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Synergy evaluation model of container multimodal transport based on BP neural network

  • S.I. : SPIoT 2020
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Abstract

With the rapid development of economic globalization, the trade of various countries has become increasingly close and thus the rapid growth of container transportation business. The rational choice of transportation routes and intermodal transportation methods has become a hot topic in this field. In order to improve the efficiency of intermodal transportation and reduce the cost of intermodal transportation, this paper studies the evaluation of the synergy effect of container multimodal transportation based on the BP neural network algorithm. First, it is determined that the multi-attribute decision-making method is used to comprehensively evaluate the synergy effect of container multimodal transportation, and the data are normalized. Select the appropriate hidden layer nodes, use Matlab to determine the learning rate, select the logsig function for the network transfer function, select Traingdx as the training function, and establish a container multimodal transport synergy evaluation model based on BP neural network. Secondly, the model is solved, and the problem is transformed into finding the shortest route from Q to P without exceeding the cost and time constraints. Then carry on the simulation experiment, use Matlab to solve the transportation time and total cost between cities. Experimental data show that when the number of iterations is 800, the algorithm begins to converge; at the fifth training, the error between the actual output and the expected output is only 0.0004; in route B, the time of multimodal transportation is 48.18% less than that of single transportation, and the cost is saved by 50.02%. This shows that the container multimodal transport synergy evaluation model based on BP neural network can accurately evaluate the effects, and container multimodal transport can indeed improve transportation efficiency and save costs.

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References

  1. Ren T, Liu S, Yan G et al (2016) Temperature prediction of the molten salt collector tube using BP neural network. Renew Power Gener IET 10(2):212–220

    Article  Google Scholar 

  2. Brcic D, Kos S, Vukic L (2017) Comparison of external costs in multimodal container transport chain. Promet-Traffic Transp 29(2):243–244

    Article  Google Scholar 

  3. Kim NS, Park B, Lee KD (2016) A knowledge based freight management decision support system incorporating economies of scale: multimodal minimum cost flow optimization approach. Inf Technol Manag 17(1):81–94

    Article  Google Scholar 

  4. Chai J, Hayashibe M (2020) Motor synergy development in high-performing deep reinforcement learning algorithms. IEEE Robot Autom Lett 5(2):1271–1278

    Article  Google Scholar 

  5. Liu T, Yin S (2017) An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation. Multimed Tools Appl 76(9):11961–11974

    Article  Google Scholar 

  6. Zheng D, Qian ZD, Liu Y et al (2018) Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network. Constr Build Mater 158:614–623

    Article  Google Scholar 

  7. Liu Z, Xu Y, Qiu C et al (2019) A novel support vector regression algorithm incorporated with prior knowledge and error compensation for small datasets. Neural Comput Applic 31:4849–4864

    Article  Google Scholar 

  8. Wang J, Fang K, Pang W et al (2017) Wind power interval prediction based on improved pso and bp neural network. J Electr Eng Technol 12(3):989–995

    Article  Google Scholar 

  9. Xu Z, Cheng C, Sugumaran V (2020) Big data analytics of crime prevention and control based on image processing upon cloud computing. J Surveill Secur Saf 1:16–33

    Google Scholar 

  10. Zhou Q, Zheng Xu, Yen NY (2019) User sentiment analysis based on social network information and its application in consumer reconstruction intention. Comput Hum Behav 100:177–183

    Article  Google Scholar 

  11. Wang W, Tang R, Li C et al (2018) A BP neural network model optimized by mind evolutionary algorithm for predicting the ocean wave heights. Ocean Eng 162:98–107

    Article  Google Scholar 

  12. Clausen U, Kaffka J (2016) Development of priority rules for handlings in inland port container terminals with simulation. J Simul 10(2):95–102

    Article  Google Scholar 

  13. Corman F, Viti F, Negenborn RR (2017) Equilibrium models in multimodal container transport systems. Flex Serv Manuf J 29(1):125–153

    Article  Google Scholar 

  14. Ambrosino D, Sciomachen A (2016) A capacitated hub location problem in freight logistics multimodal networks. Optim Lett 10(5):875–901

    Article  MathSciNet  Google Scholar 

  15. Riessen BV, Negenborn R, Dekker R (2016) Real-time container transport planning with decision trees based on offline obtained optimal solutions. Decis Support Sys 89:1–16

    Article  Google Scholar 

  16. Othman MR, Jeevan J, Rizal S (2016) The Malaysian Intermodal terminal system: the implication on the malaysian maritime cluster. Int J E Navig Marit Econ 4:46–61

    Google Scholar 

  17. Engler E, Gewies S, Bany P et al (2018) Trajectory-based multimodal transport management for resilient transportation. Transport Prob 13(1):81–96

    Article  Google Scholar 

  18. Elyseu F (2017) Evolution of container sea transport. Civ Eng 25(4):38–44

    Google Scholar 

  19. Skačkauskienė I, Kazlauskienė E, Katinienė A (2017) Modelling knowledge synergy evaluation. Monten J Econ 13(1):35–49

    Article  Google Scholar 

  20. Yu Y, Li B, Wang C et al (2019) Evaluation and synergy of material and energy in the smelting process of ferrochrome pellets in steel belt sintering-submerged arc furnace. Energy 179:792–804

    Article  Google Scholar 

  21. Mecha AC, Onyango MS, Ochieng A et al (2017) Evaluation of synergy and bacterial regrowth in photocatalytic ozonation disinfection of municipal wastewater. Sci Total Environ 601–602:626–635

    Article  Google Scholar 

  22. Joshi G (2018) Synergy via redundancy: boosting service capacity with adaptive replication. ACM Sigmetrics Perform Eval Review 45(3):21–28

    Article  Google Scholar 

  23. Morris JH, Oliver T, Kroll T et al (2017) Physical activity participation in community dwelling stroke survivors: synergy and dissonance between motivation and capability. A Qual study Physiother 103(3):311–321

    Google Scholar 

  24. Lasam G, Ramirez R (2017) Concomitant left atrial myxoma and patent foramen ovale: is it an evolutional synergy for a cerebrovascular event? Cardiol Res 8(1):26–29

    Article  Google Scholar 

  25. Wegene JD, Thanikaivelan P (2018) Synergy of organic nanoclay and inorganic phosphates for fire retardant leather applications. J Am Leather Chem Assoc 113(11):371–379

    Google Scholar 

Download references

Acknowledgements

This work was supported by Projects of the scientific research plan in 2020 in Shaanxi province department of education “Profit Distribution Research of Multimodal Transport based on Competition Game” (20JZ016), Central College Fund of Chang’an University Project Research and Practice of "Theory and Method of Transportation Planning" online teaching Reform (300103102047). This work also was supported by Science research project of education department of Jilin province, Project number: JJKH20190774SK, JJKH20200206SK; Program for Innovative Research Team of Jilin Engineering Normal University.

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Correspondence to Haiwen Wang.

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Zhu, W., Wang, H. & Zhang, X. Synergy evaluation model of container multimodal transport based on BP neural network. Neural Comput & Applic 33, 4087–4095 (2021). https://doi.org/10.1007/s00521-020-05584-1

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