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Research on Optimization of Expressway Logistics Path Based on the Advantages of Multimodal Transport in the Environment of Internet of Things

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Abstract

Multimodal transportation is a transportation process completed by connecting and transporting two or more vehicles, also known as combined transportation. Road transportation plays a unique role in the operation and development of the logistics industry, and has unique characteristics in many modes of transportation. This article aims to study the optimization of high-speed logistics routes and provide favorable conditions for the development of multimodal transport. In this article, the node time window defined by multi-transmission network scheduling plays a role, and the transmission time is studied. It analyzes at the lowest cost, searches for the best multimodal road route, and provides decision support and route optimization for multimodal transport through two sets of experiments. According to the survey results, more than 80% of expressways have logistics problems, and 60% of expressways have failed to successfully complete transportation operations. By measuring the exchange time of nodes in different modes of transportation and the transportation time between nodes, it can provide effective support for optimizing multimodal transportation routes, effectively promoting the development of the logistics industry and developing the country. Economics and experimental results prove that this procedure is highly applicable.

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References

  1. Bocewicz, G., Banaszak, Z., & Nielsen, I. (2019). Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraints. Annals of Operations Research, 273(1), 1–25.

    MathSciNet  MATH  Google Scholar 

  2. Zheng, N., Dantsuji, T., & Wang, P. (2017). Macroscopic approach for optimizing road space allocation of bus lanes in multimodal urban networks through simulation analysis. Transportation Research Record Journal of the Transportation Research Board, 2651(1), 42–51.

    Article  Google Scholar 

  3. Engler, E., Gewies, S., & Banyś, P. (2018). Trajectory-based multimodal transport management for resilient transportation. Transport Problems, 13(1), 81–96.

    Article  Google Scholar 

  4. Yang, D., & Wu, P. (2021). E-commerce logistics path optimization based on a hybrid genetic algorithm[J]. Complexity, 2021(4), 1–10.

    MathSciNet  Google Scholar 

  5. Nielsen, I., Wjcik, R., & Bocewicz, G. (2016). Multimodal processes optimization subject to fuzzy operation time constraints: declarative modeling approach A preliminary version was presented at the 12th international conference on distributed computing and artificial intelligence, June 3–5, 2015, Sp. Frontiers of Information Technology and Electronic Engineering, 17(4), 338–347.

    Article  Google Scholar 

  6. Theissing, S. (2016). Supervision in multi-modal transportation system. Canadian Theatre Review, 143(143), 24–28.

    Google Scholar 

  7. Sinkevičius, G., Ginevicius, R., & Jarašūnienė, A. (2016). Analysis of clusterization and networking processes in developing intermodal transportation. Ekonomia I Zarzadzanie, 8(2), 63–71.

    Article  Google Scholar 

  8. Terrin, J. J. (2016). High speed railway hubs in European medium-sized cities: The case of the ENTER.HUB Network. Open Transportation Journal, 10(Suppl-1, M11), 119–123.

    Article  Google Scholar 

  9. Scheurer, J. (2016). How intermediate capacity modes provide accessibility and resilience in metropolitan transit networks: Insights from a global study of 19 cities. Journal of Public Transportation, 19(4), 107–125.

    Article  Google Scholar 

  10. Gillespie, W., & Fahrenwald, P. (2017). Transit access measure: Incorporating walk and drive access. Transportation Research Record Journal of the Transportation Research Board, 2653(1), 82–92.

    Article  Google Scholar 

  11. Smirnov, A. V., Kashevnik, A. M., & Ponomarev, A. (2016). Context-based infomobility system for cultural heritage recommendation: Tourist Assistant—TAIS. Personal and Ubiquitous Computing, 21(2), 1–15.

    Google Scholar 

  12. Кoндaкoв Bлaдимиp Пeтpoвич. (2017). O диффepeнциpyeмocти oтoбpaжeний и cтpoeнии пpocтpaнcтв гoлoмopфныx фyнкций нa бecкoнeчнoмepныx пpocтpaнcтвax. zhurnal neorganicheskoj khimii 1(3), 28.

  13. Péra, T. G., & Caixeta-Filho, J. V. (2016). Impact of vertically integrated road transport on Brazilian sugar export logistics: A mathematical programming application. International Business Research, 9(12), 85.

    Article  Google Scholar 

  14. Memon, A. A., Meng, M., Wong, Y. D., et al. (2017). Impact of integrated multimodal traveler information on auto commuter’s mode switching propensity. Open Transportation Journal, 11(1), 20–30.

    Article  Google Scholar 

  15. Li, Z., Zhao, Li., & Yuan, Z. (2016). Highway transportation efficiency evaluation for Beijing-Tianjin-Hebei region based on advanced DEA model. International Review for Spatial Planning and Sustainable Development, 4(3), 36–44.

    Article  Google Scholar 

  16. Qi, H.-L., Tian, W.-P., & Zhao, F. (2016). Risk assessment of snow disaster for trunk highway transportation in Shaanxi, China. Natural Hazards, 85(1), 523–536.

    Article  Google Scholar 

  17. Noh, S. J., Shim, D., & Jeon, M. (2016). Adaptive Sliding-window strategy for vehicle detection in highway environments. IEEE Transactions on Intelligent Transportation Systems, 17(2), 323–335.

    Article  Google Scholar 

  18. Xing-hua, Hu., Long, B., & Zhu, X.-N. (2016). Signal-planning optimization for bus priority signal intersections on the basis of green loss equilibrium. Journal of Highway and Transportation Research and Development, 10(2), 59–67.

    Google Scholar 

  19. Morris, E. A., Brown, J. R., & Taylor, B. D. (2016). Negotiating a financial package for freeways: How California’s Collier-Burns Highway act helped pave the way for the era of the American interstate highway. Transportation Research Record Journal of the Transportation Research Board, 2552(2552), 16–22.

    Article  Google Scholar 

  20. Chen, C., Kriz, K. A., & Wang, Q. (2016). How does the health of transportation infrastructure affect state credit ratings? An empirical analysis. Public Finance Review, 44(4), 149–154.

    Google Scholar 

  21. Shou-feng, Lu., Wang, J., & Xue, Z.-G. (2016). Traffic analysis and OD travel time matrix based on two-fluid model. Journal of Highway and Transportation Research and Development, 10(3), 78–84.

    Google Scholar 

  22. Han, X., Wang, Di., & Liu, Y.-S. (2016). Valid path searching of an interrupted urban rail transit network. Journal of Highway and Transportation Research and Development, 10(2), 79–83.

    Google Scholar 

  23. Feng, Z., Zhan, J., & Liu, J. (2017). Development and validation of highway emergency lane illegal occupancy scale for Chinese drivers. Transportation Research Record Journal of the Transportation Research Board, 2634(1), 43–49.

    Article  Google Scholar 

  24. Atabi, F., Ghorbani, R., & Jabbari, M. (2017). Assessment of safe distance for five toxic materials commonly in the accidents of chemical road transportation using ALOHA and PHAST software and CEI index: Case of Tehran-Qazvin highway. Iran Occupational Health, 14(4), 36–50.

    Google Scholar 

  25. Jannat, G. E., Henning, T. F. P., & Zhang, C. (2016). Road section length variability on pavement management decision making for Ontario, Canada, highway systems. Transportation Research Record Journal of the Transportation Research Board, 2589(2589), 87–96.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Management, Northwestern Polytechnical University, Xi’an, 710072, China

    Wen Xu

  2. Guang Xi Computer Center Co., Ltd, Nanning, 530022, China

    Mei Rong

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  1. Wen Xu
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  2. Mei Rong
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All authors contributed to the study conception and design, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wen Xu.

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Xu, W., Rong, M. Research on Optimization of Expressway Logistics Path Based on the Advantages of Multimodal Transport in the Environment of Internet of Things. Wireless Pers Commun 126, 1981–1997 (2022). https://doi.org/10.1007/s11277-021-08755-y

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