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Considering Congestion Costs and Driver Behaviour into Route Optimisation Algorithms in Smart Cities

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Smart Cities (Smart-CT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10268))

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Abstract

Congestion costs have been excluded from the study of traditional vehicle routing problems until very recently. However, with our urban areas experiencing higher levels of traffic congestion, with the increase in on-demand deliveries, and with the growth of intelligent transport systems and smart cities, researchers are raising awareness on the impact that traffic congestion and driver behaviour has for urban logistics. This paper studies the evolution of the vehicle routing problem, focusing on how traffic congestion costs and driver behaviour effects have been considered so far, and analysing how the research community has to deal with this challenge.

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References

  1. Eksioglu, B., Vural, A., Reisman, A.: The vehicle routing problem: a taxonomic review. Comput. Ind. Eng. 57(4), 1472–1483 (2009)

    Article  Google Scholar 

  2. Dantzig, G., Fulkerson, R., Johnson, S.: Solution of a large-scale travelling salesman problem. Oper. Res. 2, 393–410 (1954)

    Google Scholar 

  3. Dantzig, G., Ramser, J.H.: The truck dispatching problem. Manage. Sci. 6(1), 80–91 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  4. Clarke, G., Wright, J.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12, 568–581 (1964)

    Article  Google Scholar 

  5. Levin, A.: Scheduling and fleet routing models for transportation systems. Transp. Sci. 5(3), 232 (1971)

    Article  MathSciNet  Google Scholar 

  6. Wilson, N., Sussman, J.: Implementation of computer algorithms forthe dial-a-bus system. Bull. Oper. Res. Soc. Am. 19(1) (1971)

    Google Scholar 

  7. Liebman, J.: Mathematical models for solid waste collection and disposal. In: 38th national meeting of the Operations Research Society of America Bulletin of the Operations Research Society of America, vol. 18, no. 2 (1970)

    Google Scholar 

  8. Golden, B., Magnanti, T., Nguyan, H.: Implementing vehicle routing algorithms. Networks 7(2), 113–148 (1972)

    Article  MATH  Google Scholar 

  9. Juan, A.A., Faulin, J., Grasman, S.E., Rabe, M., Figueira, G.: A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems. Oper. Res. Perspect. 2, 62–72 (2015)

    Article  MathSciNet  Google Scholar 

  10. United Nations, World Urbanization Prospects: The 2014 Revision (2015)

    Google Scholar 

  11. Marks, D., Stricker, R.: Routing for public service vehicles. Locate Full-Text (Opens in a New Window) 97(UP2), 165–178 (1971)

    Google Scholar 

  12. Daganzo, C.F.: Distance travelled to visit N points with a maximum of C stops per vehicle: an analytical model an application. Transp. Sci. 18(4), 331–350 (1984)

    Article  MathSciNet  Google Scholar 

  13. Van Woensel, T., Kerbache, L., Peremans, H., Vandaele, N.: Vehicle routing with dynamic travel times: a queueing approach. Eur. J. Oper. Res. 186(3), 990–1007 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  14. Srivatsa Srinivas, S., Gajanand, M.: Vehicle routing problem and driver behaviour: a review and framework for analysis. Transp. Rev., 1–22 (2016)

    Google Scholar 

  15. Kim, G., Ong, Y., Cheong, T., Tan, P.: Solving the dynamic vehicle routing problem under traffic congestion. IEEE Trans. Intell. Transp. Syst. 17(8), 2367–2380 (2016)

    Article  Google Scholar 

  16. Lecluyse, C., Sorensen, K., Peremans, H.: A network-consistent time-dependent travel time layer for routing optimization problems. Eur. J. Oper. Res. 3(1), 395–413 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Huang, Y., Zhao, L., Van Woensel, T., Gross, J.: Time-dependent vehicle routing problem with path flexibility. Transp. Res. Part B Methodol. 95(1), 169–195 (2017)

    Article  Google Scholar 

  18. Jabali, O., Van Woensel, T., De Kok, A.: Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Prod. Oper. Manage. 21(6), 1060–1074 (2012)

    Article  Google Scholar 

  19. Lai, M., Yang, H., Yang, S., Zhao, J., Xu, J.: Cyber-physical logistics system-based vehicle routing optimization. J. Ind. Manage. Optimization 10(3), 701–715 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. Cirovic, G., Pamucar, D., Bozanic, D.: Green logistic vehicle routing problem: routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Syst. Appl. 41(9), 4245–4258 (2014)

    Article  Google Scholar 

  21. Nha, V., Djahel, S., Murphy, J.: A comparative study of vehicles’ routing algorithms for route planning in Smart Cities. In: 1st International Workshop on Vehicular Traffic Management for Smart Cities, VTM 2012 (2012)

    Google Scholar 

  22. Novaes, A., Bez, E., Burin, P., Aragao, D.: Dynamic milk-run OEM operations in over-congested traffic conditions. Comput. Ind. Eng. 88(11), 326–340 (2015)

    Article  Google Scholar 

  23. Conrad, R., Figliozzi, M.: Algorithms to quantify impact of congestion on time-dependent real-world Urban Freight distribution networks. Transp. Res. Rec. 2168, 104–113 (2010)

    Article  Google Scholar 

  24. Du, M., Yi, H.: Research on multi-objective emergency logistics vehicle routing problem under constraint conditions. J. Ind. Eng. Manage. 6(1), 258–266 (2013)

    MathSciNet  Google Scholar 

  25. Ehmke, J., Steinert, A., Mattfeld, D.: Advanced routing for city logistics service providers based on time-dependent travel times. J. Comput. Sci. 3(4), 193–205 (2012)

    Article  Google Scholar 

  26. Du, M., Yi, H.: Multi-objective emergency logistics vehicle routing problem: ‘Road congestion’, ‘unilateralism time window’. In: 2nd International Conference on Logistics, Informatics and Service Science, Beijing (2013)

    Google Scholar 

  27. Polimeni, A., Vitetta, A.: Vehicle routing in urban areas: An optimal approach with cost function calibration. Transportmetrica B 2(1), 1–19 (2014)

    Google Scholar 

  28. Liang, X., Zhang, Y.: Study on vehicle routing problem with travel time coefficients. In: International Conference of Chinese Logistics and Transportation Professionals, Chengdu (2008)

    Google Scholar 

  29. Kok, A., Hans, E., Schutten, J.: Vehicle routing under time-dependent travel times: The impact of congestion avoidance. Comput. Oper. Res. 39(5), 910–918 (2012)

    Article  MATH  Google Scholar 

  30. Liang, Z.: Research of blocking factor combined with improved ant colony algorithm in VRP. In: 7th International Conference on Computational Intelligence and Security, Sanya (2011)

    Google Scholar 

  31. Xiao, J., Lu, B.: Vehicle routing problem with soft time windows. Adv. Intell. Soft Comput. 1, 317–322 (2012)

    Google Scholar 

  32. Gupta, A., Heng, C., Ong, Y., Tan, P., Zhang, A.: A generic framework for multi-criteria decision support in eco-friendly urban logistics systems. Expert Syst. Appl. 71(1), 288–300 (2017)

    Article  Google Scholar 

  33. Muñoz-Villamizar, A., Montoya-Torres, J., Herazo-Padilla, N.: Mathematical programming modeling and resolution of the location-routing problem in urban logistics. Ingenieria y Universidad 18(2), 271–289 (2014)

    Article  Google Scholar 

  34. Soysal, M., Bloemhof-Ruwaard, J., Bektas, T.: The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations. Int. J. Prod. Econ. 164(1), 366–378 (2015)

    Article  Google Scholar 

  35. Islam, S., Oslen, T.: Truck-sharing challenges for hinterland trucking companies: a case of the empty container truck trips problem. Bus. Process Manage. J. 20(2), 290–334 (2014)

    Article  Google Scholar 

  36. You, S., Chow, J., Ritchie, S.: Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics. Transportmetrica A: Transp. Sci. 12(7), 650–673 (2016)

    Article  Google Scholar 

  37. Jin, X., Tang, Y., Xu, Q.: Routing optimization of city distribution considering access restriction. Appl. Mech. Mater. 505–506, 959–966 (2014)

    Article  Google Scholar 

  38. Danielis, R., Rotaris, L., Marcucci, E.: Urban freight policies and distribution channels. Eur. Transp. (Trasporti Europei) 46, 114–146 (2010)

    Google Scholar 

  39. Zhu, X., Liu, T., Qiao, P.: The design and implementation of GIS logistics distribution system considering traffic information. Appl. Mech. Mater. 380–384, 4671–4675 (2013)

    Article  Google Scholar 

  40. Yin, Y., Liu, T., Tang, L., Li, Q.: Vehicle routing problem research based on information utility theory. Gummi, Fasern, Kunststoffe 69(14), 2084–2090 (2016)

    Google Scholar 

  41. Figliozzi, M.: The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels: results from a case study in Portland, Oregon. Transp. Res. Part C Emerg. Technol. 19(5), 766–778 (2011)

    Article  Google Scholar 

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Acknowledgments

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P and TRA2015-71883-REDT), FEDER, and the Ibero-American Program for Science and Technology for Development (CYTED2014-515RT0489).

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Authors and Affiliations

  1. Department of Statistics and OR, Institute of Smart Cities, Public University of Navarra, Pamplona, Spain

    Pablo Alvarez, Iosu Lerga, Adrian Serrano & Javier Faulin

Authors
  1. Pablo Alvarez
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  2. Iosu Lerga
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  3. Adrian Serrano
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  4. Javier Faulin
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Corresponding author

Correspondence to Pablo Alvarez .

Editor information

Editors and Affiliations

  1. University of Malaga, Malaga, Spain

    Enrique Alba

  2. University of Malaga, Malaga, Spain

    Francisco Chicano

  3. University of Malaga, Malaga, Spain

    Gabriel Luque

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Alvarez, P., Lerga, I., Serrano, A., Faulin, J. (2017). Considering Congestion Costs and Driver Behaviour into Route Optimisation Algorithms in Smart Cities. In: Alba, E., Chicano, F., Luque, G. (eds) Smart Cities. Smart-CT 2017. Lecture Notes in Computer Science(), vol 10268. Springer, Cham. https://doi.org/10.1007/978-3-319-59513-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-59513-9_5

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  • Print ISBN: 978-3-319-59512-2

  • Online ISBN: 978-3-319-59513-9

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