Skip to main content
Log in

DB-Corouting: Density Based Coordinated Vehicle Rerouting in Smart Environment

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Congestion control is a widely accepted domain in Intelligent Transportation System. Two approaches are commonly used to address the issue: either by controlling the traffic signals or by re-routing the vehicles in a congested state. However, the objective is to minimize the average travel time of the vehicles in a given road scenario. Choosing shortest path could be a solution. But the vehicles, following the shortest path, may face congestion if the decision is done in an un-coordinated manner. This could be due to non-inclusion of crucial decision parameter(s) and lack of cooperative decision on the decisive parameters of the concerned traffic scenario. There are efforts to include the density of the road segments within decision variables. The novelty of the proposed solution is to address the adaptive nature of the density parameter and considers effectively in the solution proposal. The solution considers the effect of density in a nearby road segment is more than the rare one. The introduction of the adaptive nature of this decision variable models the real road network more accurately and subsequent solution is more effective. Exhaustive experimentation has been done, considering various use cases. The proposed Density Based Coordinated Vehicle Rerouting, coined as “DB-Corouting” algorithm is simulated through “SUMO” and “Open Street Map” and the necessary finding ensures the effectiveness of the proposed solution in terms of selected metrics such as average traveling time, average waiting time, Traffic satisfaction Index etc.. The proposed solution outperforms the comparable solutions in terms of the selected metrics and always offers an efficient solution irrespective of traffic distribution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. INRIX: Economic and environmental impact of traffic congestion in europe and the us. http://inrix.com/economic-environment-cost-congestion/,2014http://inrix.com/economic-environment-cost-congestion/,2014 (2014)

  2. Bauza, R., Gozalvez, J.: Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. J. Netw. Comput. Appl. 36(5), 1295 (2013). https://doi.org/10.1016/j.jnca.2012.02.007. http://linkinghub.elsevier.com/retrieve/pii/S1084804512000628

    Article  Google Scholar 

  3. Pan, J., Popa, I.S., Zeitouni, K., Borcea, C.: Proactive vehicular traffic rerouting for lower travel time. IEEE Trans. Veh. Technol. 62(8), 3551 (2013). https://doi.org/10.1109/TVT.2013.2260422

    Article  Google Scholar 

  4. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., Scholl, H.J.: Understanding smart cities: An integrative framework. In: Proceedings of the Annual Hawaii International Conference on System Sciences. pp. 2289–2297. https://doi.org/10.1109/HICSS.2012.615 (2012)

  5. Doolan, R., Muntean, G.M: VANET-enabled eco-friendly road characteristics-aware routing for vehicular traffic. In: IEEE Vehicular technology conference. https://doi.org/10.1109/VTCSpring.2013.6692807 (2013)

  6. WAZE. Waze app bugs and issues

  7. Cao, Z., Jiang, S., Zhang, J., Guo, H.: A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion. IEEE Trans. Intell. Transp. Syst. 18(7), 1958 (2017). https://doi.org/10.1109/TITS.2016.2613997

    Article  Google Scholar 

  8. Pan, J.S., Popa, I.S., Borcea, C.: DIVERT: A distributed vehicular traffic re-routing system for congestion avoidance. IEEE Trans. Mob. Comput. 16(1), 58 (2017). https://doi.org/10.1109/TMC.2016.2538226

    Article  Google Scholar 

  9. Li, Z., Shahidehpour, M., Bahramirad, S., Khodaei, A.: Optimizing Traffic Signal Settings in Smart Cities. IEEE Trans. Smart Grid 8(5), 2382 (2017). https://doi.org/10.1109/TSG.2016.2526032

    Article  Google Scholar 

  10. Li, Z., Al Hassan, R., Shahidehpour, M., Bahramirad, S., Khodaei, A.: A Hierarchical Framework for Intelligent Traffic Management in Smart Cities. IEEE Trans. Smart Grid 3053(c), 1 (2017). https://doi.org/10.1109/TSG.2017.2750542. http://ieeexplore.ieee.org/document/8030345/

    Google Scholar 

  11. Garcia-Nieto, J., Olivera, A.C., Alba, E.: Optimal cycle program of traffic lights with particle swarm optimization. IEEE Trans. Evol. Comput. 17(6), 823 (2013). https://doi.org/10.1109/TEVC.2013.2260755

    Article  Google Scholar 

  12. Lin, W.H., Wang, C.: An enhanced 0-1 mixed-integer LP formulation for traffic signal control. IEEE Trans. Intell. Transp. Syst. 5(4), 238 (2004). https://doi.org/10.1109/TITS.2004.838217

    Article  Google Scholar 

  13. Jang, K., Kim, H., Jang, I.G.: Traffic Signal Optimization for Oversaturated Urban Networks: Queue Growth Equalization. IEEE Trans. Intell. Transp. Syst. 16(4), 2121 (2015). https://doi.org/10.1109/TITS.2015.2398896

    Article  Google Scholar 

  14. Sánchez-Medina, J.J., Galán-Moreno, M.J., Rubio-Royo, E.: Traffic signal optimization in la Almozara District in Saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing. IEEE Trans. Intell. Transp. Syst. 11(1), 132 (2010). https://doi.org/10.1109/TITS.2009.2034383

    Article  Google Scholar 

  15. Qureshi, K.N., Abdullah, A.H.: A Survey on Intelligent Transportation Systems. Middle-East J. Sci. Res. 15(5), 629 (2013). https://doi.org/10.5829/idosi.mejsr.2013.15.5.11215

    Google Scholar 

  16. Brennand, C.A., De Souza, A.M., Maia, G., Boukerche, A., Ramos, H., Loureiro, A.A., Villas, L.A.: An intelligent transportation system for detection and control of congested roads in urban centers. In: Proceedings - IEEE Symposium on Computers and Communications 2016-Febru, 663. https://doi.org/10.1109/ISCC.2015.7405590(2016)

  17. Garip, M.T., Gursoy, M.E., Reiher, P., Gerla, M.: Scalable reactive vehicle-to-vehicle congestion avoidance mechanism. In: 2015 12th Annual IEEE Consumer Communications and Networking Conference, CCNC 2015 pp. 943–948. https://doi.org/10.1109/CCNC.2015.7158103 (2015)

  18. Brennand, C.A.R.L., Souza, A.M.D., Maia, G., Boukerche, A., Ramos, H., Loureiro, A.A.F., Villas, L.A.: An Intelligent Transportation System for Detection and Control of Congested Roads in Urban Centers. In: 20th IEEE Symposium on Computers and Communication (ISCC), pp. 663–668 (2015)

  19. Fukumoto, J., Sirokane, N., Ishikawa, Y., Wada, T., Ohtsuki, K., Okada, H.: Analytic method for real-time traffic problems by using Contents Oriented Communications in VANET. In: ITST 2007 - 7th International Conference on Intelligent Transport Systems Telecommunications, Proceedings. pp. 40–45. https://doi.org/10.1109/ITST.2007.4295830 (2007)

  20. Nadeem, T., Dashtinezhad, S., Liao, C., Iftode, L.: TrafficView. ACM SIGMOBILE Mobile Comput. Commun. Rev. 8(3), 6 (2004). https://doi.org/10.1145/1031483.1031487. http://portal.acm.org/citation.cfm?doid=1031483.1031487

    Article  Google Scholar 

  21. Wischhof, L., Ebner, A., Rohling, H.: Information dissemination in self-organizing intervehicle networks. IEEE Trans. Intell. Transp. Syst. 6(1), 90 (2005). https://doi.org/10.1109/TITS.2004.842407

    Article  Google Scholar 

  22. Miller, J.: Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture. In: IEEE Intelligent Vehicles Symposium Proceedings pp. 715–720. https://doi.org/10.1109/IVS.2008.4621301 (2008)

  23. Lin, L., Osafune, T.: Road congestion detection by distributed vehicle-to vehicle communication systems (2011)

  24. Mathew, T.V.: Chapter 38: Coordinated Traffic Signal (2014)

  25. Mathew, T.V.: Chapter 34 : Design Priciples of Traffic Signal. http://nptel.ac.in/courses/105101008/downloads/cete_34.pdf (2014)

  26. Transportation Research Board: Highway capacity manual. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000746(2000)

  27. Krajzewicz, D., Bonert, M., Wagner, P.: Transportation Research 1–5 . http://en.scientificcommons.org/20058515 (2006)

  28. Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent Development and Applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5(3), 128 (2012). http://elib.dlr.de/80483/

    Google Scholar 

  29. Wegener, A., Piórkowski, M., Raya, M., Hellbrück, H., Fischer, S., Hubaux, J.P.: TraCI: An Interface for Coupling Road Traffic and Network Simulators. In: Proceedings of the 11th communications and networking simulation symposium on - CNS ’08 (2008), p. 155. https://doi.org/10.1145/1400713.1400740. http://portal.acm.org/citation.cfm?doid=1400713.1400740

  30. Haklay, M., Weber, P.: OpenStreetMap: User-Generated Street Maps. Pervasive Computing. IEEE Perv. Comput. 7(4), 12 (2008). https://doi.org/10.1109/MPRV.2008.80

    Article  Google Scholar 

  31. Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 55(5), 5597 (1997). https://doi.org/10.1103/PhysRevE.55.5597

    Article  Google Scholar 

  32. De Souza, A.M., Yokoyama, R.S., Botega, L.C., Meneguette, R.I., Villas, L.A.: SCORPION: A solution using cooperative rerouting to prevent congestion and improve traffic condition. In: Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Se (September 2016) 497. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.71 (2015)

Download references

Acknowledgment

This publication is an outcome of the R&D work undertaken project the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pratik Dutta.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Visvesvaraya PhD Scheme for Electronics & IT.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dutta, P., Khatua, S. & Choudhury, S. DB-Corouting: Density Based Coordinated Vehicle Rerouting in Smart Environment. Int. J. ITS Res. 19, 539–556 (2021). https://doi.org/10.1007/s13177-021-00261-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-021-00261-6

Keywords

Navigation