., 2015. "Statistical approach for activity-based model calibration based on plate scanning and traffic counts data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 280-300.
  5. Fu, Chenyi & Zhu, Ning & Ling, Shuai & Ma, Shoufeng & Huang, Yongxi, 2016. "Heterogeneous sensor location model for path reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 77-97.
  6. Mojtaba Rostami Nasab & Yousef Shafahi, 2020. "Estimation of origin–destination matrices using link counts and partial path data," Transportation, Springer, vol. 47(6), pages 2923-2950, December.
  7. Hadavi, Majid & Shafahi, Yousef, 2016. "Vehicle identification sensor models for origin–destination estimation," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 82-106.
  8. Owais, Mahmoud & Moussa, Ghada S. & Hussain, Khaled F., 2019. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
  9. Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
  10. Lo, Hong K. & Chen, Anthony & Castillo, Enrique, 2016. "Robust network sensor location for complete link flow observability under uncertaintyAuthor-Name: Xu, Xiangdong," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 1-20.
  11. Mínguez, R. & Sánchez-Cambronero, S. & Castillo, E. & Jiménez, P., 2010. "Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 282-298, February.
  12. Wang, Yinpu & An, Chengchuan & Ou, Jishun & Lu, Zhenbo & Xia, Jingxin, 2022. "A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
  13. Dongya Li & Wei Wang & De Zhao, 2022. "A Practical and Sustainable Approach to Determining the Deployment Priorities of Automatic Vehicle Identification Sensors," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
  14. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
  15. Zhu, Ning & Fu, Chenyi & Zhang, Xuanyi & Ma, Shoufeng, 2022. "A network sensor location problem for link flow observability and estimation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 428-448.
  16. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
  17. Osorio, Carolina, 2019. "High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 18-43.
  18. He, Sheng-xue, 2013. "A graphical approach to identify sensor locations for link flow inference," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 65-76.
  19. Fu, Chenyi & Zhu, Ning & Ma, Shoufeng, 2017. "A stochastic program approach for path reconstruction oriented sensor location model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 210-237.
  20. Santos Sánchez-Cambronero & Fernando Álvarez-Bazo & Ana Rivas & Inmaculada Gallego, 2021. "Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors," Sustainability, MDPI, vol. 13(8), pages 1-30, April.
  21. Viti, Francesco & Rinaldi, Marco & Corman, Francesco & Tampère, Chris M.J., 2014. "Assessing partial observability in network sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 65-89.
  22. Hu, Shou-Ren & Peeta, Srinivas & Chu, Chun-Hsiao, 2009. "Identification of vehicle sensor locations for link-based network traffic applications," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 873-894, September.
  23. Dimitris Bertsimas & Julia Yan, 2018. "From Physical Properties of Transportation Flows to Demand Estimation: An Optimization Approach," Transportation Science, INFORMS, vol. 52(4), pages 1002-1011, August.
  24. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
  25. Maryam Abareshi & Mehdi Zaferanieh & Bagher Keramati, 2017. "Path Flow Estimator in an Entropy Model Using a Nonlinear L-Shaped Algorithm," Networks and Spatial Economics, Springer, vol. 17(1), pages 293-315, March.
  26. Xiaopeng Li & Yanfeng Ouyang, 2012. "Reliable Traffic Sensor Deployment Under Probabilistic Disruptions and Generalized Surveillance Effectiveness Measures," Operations Research, INFORMS, vol. 60(5), pages 1183-1198, October.
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