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Reference: [1] LouXia, HeBiao, LiuZhisuo. Study on optimal control of vehicle path at intersections in the context of Internet of vehicles [J]. Computer Simulation, 2017, 34(4):166-171. [2] Baouche F, Billot R, Trigui R, et al. Efficient Allocation of Electric Vehicles Charging Stations: Optimization Model and Application to a Dense Urban Network[J]. IEEE Intelligent Transportation Systems Magazine, 2017, 6(3):33-43. [3] Han Y, Moutarde F. Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using Non-Negative Tensor Factorization[J]. International Journal of Intelligent Transportation Systems Research, 2016, 14(1):36-49. [4] Nguyen-Trong K, Nguyen-Thi-Ngoc A, Nguyen-Ngoc D, et al. Optimization of municipal solid waste transportation by integrating GIS analysis, equation-based, and agent-based model[J]. Waste Management, 2017, 59:14. [5] Tiratanapakhom T, Kim H, Nam D, et al. Braess’ paradox in the uncertain demand and congestion assumed Stochastic Transportation Network Design Problem[J]. Ksce Journal of Civil Engineering, 2016, 20(7):1-10. [6] Heidari M, Hosseini-Motlagh S M, Nikoo N. A subway planning bi-objective multi-period optimization model integrating timetabling and vehicle scheduling: a case study of Tehran[J]. Transportation, 2018:1-27. [7] Ardjmand E, Young W A, Weckman G R, et al. Applying genetic algorithm to a new bi-objective stochastic model for transportation, location, and allocation of hazardous materials[J]. Expert Systems with Applications, 2016, 51(C):49-58. [8] Zheng Y, Li S E, Li K, et al. Platooning of Connected Vehicles With Undirected Topologies: Robustness Analysis and Distributed H-infinity Controller Synthesis[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, PP(99):1-12. [9] Zhang J, Li Y. Analysis and Optimization of the Milk-run Model in Automotive Industry : An Automobile Manufacturing Company Case Study(Case Study)[J]. Journal of Japan Industrial Management Association, 2017, 61(3):214-221. [10] Tattini J, Ramea K, Gargiulo M, et al. Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model[J]. Applied Energy, 2018, 212:265-282.
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Tsuruta Institute of Medical Information Technology
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