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Dynamic location models of mobile sensors for travel time estimation on a freeway

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors, travel time on a freeway is stochastic. To obtain travel time estimates for a freeway accurately, this paper proposes two traffic sensor location models that consider minimizing the error of travel time estimation and maximizing the collected traffic flow. First, a dynamic optimal location model of the mobile sensor is proposed under the assumption that there are no traffic sensors on a freeway. Next, a dynamic optimal combinatorial model of adding mobile sensors taking account of fixed sensors on a freeway is presented. It should be pointed out that the technology of data fusion will be adopted to tackle the collected data from multiple sensors in the second optimization model. Then, a simulated annealing algorithm is established to find the solutions of the proposed two optimization models. Numerical examples demonstrate that dynamic optimization of mobile sensor locations for the estimation of travel times on a freeway is more accurate than the conventional location model.
Rocznik
Strony
271--287
Opis fizyczny
Bibliogr. 41 poz., tab., wykr.
Twórcy
autor
  • School of Mathematics, China University of Mining and Technology, 1 Daxue Road, Tongshan District, Xuzhou 221116, China; School of Mathematics and Statistics, Fuyang Normal University, 100 Qinghe Road, Yingzhou District, Fuyang 236037, China
autor
  • School of Management, Xuzhou Medical University, 209 Tongshan Road, Gulou District, Xuzhou 221004, China
autor
  • School of Mathematics, China University of Mining and Technology, 1 Daxue Road, Tongshan District, Xuzhou 221116, China
autor
  • College of Mathematics and Information Science, Guangxi University, 100 Daxue Road, Xixiangtang District, Nanning 530004, China
Bibliografia
  • [1] Ban, X., Chu, L., Herring, R. and Margulici, J. (2011). Sequential modeling framework for optimal sensor placement for multiple intelligent transportation system applications, Journal of Transportation Engineering 137(2): 112–120.
  • [2] Ban, X., Herring, R., Margulici, J. and Bayen, A. (2009). Optimal sensor placement for travel time estimation, Transportation and Traffic Theory 2009: 697–721.
  • [3] Bartin, B., Ozbay, K. and Iyigun, C. (2007). A clustering based methodology for determining optimal roadway configuration of detectors for travel time estimation, Transportation Research Record 2000: 98–105.
  • [4] Beryini, R. and Lovell, D. (2009). Impacts of sensor spacing on accurate freeway travel time estimation for traveler information, Journal of Intelligent Transportation Systems 13(2): 97–110.
  • [5] Chakraborty, P., Hegde, C. and Sharma, A. (2019). Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds, Transportation Research C 105: 81–99.
  • [6] Chang, B.-J., Hwang, R.-H., Tsai, Y.-L., Yu, B.-H. and Liang, Y.-H. (2019). Cooperative adaptive driving for platooning autonomous self driving based on edge computing, International Journal of Applied Mathematics and Computer Science 29(2): 213–225, DOI: 10.2478/amcs-2019-0016.
  • [7] Chaudhuri, P., Martin, P.T., Stevanovic, A.Z. and Zhu, C. (2010). The effects of detector spacing on travel time prediction on freeways, World Academy of Science, Engineering and Technology 42(6): 1–10.
  • [8] Chou, J.-J., Shih, C.-S., Wang, W.-D. and Huang, K.-C. (2019). IoT sensing networks for gait velocity measurement, International Journal of Applied Mathematics and Computer Science 29(2): 245–259, DOI: 10.2478/amcs-2019-0018.
  • [9] Chow, J. (2016). Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy, International Journal of Transportation Science and Technology 5(3): 167–185.
  • [10] Danczyk, A., Di, X. and Liu, H. (2016). A probabilistic optimization model for allocating freeway sensors, Transportation Research C 67: 378–398.
  • [11] Danczyk, A. and Liu, H. (2011). A mixed-integer linear program for optimizing sensor locations along freeway corridors, Transportation Research Part B 45(1): 208–217.
  • [12] Fischetti, M. and Monaci, M. (2020). A branch-and-cut algorithm for mixed-integer bilinear programming, European Journal of Operational Research 282(2): 506–514.
  • [13] Fu, C., Zhu, N. and Ma, S. (2017). A stochastic program approach for path reconstruction oriented sensor location model, Transportation Research Part B 102: 210–237.
  • [14] Fujito, I., Margiotta, R., Huang, W. and Perez, W.A. (2006). Effect of sensor spacing on performance measure calculations, Journal of the Transportation Research Board 1945: 1–11.
  • [15] Geetla, T., Batta, R., Blatt, A., Flanigan, M. and Majka, K. (2014). Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization, Transportation Research C 45: 64–82.
  • [16] Gentili, M. and Mirchandani, P. (2012). Locating sensors on traffic networks: Models, challenges and research opportunities, Transportation Research C 24: 227–255.
  • [17] Gentili, M. and Mirchandani, P. (2018). Review of optimal sensor location models for travel time estimation, Transportation Research C 90: 74–96.
  • [18] He, S. (2013). A graphical approach to identify sensor locations for link flow inference, Transportation Research B 51: 65–76.
  • [19] Hong, Z. and Fukuda, D. (2012). Effects of traffic sensor location on traffic state estimation, Procedia-Social and Behavioral Sciences 54(2290): 1186–1196.
  • [20] Karatsoli, M., Margreiter, M. and Spangler, M. (2017). Bluetooth-based travel times for automatic incident detection-a systematic description of the characteristics for traffic management purposes, Transportation Research Procedia 24: 204–211.
  • [21] Kianfar, J. and Edara, P. (2010). Optimizing freeway traffic sensor locations by clustering global-positioning-system-derived speed patterns, IEEE Transactions on Intelligent Transportation Systems 11(3): 738–747.
  • [22] Kim, J., Park, B., Lee, J. and Won, J. (2011). Determining optimal sensor locations in freeway using genetic algorithm-based optimization, Engineering Applications of Artificial Intelligence 24(2): 318–324.
  • [23] Kolak, O., Feyzioğlu, O. and Noyan, N. (2018). Bi-level multi-objective traffic network optimisation with sustainability perspective, Expert Systems with Applications 104(15): 294–306.
  • [24] Kolosz, B., Grant-Muller, S. and Djemame, K. (2013). Modelling uncertainty in the sustainability of intelligent transport systems for highways using probabilistic data fusion, Environmental Modelling & Software 49: 78–97.
  • [25] Li, X. and Ouyang, Y. (2011). Reliable sensor deployment for network traffic surveillance, Transportation Research B 45: 218–231.
  • [26] Liu, F. L., Wang, Y., Bai, Y. and Yu, J. (2019). Study on stealth characteristics of metamaterials based on simulated annealing algorithm, Procedia Computer Science 147: 221–227.
  • [27] Liu, H. and Danczyk, A. (2009). Optimal sensor locations for freeway bottleneck identification, Computer-Aided Civil and Infrastructure Engineering 24(8): 535–550.
  • [28] Ma,W. and Qian, Z. (2018). Statistical inference of probabilistic origin-destination demand using day-to-day traffic data, Transportation Research C 88: 227–256.
  • [29] Meng, T., Jing, X., Yan, Z. and Pedrycz, W. (2020). A survey on machine learning for data fusion, Information Fusion 57: 115–229.
  • [30] Nemati, M., Braun, M. and Tenbohlen, S. (2018). Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming, Applied Energy 210: 944–963.
  • [31] Ng, M. (2013). Partial link flow observability in the presence of initial sensors: Solution without path enumeration, Transportation Research E 51: 62–66.
  • [32] Olia, A., Abdelgawad, H., Abdulhai, B. and Razavi, S. (2017). Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment, Journal of Intelligent Transportation Systems 21(4): 296–309.
  • [33] Park, H. and Haghani, A. (2015). Optimal number and location of bluetooth sensors considering stochastic travel time prediction, Transportation Research C 55: 203–216.
  • [34] Salari, M., Kattan, L., Lam, W., Lo, H. and Esfeh, M. (2019). Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure, Transportation Research Part B 121: 216–251.
  • [35] Song, Z.R., Zang, L.L. and Zhu, W.X. (2020). Study on minimum emission control strategy on arterial road based on improved simulated annealing genetic algorithm, Physica A 537: 1–11.
  • [36] Xing, T., Zhou, X. and Taylor, J. (2013). Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach, Transportation Research B 57: 66–90.
  • [37] Yang, Y. and Fan, Y. (2015). Data dependent input control for origin-destination demand estimation using observability analysis, Transportation Research B 78: 385–403.
  • [38] Zhan, F., Wan, X., Zhang, J., Li, R. and Ran, B. (2015). Sample size reduction method based on data fusion for freeways with fixed detectors, Transportation Research Record 2528: 18–26.
  • [39] Zhu, N., Fu, C. and Ma, S. (2018). Data-driven distributionally robust optimization approach for reliable travel-time-information-gain-oriented traffic sensor location model, Transportation Research B 113: 91–120.
  • [40] Zhu, N., Liu, Y., Ma, S. and He, Z. (2014). Mobile traffic sensor routing in dynamic transportation systems, IEEE Transactions on Intelligent Transportation Systems 15(5): 2273–2285.
  • [41] Zhu, N., Ma, S. and Zheng, L. (2017). Travel time estimation oriented freeway sensor placement problem considering sensor failure, Journal of Intelligent Transportation Systems 21(1): 26–40.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ce5747b2-f4cd-4c69-aabb-85a61c8bd519
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