Optimum Vehicle Flows in a Fully Automated Vehicle Network

Joerg Schweizer, Tiziano Parriani, Emiliano Traversi, Federico Rupi


This paper provides a novel assignment method and a solution algorithm that allows to determine the optimum vehicle flows in a fully automated vehicle network. This assignment method incorporates the following specific features: (1) optimal redistribution of occupied and unoccupied vehicles; (2) inter-vehicle spacing is adapted to meet the minimum safe distance criteria on congested link, (no collision in the worst failure case); (3) trip-time minimization of all traffic participants by a centralized vehicle routing. The latter feature allows the realization of a so called system optimum solution, which minimizes the total time of all trips. This assignment method is applied to two, topologically different, test networks at different travel demand levels, in order to determine: the share of unoccupied vehicle, the minimum number of required vehicles, the share of congested links, the lost trip-time of occupied vehicles due to the presents of unoccupied vehicles. Furthermore, the advantage of a centralized vehicle routing is quantified by comparing the total trip-times of a scenario using a system optimum solution with a scenario applying the user equilibrium solution, without considering unoccupied vehicle flows. Regarding the investigated scenarios, the share of unoccupied vehicle flows with centralized vehicle routing in a uniform, random demand scenario is approximately 11%􀀀14%.


  1. NHTSA'S Office of Behavioral Safety Research 2012 Some theoretical aspects of road traffic research. facts DOT HS 811 611. http://wwwnrd.nhtsa.dot.gov/CATS/index.aspx
  2. Wardrop J.G. (1952) Some theoretical aspects of road traffic research. Proc. Inst. Civ. Eng. 2: pp. 325-378.
  3. Frank H. and Wolfe P. (1956) An Algorithm for quadratic programming, Naval Research Logistics Quarterly, 3, pp. 95-110.
  4. Beckmann M.J. (1965) On optimal tolls for highways, tunnels and bridges Vehicular Traffic Science, American Elsevier, New York (1965), pp. 331341.
  5. Yang H., Huang H. J. (1998), Principle of marginal-cost pricing: How does it work in a general network? Transportation Research, 32A (1998), pp. 45-54
  6. Patriksson M. (1994), The Traffic Assignment Problem: Models and Methods, VSP, Utrecht, the Netherlands.
  7. Nie Y., Zhang H.M., Lee D. (2004) Models and algorithms for the traffic assignment problem with link capacity constraints Transportation Research Part B 38, pp. 285312
  8. Xu M., Qu Y., Gao Z. (2008) Implementing Frank-Wolfe Algorithm under Different Flow Update Strategies and Line Search Technologies, J Transpn Sys Eng & IT, 8(3), pp. 14-22
  9. Cascetta E. (2001) Transportation systems engineering: theory and methods. Kluwer Academic Publisher
  10. Inouea S., Maruyama T. (2012) Computational Experience on Advanced Algorithms for User Equilibrium Traffic Assignment Problem and Its Convergence Error, Procedia - Social and Behavioral Sciences 43, pp. 445- 456
  11. Tomlin J.A. (1966) Minimum-Cost Multicommodity Network Flows. Operations Research, pp. 45-51.
  12. Lübbecke M.E., Desrosiers J. (2005) Selected topics in column generation. Operations Research, 53.6: pp. 1007- 1023.
  13. Frangioni A., Gallo G. (1999) A bundle type dual-ascent approach to linear multicommodity min-cost flow problems. INFORMS Journal on Computing, 11.4: pp. 370-393.
  14. Andréasson, I (1994). Vehicle Distribution in Large Personal Rapid Transit Systems. Transportation Research Record, No. 1451, pp 95-99, Transportation Research Board, Washington, D.C.
  15. Schweizer J., Danesi A., Rupi F.,Traversi E. (2012) Comparison of static vehicle flow assignment methods and microsimulations for a personal rapid transit network, J. Adv. Transp.; 46: pp. 340-350
  16. Lees-Miller J.D, Hammersley J.S, Wilson R.E (2010) Theoretical Maximum Capacity as a Benchmark for Empty Vehicle Redistribution in Personal Rapid Transit, Journal of the Transportation Research Board, No. 2146, pp 76-83, Transportation Research Board, ISSN: 0361-1981
  17. Koskinen K., Luttinen T., Kosonen I. (2007) Developing a microscopic simulator for personal rapid transit (PRT) systems, Transportation Research Board 86th Annual Meeting, Washington, D.C.
  18. Horowitz R. and Varaiya P. (2000) Control design of automated highway system, IEEE Proc., vol. 88, no. 7.
  19. Li P., Alvarez L., and Horowitz R. (1997) “AHS safe control laws for platoon leaders,” IEEE Trans. Contr. Syst. Technol., vol. 5, no. 6, pp. 615-628.
  20. J. Schweizer (2004) Non-linear feedback control for short time headways based on constant- safety vehiclespacing. In IEEE Intelligent Vehicles Symposium, pp. 167 - 172.

Paper Citation

in Harvard Style

Schweizer J., Parriani T., Traversi E. and Rupi F. (2016). Optimum Vehicle Flows in a Fully Automated Vehicle Network . In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-185-4, pages 195-202. DOI: 10.5220/0005863101950202

in Bibtex Style

author={Joerg Schweizer and Tiziano Parriani and Emiliano Traversi and Federico Rupi},
title={Optimum Vehicle Flows in a Fully Automated Vehicle Network},
booktitle={Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},

in EndNote Style

JO - Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Optimum Vehicle Flows in a Fully Automated Vehicle Network
SN - 978-989-758-185-4
AU - Schweizer J.
AU - Parriani T.
AU - Traversi E.
AU - Rupi F.
PY - 2016
SP - 195
EP - 202
DO - 10.5220/0005863101950202