Fuel Efficient Truck Platooning with Time Restrictions and Multiple Speeds Solved by a Particle Swarm Optimisation

  • Abtin NourmohammadzadehEmail author
  • Sven Hartmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)


In this paper, the problem of driving vehicles behind each other in close proximity as a file to reduce the total fuel consumption, called fuel-efficient platooning (FEP), is investigated. Some real-life attitudes like time restrictions and multiple allowable speeds for vehicles are taken into account. Linear mathematical formulations are presented for the FEP problem, which are coded in GAMS and solved by the GUROBI solver. Since the problem has a high computational complexity, an alternative evolutionary solution approach with Particle Swarm Optimisation (PSO) is proposed. An appropriate application procedure is given, which converts the continuous solution space of PSO into the routing, time scheduling and speed adjustment. The performance of our PSO is tested with some generated samples including up to 1000 vehicles on the graph of Chicago road network. The results verify the goodness of our PSO in terms of solution quality and time.


Vehicle platooning Fuel consumption reduction Linear modelling GAMS/GUROBI Particle swarm optimisation (PSO) 


  1. 1.
    Schittler, M.: State-of-the-art and emerging truck engine technologies for optimized performance, emissions and life cycle costs. In: 9th Diesel Emissions Reduction Conference, August 2003, Rhode Island, USA, August 2003Google Scholar
  2. 2.
    Schroten, A., Warringa, G., Bles, M.: Marginal abatement cost curves for heavy duty vehicles. In: Background report. CE Delft, Delft, Netherlands, July 2012Google Scholar
  3. 3.
    Bonnet, C., Fritz, H.: Fuel consumption reduction in a platoon: experimental results with two electronically coupled trucks at close spacing. In: Intelligent Vehicle Technology - SP-1558Google Scholar
  4. 4.
    Kianfar, R., Falcone, P., Fredriksson, J.: A control matching model predictive control approach to string stable vehicle platooning. Control. Eng. Pract. 45, 163–173 (2015)CrossRefGoogle Scholar
  5. 5.
    Alam, A., Mårtensson, J., Johansson, K.H.: Control engineering practice experimental evaluation of decentralized cooperative cruise control for heavy-duty vehicle platooning. Control. Eng. Pract. 38, 11–25 (2015)CrossRefGoogle Scholar
  6. 6.
    Gao, S., Lim, A., Bevly, D.: An empirical study of DSRC V2V performance in truck platooning scenarios. Digit. Commun. Netw. 2(4), 233–244 (2016)CrossRefGoogle Scholar
  7. 7.
    Bergenhem, C., Hedin, E., Skarin, D.: Vehicle-to-vehicle communication for a platooning system. Procedia Soc. Behav. Sci. 48, 1222–1233 (2012)CrossRefGoogle Scholar
  8. 8.
    Liang, K.Y., Deng, Q., Mårtensson, J., Ma, X., Johansson, K.H.: The influence of traffic on heavy-duty vehicle platoon formation. In: Intelligent Vehicles Symposium (IV), pp. 150–155. IEEE, June 2015Google Scholar
  9. 9.
    van de Hoef, S., Johansson, K.H., Dimarogonas, D.V.: Computing feasible vehicle platooning opportunities for transport assignments. IFAC-PapersOnLine 49(3), 43–48 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Heikoop, D.D., de Winter, J.C.F., van Arem, B., Stanton, N.A.: Effects of platooning on signal-detection performance, workload, and stress: a driving simulator study. Appl. Ergon. 60, 116–127 (2017)CrossRefGoogle Scholar
  11. 11.
    Li, B.: Stochastic modeling for vehicle platoons (II): statistical characteristics. Transp. Res. Part B Methodol. 95, 378–393 (2017)CrossRefGoogle Scholar
  12. 12.
    Dafflon, B., Gechter, F., Gruer, P., Koukam, A.: Vehicle platoon and obstacle avoidance: a reactive agent approach. IET Intell. Transp. Syst. 7(3), 257–264 (2013)CrossRefGoogle Scholar
  13. 13.
    El Zaher, M., Gechter, F., Hajjar, M., Gruer, P.: An interaction model for a local approach to vehicle platoons. Int. J. Veh. Auton. Syst. 13, 91–113 (2016)CrossRefGoogle Scholar
  14. 14.
    Yu, K., Liang, Q., Yang, J., Guo, Y.: Model predictive control for hybrid electric vehicle platooning using route information. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 230(9), 1273–1285 (2016)CrossRefGoogle Scholar
  15. 15.
    Yu, K., et al.: Model predictive control for hybrid electric vehicle platooning using slope information. IEEE Trans. Intell. Transp. Syst. 17(7), 1894–1909 (2016)CrossRefGoogle Scholar
  16. 16.
    Tadakuma, K., Doi, T., Shida, M., Maeda, K.: Prediction formula of aerodynamic drag reduction in multiple-vehicle platooning based on wake analysis and on-road experiments. SAE Int. J. Passeng. Cars Mech. Syst. 9(2), 645–656 (2016)Google Scholar
  17. 17.
    van de Hoef, S., Johansson, K.H., Dimarogonas, D.V.: Coordinating truck platooning by clustering pairwise fuel-optimal plans. In: ITSC 2015, October, pp. 408–415 (2015)Google Scholar
  18. 18.
    Liang, K.Y.: Coordination and routing for fuel-efficient heavy-duty vehicle platoon formation. Licentiate thesis. KTH Royal Institute of Technology, Sweden (2014)Google Scholar
  19. 19.
    Larsson, E., Sennton, G., Larson, J.: The vehicle platooning problem: computational complexity and heuristics. Transp. Res. Part C 60, 258–277 (2015)CrossRefGoogle Scholar
  20. 20.
    Kammer, C.: Coordinated heavy truck platoon routing using global and locally distributed approaches. Master thesis. KTH Royal Institute of Technology, Sweden (2013)Google Scholar
  21. 21.
    Larson, J., Munson, T., Sokolov, V.: Coordinated platoon routing in a metropolitan network, pp. 73–82 (2016)Google Scholar
  22. 22.
    Nourmohammadzadeh, A., Hartmann, S.: The fuel-efficient platooning of heavy duty vehicles by mathematical programming and genetic algorithm. In: Martín-Vide, C., Mizuki, T., Vega-Rodríguez, M.A. (eds.) TPNC 2016. LNCS, vol. 10071, pp. 46–57. Springer, Cham (2016). Scholar
  23. 23.
    Zhang, W., Sundberg, M., Karlström, A.: Platoon coordination with time windows: an operational perspective. Transp. Res. Procedia 27, 357–364 (2017). 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4–6 September 2017. Budapest, Hungary (2017)Google Scholar
  24. 24.
    Boysen, N., Briskorn, D., Schwerdfeger, S.: The identical-path truck platooning problem. Transp. Res. Part B Methodol. 109, 26–39 (2018)CrossRefGoogle Scholar
  25. 25.
    Luo, F., Larson, J., Munson, T.: Coordinated platooning with multiple speeds. Transp. Res. Part C Emerg. Technol. 90, 213–225 (2018)CrossRefGoogle Scholar
  26. 26.
    GAMS Development Corporation. General algebraic modeling system (GAMS) release 24.2.1 (2013)Google Scholar
  27. 27.
  28. 28.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995Google Scholar
  29. 29.
    Box, G.E.P., Draper, N.R.: Response Surfaces, Mixtures, and Ridge Analyses, 2nd edn. Wiley-Interscience, Hoboken(2007)Google Scholar
  30. 30.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of InformaticsClausthal University of TechnologyClausthal-ZellerfeldGermany

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