Rank-Constrained Mixed-Integer Optimization for Heterogeneous Sensor Location in Route Reconstruction

Published Online:https://doi.org/10.1287/ijoc.2024.0965

References

  • Bernas M, Płaczek B, Korski W, Loska P, Smyła J, Szymała P (2018) A survey and comparison of low-cost sensing technologies for road traffic monitoring. Sensors 18(10):3243.CrossrefGoogle Scholar
  • Bianco L, Confessore G, Reverberi P (2001) A network based model for traffic sensor location with implications on O/D matrix estimates. Transportation Sci. 35(1):50–60.LinkGoogle Scholar
  • Castillo E, Menéndez JM, Jiménez P (2008) Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations. Transportation Res. Part B: Methodological 42(5):455–481.CrossrefGoogle Scholar
  • Castillo E, Rivas A, Jiménez P, Menéndez JM (2012) Observability in traffic networks. Plate scanning added by counting information. Transportation 39:1301–1333.CrossrefGoogle Scholar
  • Castillo E, Grande Z, CalviñO A, Szeto WY, Lo HK (2015) A state-of-the-art review of the sensor location, flow observability, estimation, and prediction problems in traffic networks. J. Sensors 2015(1):1–26.CrossrefGoogle Scholar
  • Cerrone C, Cerulli R, Gentili M (2015) Vehicle-ID sensor location for route flow recognition: Models and algorithms. Eur. J. Oper. Res. 247(2):618–629.CrossrefGoogle Scholar
  • Daryalal M, Pouya H, DeSantis MA (2023) Network migration problem: A hybrid logic-based Benders decomposition approach. INFORMS J. Comput. 35(3):593–613.LinkGoogle Scholar
  • Elçi Ö, Hooker J (2022) Stochastic planning and scheduling with logic-based Benders decomposition. INFORMS J. Comput. 34(5):2428–2442.LinkGoogle Scholar
  • Fazel-Zarandi MM, Beck JC (2012) Using logic-based Benders decomposition to solve the capacity-and distance-constrained plant location problem. INFORMS J. Comput. 24(3):387–398.LinkGoogle Scholar
  • Fu C, Chen L, Lou Z (2026) Rank-constrained mixed-integer optimization for heterogeneous sensor location in route reconstruction. https://doi.org/10.1287/ijoc.2024.0965.cd, https://github.com/INFORMSJoC/2024.0965.Google Scholar
  • Fu C, Zhu N, Ma S (2017) A stochastic program approach for path reconstruction oriented sensor location model. Transportation Res. Part B: Methodological 102:210–237.CrossrefGoogle Scholar
  • Fu C, Zhu N, Ling S, Ma S, Huang Y (2016) Heterogeneous sensor location model for path reconstruction. Transportation Res. Part B: Methodological 91:77–97.CrossrefGoogle Scholar
  • Gentili M, Mirchandani PB (2005) Location of active sensors on traffic network. Ann. Oper. Res. 136:229–257.CrossrefGoogle Scholar
  • Gentili M, Mirchandani PB (2012) Locating sensors on traffic networks: Models, challenges and research opportunities. Transportation Res. Part C: Emerging Tech. 24:227–255.CrossrefGoogle Scholar
  • Gentili M, Mirchandani PB (2018) Review of optimal sensor location models for travel time estimation. Transportation Res. Part C: Emerging Tech. 90:74–96.CrossrefGoogle Scholar
  • Hadavi M, Shafahi Y (2016) Vehicle identification sensor models for origin–destination estimation. Transportation Res. Part B: Methodological 89:82–106.CrossrefGoogle Scholar
  • Han Q, Yang L, Chen Q, Zhou X, Zhang D, Wang A, Sun R, Luo X (2023) A GNN-guided predict-and-search framework for mixed-integer linear programming. Proc. 11th Internat. Conf. Learn. Representations (ICLR, Appleton, WI).Google Scholar
  • Hasija S, Shen ZJM, Teo CP (2020) Smart city operations: Modeling challenges and opportunities. Manufacturing Service Oper. Management 22(1):203–213.LinkGoogle Scholar
  • Hooker JN (2023) Logic-Based Benders Decomposition: Theory and Applications, Synthesis Lectures on Operations Research and Applications (Springer, Cham, Switzerland).Google Scholar
  • Hu S, Peeta S, Chu C (2009) Identification of vehicle sensor locations for link-based network. Transportation Res. Part B: Methodological 43(8–9):873–894.CrossrefGoogle Scholar
  • Hu SR, Peeta S, Liou HT (2015) Integrated determination of network origin–destination trip matrix and heterogeneous sensor selection and location strategy. IEEE Trans. Intelligent Transportation Systems 17(1):195–205.CrossrefGoogle Scholar
  • Kang M, Lee C (2021) An exact algorithm for heterogeneous drone-truck routing problem. Transportation Sci. 55(5):1088–1112.LinkGoogle Scholar
  • Laporte G, Louveaux FV (1993) The integer L-shaped method for stochastic integer programs with complete recourse. Oper. Res. Lett. 13(3):133–142.CrossrefGoogle Scholar
  • Li X, Ouyang Y (2012) Reliable traffic sensor deployment under probabilistic disruptions and generalized surveillance effectiveness measures. Oper. Res. 60(5):1183–1198.LinkGoogle Scholar
  • Lubin M, Vielma JP, Zadik I (2022) Mixed-integer convex representability. Math. Oper. Res. 47(1):720–749.LinkGoogle Scholar
  • Mak HY (2022) Enabling smarter cities with operations management. Manufacturing Service Oper. Management 24(1):24–39.LinkGoogle Scholar
  • Markovsky I (2012) Low Rank Approximation: Algorithms, Implementation, Applications (Springer, London).CrossrefGoogle Scholar
  • Martínez KP, Adulyasak Y, Jans R (2022) Logic-based Benders decomposition for integrated process configuration and production planning problems. INFORMS J. Comput. 34(4):2177–2191.LinkGoogle Scholar
  • Mimbela LEY, Klein LA (2007) Summary of Vehicle Detection and Surveillance Technologies Used in Intelligent Transportation Systems (The National Vehicle Detector Clearinghouse, Las Cruces, New Mexico).Google Scholar
  • 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 Res. Part B: Methodological 44(2):282–298.CrossrefGoogle Scholar
  • Owais M (2022) Traffic sensor location problem: Three decades of research. Expert Syst. Appl. 208:118134.CrossrefGoogle Scholar
  • Park H, Haghani A, Gao S, Knodler MA, Samuel S (2018) Anticipatory dynamic traffic sensor location problems with connected vehicle technologies. Transportation Sci. 52(6):1299–1326.LinkGoogle Scholar
  • Salari M, Kattan L, Lam WH, Lo H, Esfeh MA (2019) Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure. Transportation Res. Part B: Methodological 121:216–251.CrossrefGoogle Scholar
  • Sánchez-Cambronero S, Jiménez P, Rivas A, Gallego I (2017) Plate scanning tools to obtain travel times in traffic networks. J. Intelligent Transportation Systems 21(5):390–408.CrossrefGoogle Scholar
  • Shao M, Xie C, Sun L (2021) Optimization of network sensor location for full link flow observability considering sensor measurement error. Transportation Res. Part C: Emerging Tech. 133:103460.CrossrefGoogle Scholar
  • Sun C, Dai R (2017) Rank-constrained optimization and its applications. Automatica 82:128–136.CrossrefGoogle Scholar
  • Sun W, Shao H, Wu T, Shao F, Fainman EZ (2022) Reliable location of automatic vehicle identification sensors to recognize origin-destination demands considering sensor failure. Transportation Res. Part C: Emerging Tech. 136:103551.CrossrefGoogle Scholar
  • Wang N, Mirchandani P (2013) Sensor location model to optimize origin-destination estimation with a Bayesian statistical procedure. Transportation Res. Record: J. Transportation Res. Board 2334(1):29–39.CrossrefGoogle Scholar
  • Xing T, Zhou X, Taylor J (2013) Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach. Transportation Res. Part B: Methodological 57:66–90.CrossrefGoogle Scholar
  • Yang H, Zhou J (1998) Optimal traffic counting location for origin-destination matrix estimation. Transportation Res. Part B: Methodological 32(2):109–126.CrossrefGoogle Scholar
  • Yu X, Ma S, Zhu N, Lam WH, Fu H (2023) Ensuring the robustness of link flow observation systems in sensor failure events. Transportation Res. Part B: Methodological 178:102849.CrossrefGoogle Scholar
  • Zangui M, Yin Y, Lawphongpanich S (2015) Sensor location problems in path-differentiated congestion pricing. Transportation Res. Part C: Emerging Tech. 55:217–230.CrossrefGoogle Scholar
  • Zhu N, Fu C, Ma S (2018) Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model. Transportation Res. Part B: Methodological 113:91–120.CrossrefGoogle Scholar
  • Zhu N, Fu C, Zhang X, Ma S (2022) A network sensor location problem for link flow observability and estimation. Eur. J. Oper. Res. 300(2):428–448.CrossrefGoogle Scholar
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