Dynamic location models of mobile sensors for travel time estimation on a freeway
By:
References
- 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.
- Ban, X., Herring, R., Margulici, J. and Bayen, A. (2009). Optimal sensor placement for travel time estimation, Transportation and Traffic Theory 2009: 697–721.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Danczyk, A., Di, X. and Liu, H. (2016). A probabilistic optimization model for allocating freeway sensors, Transportation Research C 67: 378–398.
- 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.
- 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.
- 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.
- 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.
- 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.
- Gentili, M. and Mirchandani, P. (2012). Locating sensors on traffic networks: Models, challenges and research opportunities, Transportation Research C 24: 227–255.
- Gentili, M. and Mirchandani, P. (2018). Review of optimal sensor location models for travel time estimation, Transportation Research C 90: 74–96.
- He, S. (2013). A graphical approach to identify sensor locations for link flow inference, Transportation Research B 51: 65–76.
- Hong, Z. and Fukuda, D. (2012). Effects of traffic sensor location on traffic state estimation, Procedia-Social and Behavioral Sciences 54(2290): 1186–1196.
- 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.
- 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.
- 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.
- 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.
- 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.
- Li, X. and Ouyang, Y. (2011). Reliable sensor deployment for network traffic surveillance, Transportation Research B 45: 218–231.
- 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.
- Liu, H. and Danczyk, A. (2009). Optimal sensor locations for freeway bottleneck identification, Computer-Aided Civil and Infrastructure Engineering 24(8): 535–550.
- 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.
- Meng, T., Jing, X., Yan, Z. and Pedrycz, W. (2020). A survey on machine learning for data fusion, Information Fusion 57: 115–229.
- 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.
- Ng, M. (2013). Partial link flow observability in the presence of initial sensors: Solution without path enumeration, Transportation Research E 51: 62–66.
- 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.
- Park, H. and Haghani, A. (2015). Optimal number and location of bluetooth sensors considering stochastic travel time prediction, Transportation Research C 55: 203–216.
- 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.
- 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.
- 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.
- Yang, Y. and Fan, Y. (2015). Data dependent input control for origin-destination demand estimation using observability analysis, Transportation Research B 78: 385–403.
- 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.
- 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.
- 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.
- 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.
Language: English
Page range: 271 - 287
Submitted on: Dec 1, 2020
Accepted on: Apr 10, 2021
Published on: Jul 8, 2021
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2021 Weiwei Sun, Liang Shen, Hu Shao, Pengjie Liu, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.