A Backpressure Framework Applied to Road Traffic Routing for Electric Vehicles

Evangelos D. Spyrou, Dimitris Mitrakos


Electric vehicles (EVs) emerged in the transport domain, due to their energy efficiency and clean energy that they utilise. The electric vehicle routing problem is essentially a problem of selecting a set of minimum cost routes, while the demand of the customers is achieved. Route cost metrics include energy consumption and driving time. In this work, we model the electric vehicle routing problem using a wireless network methodology, namely the backpressure framework. The penalty imposed to every route includes the driving time of each road. We derive a weight as a function of the road queue backpressure and the driving time of a car. The next route for our EV is the one that has the highest weight. It turns out that this methodology leads to faster routes in that there are often roads with accidents or traffic jams, even though they are in the shortest path of the route to the destination. We present results via simulations, which verify the fact that backpressure is an efficient algorithm to be applied to electric vehicle routing.


  1. Abousleiman, R. and Rawashdeh, O. (2014). Energy efficient routing for electric vehicles using particle swarm optimization. Technical report, SAE Technical Paper.
  2. Afroditi, A., Boile, M., Theofanis, S., Sdoukopoulos, E., and Margaritis, D. (2014). Electric vehicle routing problem with industry constraints: trends and insights for future research. Transportation Research Procedia, 3:452-459.
  3. Artmeier, A., Haselmayr, J., Leucker, M., and Sachenbacher, M. (2010). The optimal routing problem in the context of battery-powered electric vehicles. In Workshop CROCS at CPAIOR-10, 2nd International Workshop on Constraint Reasoning and Optimization for Computational Sustainability.
  4. Baheti, R. and Gill, H. (2011). Cyber-physical systems. The impact of control technology, 12:161-166.
  5. Baum, M., Dibbelt, J., Gemsa, A., and Wagner, D. (2014). Towards route planning algorithms for electric vehicles with realistic constraints. Computer ScienceResearch and Development, pages 1-5.
  6. Bondy, J. A. and Murty, U. S. R. (1976). Graph theory with applications, volume 290. Macmillan London.
  7. Bruglieri, M., Pezzella, F., Pisacane, O., and Suraci, S. (2015). A variable neighborhood search branching for the electric vehicle routing problem with time windows. Electronic Notes in Discrete Mathematics, 47:221-228.
  8. BUREAU, O. P. R. (1964). Traffic assignment manual. US Department of Commerce.
  9. Dantzig, G. B. and Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1):80-91.
  10. de Weerdt, M. M., Stein, S., Gerding, E. H., Robu, V., and Jennings, N. R. (2015). Intention-aware routing of electric vehicles.
  11. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1):269- 271.
  12. Emadi, A. (2011). Transportation 2.0. Power and Energy Magazine, IEEE, 9(4):18-29.
  13. Jahn, O., Möhring, R. H., Schulz, A. S., and Stier-Moses, N. E. (2005). System-optimal routing of traffic flows with user constraints in networks with congestion. Operations research, 53(4):600-616.
  14. Kopetz, H. (2011). Internet of things. In Real-time systems, pages 307-323. Springer.
  15. Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4):408-416.
  16. Moeller, S., Sridharan, A., Krishnamachari, B., and Gnawali, O. (2010). Routing without routes: the backpressure collection protocol. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pages 279-290. ACM.
  17. Nagata, Y., Bräysy, O., and Dullaert, W. (2010). A penaltybased edge assembly memetic algorithm for the vehicle routing problem with time windows. Computers & Operations Research, 37(4):724-737.
  18. Neely, M. J., Modiano, E., and Li, C.-P. (2008). Fairness and optimal stochastic control for heterogeneous networks. Networking, IEEE/ACM Transactions on, 16(2):396-409.
  19. Neely, M. J. and Urgaonkar, R. (2008). Opportunism, backpressure, and stochastic optimization with the wireless broadcast advantage. In Signals, Systems and Computers, 2008 42nd Asilomar Conference on, pages 2152-2158. IEEE.
  20. Patriksson, P. (1994). The traffic assignment problem: models and methods.
  21. Schneider, M., Stenger, A., and Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation Science, 48(4):500-520.
  22. Touati-Moungla, N. and Jost, V. (2012). Combinatorial optimization for electric vehicles management. Journal of Energy and Power Engineering, 6(5).

Paper Citation

in Harvard Style

Spyrou E. and Mitrakos D. (2016). A Backpressure Framework Applied to Road Traffic Routing for Electric Vehicles . In Proceedings of the Sixth International Symposium on Business Modeling and Software Design - Volume 1: BMSD, ISBN 978-989-758-190-8, pages 235-240. DOI: 10.5220/0006224302350240

in Bibtex Style

author={Evangelos D. Spyrou and Dimitris Mitrakos},
title={A Backpressure Framework Applied to Road Traffic Routing for Electric Vehicles},
booktitle={Proceedings of the Sixth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,},

in EndNote Style

JO - Proceedings of the Sixth International Symposium on Business Modeling and Software Design - Volume 1: BMSD,
TI - A Backpressure Framework Applied to Road Traffic Routing for Electric Vehicles
SN - 978-989-758-190-8
AU - Spyrou E.
AU - Mitrakos D.
PY - 2016
SP - 235
EP - 240
DO - 10.5220/0006224302350240