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Optimal traffic counting locations for origin-destination matrix estimation

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  1. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
  2. Chao Sun & Yulin Chang & Yuji Shi & Lin Cheng & Jie Ma, 2019. "Subnetwork Origin-Destination Matrix Estimation Under Travel Demand Constraints," Networks and Spatial Economics, Springer, vol. 19(4), pages 1123-1142, December.
  3. David Morrison & Susan Martonosi, 2015. "Characteristics of optimal solutions to the sensor location problem," Annals of Operations Research, Springer, vol. 226(1), pages 463-478, March.
  4. Castillo, Enrique & Calviño, Aida & Lo, Hong K. & Menéndez, José María & Grande, Zacarías, 2014. "Non-planar hole-generated networks and link flow observability based on link counters," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 239-261.
  5. 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.
  6. Marco Pozzoni & Giulia Ceccarelli & Andrea Gorrini & Lorenza Manenti & Luigi Sanfilippo, 2023. "TomTom Data Applications for the Assessment of Tactical Urbanism Interventions: The Case of Bologna," Sustainability, MDPI, vol. 15(17), pages 1-32, August.
  7. Xuesong Zhou & George F. List, 2010. "An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications," Transportation Science, INFORMS, vol. 44(2), pages 254-273, May.
  8. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
  9. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
  10. Bogyrbayeva, Aigerim & Kwon, Changhyun, 2021. "Pessimistic evasive flow capturing problems," European Journal of Operational Research, Elsevier, vol. 293(1), pages 133-148.
  11. Bar-Gera, Hillel & Mirchandani, Pitu B. & Wu, Fan, 2006. "Evaluating the assumption of independent turning probabilities," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 903-916, December.
  12. 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.
  13. Shi An & Lina Ma & Jian Wang, 2020. "Optimization of Traffic Detector Layout Based on Complex Network Theory," Sustainability, MDPI, vol. 12(5), pages 1-22, March.
  14. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
  15. Bera, Sharminda & Rao, K. V. Krishna, 2011. "Estimation of origin-destination matrix from traffic counts: the state of the art," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 49, pages 2-23.
  16. 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.
  17. 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).
  18. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
  19. 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.
  20. Lucio Bianco & Giuseppe Confessore & Monica Gentili, 2006. "Combinatorial aspects of the sensor location problem," Annals of Operations Research, Springer, vol. 144(1), pages 201-234, April.
  21. Chootinan, Piya & Chen, Anthony, 2011. "Confidence interval estimation for path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1680-1698.
  22. 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.
  23. 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.
  24. Ehlert, Anett & Bell, Michael G.H. & Grosso, Sergio, 2006. "The optimisation of traffic count locations in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 460-479, July.
  25. Chen, Anthony & Chootinan, Piya & Recker, Will, 2009. "Norm approximation method for handling traffic count inconsistencies in path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 852-872, September.
  26. Hui Chen & Zhaoming Chu & Chao Sun, 2021. "Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data," Sustainability, MDPI, vol. 13(23), pages 1-11, November.
  27. Cantelmo, Guido & Viti, Francesco & Cipriani, Ernesto & Nigro, Marialisa, 2018. "A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 303-320.
  28. 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.
  29. Danczyk, Adam & Liu, Henry X., 2011. "A mixed-integer linear program for optimizing sensor locations along freeway corridors," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 208-217, January.
  30. Marković, Nikola & Ryzhov, Ilya O. & Schonfeld, Paul, 2017. "Evasive flow capture: A multi-period stochastic facility location problem with independent demand," European Journal of Operational Research, Elsevier, vol. 257(2), pages 687-703.
  31. 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.
  32. 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.
  33. M. Gentili & P. Mirchandani, 2005. "Locating Active Sensors on Traffic Networks," Annals of Operations Research, Springer, vol. 136(1), pages 229-257, April.
  34. Lucio Bianco & Giuseppe Confessore & Pierfrancesco Reverberi, 2001. "A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates," Transportation Science, INFORMS, vol. 35(1), pages 50-60, February.
  35. 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.
  36. Fu, Hao & Lam, William H.K. & Shao, Hu & Kattan, Lina & Salari, Mostafa, 2022. "Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
  37. 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.
  38. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
  39. 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.
  40. Bagloee, Saeed Asadi & Sarvi, Majid & Wolshon, Brian & Dixit, Vinayak, 2017. "Identifying critical disruption scenarios and a global robustness index tailored to real life road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 60-81.
  41. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.
  42. 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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