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Highway transportation optimization control system based on OD forecast information

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Abstract

In order to effectively solve the contradiction between supply and demand of road transportation and avoid waste of road facilities and construction resources, a scientific and systematic road network plan must be formulated. OD traffic flow reflects the distribution of traffic volume from the start point to the end point in the road network for a period of time. The purpose of this paper is to design and simulate a road transport optimization control system based on OD prediction information. This paper first converts the objective function of the variable speed multi-objective optimization model, and then determines the fitness function corresponding to each objective function. Then the linear normalization method is used to normalize the fitness function, and then the judgment matrix is established, and the weights are calculated by the square root method. Transform multi-objective optimization solutions into single-objective optimization solutions. Then the binary value of the decision variable speed limit value is encoded. Finally, crossover probability, mutation probability and genetic algorithm termination evolution algebra were determined. On this basis, the solution method of the optimal speed limit value is provided. After the variable speed limit was implemented for road transportation, the average speed value during peak hours increased from 82.0 to 85.9 km/h, an increase of 3.9 km/h; during the peak period, the vehicle speed increased from 83.3 to 85.6 km/h, an increase 2.3 km/h. The experimental data shows that the variable speed limit control strategy in the road transport optimization control system based on the OD prediction information has a significant effect on improving the driving efficiency of the fast lane.

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References

  • Chand HV, Karthikeyan J (2017) Recent survey on traffic optimization in intelligent transportation system. J Adv Res Dyn Control Syst 9(15):89–100

    Google Scholar 

  • Zhang B, Guo G, Wang LY et al (2018) Vehicle speed planning and control for fuel consumption optimization with traffic light state. Zidonghua Xuebao/Acta Autom Sin 44(3):461–470

    Google Scholar 

  • Besselink B, Turri V, Hoef SHVD et al (2016) Cyber-physical control of road freight transport. Proc IEEE 104(5):1128–1141

    Article  Google Scholar 

  • Lee H, Lee B (2018) Path planning based on probabilistic roadmap for initial deployment of marsupial robot team. J Inst Control 24(1):80–89

    Google Scholar 

  • Kaddoura I, Nagel K (2019) Congestion pricing in a real-world oriented agent-based simulation context. Res Transp Econ 74(MAY):40–51

    Article  Google Scholar 

  • Zhang L, Wu WM, Huang XW (2016) A dynamic optimization model for adjacent signalized intersection control systems based on the stratified sequencing method. J Highway Transp Res Dev 10(1):85–84

    Google Scholar 

  • Shengdong M, Zhengxian X, Yixiang T (2019) Intelligent traffic control system based on cloud computing and big data mining. IEEE Trans Ind Inform (99):1–1

  • Jovanovic A, Nikolic M, Teodorovic D (2017) Area-wide urban traffic control: a Bee Colony Optimization approach. Transp Res Part C Emerg Technol 77(APR.):329–350

  • Silva FA (2018) Modern electric, hybrid electric, and fuel cell vehicles, third edition [Book News]. IEEE Ind Electron Mag 12(4):46–48

  • Abe A, Iwamoto K, Okafuji Y et al (2016) Guidance and control system using constrained adaptive backstepping method for space transportation. Proc Inst Mech Eng Part G J Aerospace Eng 230(g11):2063–2076

    Article  Google Scholar 

  • Wang Y, Yue J, Dong Y et al (2016) Review on kernel based target tracking for autonomous driving. J Inf Process 24(1):49–63

    Google Scholar 

  • Chiou S-W (2018) A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods. Reliab Eng Syst Saf 175(JUL.):105–118

  • Xiong G, Shen D, Dong X et al (2017) Parallel transportation management and control system for subways. IEEE Trans Intell Transp Syst 18(7):1974–1979

    Article  Google Scholar 

  • Babaki A, Vaez-Zadeh S, Zakerian A (2019) Performance optimization of dynamic wireless EV charger under varying driving conditions without resonant information. IEEE Trans Veh Technol 68(11):10429–10438

    Article  Google Scholar 

  • Bichiou Y, Rakha HA (2019) Developing an optimal intersection control system for automated connected vehicles. IEEE Trans Intell Transp Syst 20(5):1908–1916

    Article  Google Scholar 

  • Han K, Liu H, Gayah VV (2016) A robust optimization approach for dynamic traffic signal control with emission considerations. Transp Res Part C 70(sep.):3–26

  • Tan D, Lu C (2016) The influence of the magnetic force generated by the in-wheel motor on the vertical and lateral coupling dynamics of electric vehicles. IEEE Trans Veh Technol 65(6):4655–4668

    Article  Google Scholar 

  • Manasra H, Toledo T (2019) Optimization-based operations control for public transportation service with transfers. Transp Res 105(AUG.):456–467

  • Ye BL, Wu W, Li L et al (2016) A hierarchical model predictive control approach for signal splits optimization in large-scale urban road networks. IEEE Trans Intell Transp Syst 17(8):2182–2192

    Article  Google Scholar 

  • Wang Y, Liu Z (2019) Equilibrium and optimization in a double-ended queueing system with dynamic control. J Adv Transp 2019(PT.2):6538265.1–6538265.13

  • Koehler S, Viehl A, Bringmann O et al (2017) Energy-efficiency optimization of torque vectoring control for battery electric vehicles. IEEE Intell Transp Syst Mag 9(3):59–74

    Article  Google Scholar 

  • Lin Y, Yin K, Dang Y et al (2019) Spatio-temporal LSTM for OD passenger demand prediction. Bjing Jiaotong Daxue Xuebao/J Bjing Jiaotong Univ 43(1):114–121

    Google Scholar 

  • De Clercq O, Hoste V (2016) All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch. Comput Lingus 42(2):1–35

    MathSciNet  Google Scholar 

  • Alliod C, Chemelle JA, Jacob G et al (2016) ames test prediction on high energy molecules by on-the-Fly QSAR (OTF-QSAR). Propellants Explos Pyrotech 42(1):24–35

    Article  Google Scholar 

  • Yao B, Yu B, Hu P et al (2016) An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot. Ann Oper Res 242(2):303–320

    Article  MathSciNet  Google Scholar 

  • Zhao N, Schofield N, Niu W (2017) Energy Storage System for a Port Crane Hybrid Power-Train. IEEE Trans Transp Electr 2(4):480–492

    Article  Google Scholar 

  • Osorio C, Wang C (2017) On the analytical approximation of joint aggregate queue-length distributions for traffic networks: a stationary finite capacity Markovian network approach. Transp Res Part B Methodol 95(JAN.):305–339

  • Wai RJ, Yao JX, Lee JD (2017) Backstepping fuzzy-neural-network control design for hybrid maglev transportation system. IEEE Trans Neural Netw Learn Syst 26(2):302–317

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Economic and Management, Shaanxi College of Communication Technology, Xi’an, 710018, Shaanxi, China

    Siru Chen

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  1. Siru Chen
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Chen, S. Highway transportation optimization control system based on OD forecast information. Int J Syst Assur Eng Manag 12, 748–756 (2021). https://doi.org/10.1007/s13198-021-01071-5

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  • DOI: https://doi.org/10.1007/s13198-021-01071-5

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