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Delivery Service in Congested Urban Areas

  • Victor ZakharovEmail author
  • Alexander Krylatov
  • Alexander Mugayskikh
Chapter
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Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 54)

Abstract

Nowadays logistical costs are significant in many developing countries, for instance, basing upon the last researches, in Russian Federation they make up 20 %. No doubts that heavy traffic congestions in modern urban areas impact directly on vehicle routing costs in road networks. Moreover, logistics companies are faced with lost profits since actually they serve less number of customers then they could planned, because of traffic congestions. Thus, contemporary approaches for planning delivery routes should necessarily take into account traffic information. Herewith, accuracy of such information is crucial since all systems for traffic congestions prediction are highly sensitive to input data. Wide spread of traffic counters, plate-scanning sensors, in-vehicle guide systems can certainly provide accurate data collection. However, emphasize that data collection only is fruitless without intellectual data processing. The present paper is devoted to development of optimization approach which incorporates modern data collection systems and contemporary mathematical tools to cope with comprehensive delivery planning under traffic congestions in road networks. Implementation of the approach to Saint Petersburg city demonstrates reduction of actual travel time of delivery vehicles in the congested road network by 8–16%.

Keywords

Delivery service Vehicle routing problem Congested road networks Traffic assignment problem 

Notes

Acknowledgements

The work is jointly supported by a grant from the Russian Science Foundation (Project No. 17-71-10069).

References

  1. 1.
    Beckmann MJ, McGuire CB, Winsten CB (1956) Studies in the economics of transportation. Yale University PressGoogle Scholar
  2. 2.
    Boyce David (2007) Future research on urban transportation network modeling. Regional Sci Urban Econ 37(4):472–481CrossRefGoogle Scholar
  3. 3.
    Castillo E, Menéndez JM, Jiménez P (2008) Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations. Transp Res Part B: Methodol 42(5):455–481CrossRefGoogle Scholar
  4. 4.
    Ehmke JF, Steinert A, Mattfeld DC (2012) Advanced routing for city logistics service providers based on time-dependent travel times. J Comput Sci 3(4):193–205CrossRefGoogle Scholar
  5. 5.
    Farahani RZ, Miandoabchi E, Szeto WY, Rashidi H (2013) A review of urban transportation network design problems. Eur J Oper Res 229(2):281–302MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hollander Y, Prashker JN (2006) The applicability of non-cooperative game theory in transport analysis. Transportation 33(5):481–496Google Scholar
  7. 7.
    Holodov YA, Holodov AS, Gasnikov AV, Morozov II, Tarasov VN (2010) Modeling of transport flows – modern problems and perspectives of its solution. Works of MIPT, pp. 152–162Google Scholar
  8. 8.
    Horowitz AJ (1991) Delay-volume relations for travel forecasting: based on the 1985 Highway Capacity Manual. US Department of Transportation, Federal Highway Administration, USAGoogle Scholar
  9. 9.
    Huang Y, Zhao L, Van Woensel T, Gross JP (2017) Time-dependent vehicle routing problem with path flexibility. Transp Res Part B: Methodol 95:169–195CrossRefGoogle Scholar
  10. 10.
    Ivanov D, Dolgui A, Sokolov B (2016) Robust dynamic schedule coordination control in the supply chain. Comput Ind Eng 94:18–31CrossRefGoogle Scholar
  11. 11.
    Krylatov A (2016) Network flow assignment as a fixed point problem. J Appl Indus Math 10(2):243–256MathSciNetCrossRefGoogle Scholar
  12. 12.
    Krylatov AY, Zakharov VV (2016) Competitive traffic assignment in a green transit network. Int Game Theory Rev 18(02):1640003MathSciNetCrossRefGoogle Scholar
  13. 13.
    Krylatov AY, Zakharov VV, Malygin IG (2015) Signal control in a congested traffic area. In: 2015 International Conference” Stability and Control Processes” in Memory of VI Zubov (SCP). IEEE, pp 475–478Google Scholar
  14. 14.
    Lukinskiy V, Lukinskiy V (2016) Evaluation of the influence of the logistic operations reliability on the total costs of a supply chain. Transp Telecommun J 17(4):307–313CrossRefGoogle Scholar
  15. 15.
    Patriksson M (1994) The traffic assignment problem - models and methods. Topics in transportation. VSP BV, UtrechtGoogle Scholar
  16. 16.
    Petrovich ML (2010) City-planning approach to the solution of transport problems. Transp Russ Fed 6(31):21–25Google Scholar
  17. 17.
    Scott C, Lundgren H, Thompson P (2011) Guide to outsourcing in supply chain management. Guide to supply chain management. Springer, pp 169–182Google Scholar
  18. 18.
    Wardrop JG (1952) Road paper. some theoretical aspects of road traffic research. Proc Inst Civ Eng 1(3):325–378CrossRefGoogle Scholar
  19. 19.
    Xie F, Levinson D (2009) Modeling the growth of transportation networks: a comprehensive review. Netw Spat Econ 9(3):291–307MathSciNetCrossRefGoogle Scholar
  20. 20.
    Zyryanov VV, Kocherga VG, Pozdnyakov MN (2011) Modern approaches to development of complex schemes of the traffic organization. Transp Russ Fed 1(32):54–59Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Victor Zakharov
    • 1
    Email author
  • Alexander Krylatov
    • 1
    • 2
  • Alexander Mugayskikh
    • 3
  1. 1.Saint Petersburg State UniversitySaint-PetersburgRussia
  2. 2.Institute of Transport Problems of the Russian Academy of SciencesSaint PetersburgRussia
  3. 3.GazpromMoscowRussia

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