Advertisement

Prediction of Travel Time Estimation Accuracy in Connected Vehicle Environments

  • Osama A. Osman
  • Sherif Ishak
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)

Abstract

Effective management of transportation networks requires accurate travel time information that is largely determined by the quality of collected real-time traffic data. In connected vehicle (CV) environments, wherein equipped vehicles may be the primary source of reliable travel time data, accuracy and reliability of travel time estimates present a challenge due to the low market penetration at the early deployment stages of CV technology. The absence of ground truth data presents another challenge for quantifying the accuracy and reliability of travel time estimates. Therefore, CV infrastructure should be well planned in transportation networks to achieve acceptable and reliable estimates of travel times. Recent research shows that the accuracy of travel time estimates is influenced by traffic density, CV market penetration, and transmission range. These factors also impact the vehicle-to-vehicle and vehicle-to-infrastructure communication stability in a transportation network. This suggests correlation between the accuracy and reliability of travel time estimates and the communication stability. This study develops regression models to measure the accuracy and reliability of travel time estimates as a function of communication stability. Such models can help transportation planners assess the anticipated accuracy and reliability of travel time estimates in CV environments, as well as make better infrastructure investment decisions to ensure an acceptable level of accuracy and reliability of travel time estimates.

References

  1. 1.
    Osman, O., Ishak, S.: Accounting for traffic density and market penettration in a newly developed connectivity robustness model for connected vehicles environments. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C. (2014)Google Scholar
  2. 2.
    Yousefi, S., Altman, E., El-Azouzi, R., Fahy, M.: Analytical model for connectivity in vehicular ad hoc networks. IEEE Trans. Veh. Technol. 57(6), 3341–3356 (2008)Google Scholar
  3. 3.
    Panichpapiboon, S., Pattara-Atikom, W.: Connectivity requirements for self-organizing traffic information systems. IEEE Trans. Veh. Technol. 57(6), 3333–3340 (2008)Google Scholar
  4. 4.
    Kafsi, M., Papadimitratos, P., Dousse, O., Alpcan, T., Hubaux, J.P.: VANET connectivity analysis. Presented at IEEE Workshop on Automotive Networking and Applications (2008)Google Scholar
  5. 5.
    Yousefi, S., Fathy, M.: Metrics for performance evaluation of safety applications in vehicular ad hoc networks. Transport 23(4), 291–298 (2008)Google Scholar
  6. 6.
    Wisitpongphan, N., Bai, F., Mudalige, P., Tonguz, O.K.: On the routing problem in disconnected vehicular ad-hoc networks. Presented at 26th IEEE International Conference on Computer Communications, INFOCOM 2007 (2007)Google Scholar
  7. 7.
    Manvi, S.S., Kakkasageri, M.S., Mahapurush, C.V.: Performance analysis of AODV, DSR, and swarm intelligence routing protocols in vehicular ad hoc network environment. Presented at International Conference on Future Computer and Communication, ICFCC (2009)Google Scholar
  8. 8.
    El-atty, S.M.A., Stamatiou, G.K.: Performance analysis of Multihop connectivity in VANET. Presented at the 7th International Symposium on Wireless Communication Systems (ISWCS) (2010)Google Scholar
  9. 9.
    Reinders, R., Van Eenennaam, M., Karagiannis, G., Heijenk, G.: Contention window analysis for beaconing in VANETs. Presented at the 7th International Wireless Communications and Mobile Computing Conference (IWCMC) (2011)Google Scholar
  10. 10.
    Yang, Y., Bagrodia, R.: Evaluation of VANET-based advanced intelligent transportation systems. Proceedings of the Sixth ACM International Workshop on Vehicular Internetworking, pp. 3–12 (2009)Google Scholar
  11. 11.
    Roess, R.P., Prassas, E.S., McShane, W.R.: Traffic Engineering, 3rd edn. Prentice-Hall, Englewood Cliffs (2004)Google Scholar
  12. 12.
    Spanos, D.P., Murray, R.M.: Robust connectivity of networked vehicles. Presented at 43rd IEEE Conference on Decision and Control (2004)Google Scholar
  13. 13.
    Chen, C., Kinafar, J., Edara, P.: New snapshot generation protocol for travel time estimation in a connected vehicle environment. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C (2014)Google Scholar
  14. 14.
    Comert, G., Cetin, M.: queue length estimation from probe vehicle location and the impact of sample size. Eur. J. Oper. Res. 197(1), 196–202 (2009)Google Scholar
  15. 15.
    Osman, O.A., Bakhit, P.R., Ishak, S.: Queue estimation at signalized intersections using basic safety messages in connected vehicle environments. Paper presented at the Transportation Research Board 95th Annual Meeting (2016)Google Scholar
  16. 16.
    Arogate, J., Christofa, E., Xuan, Y., Skabardonis, A.: Estimation of arterial measures of effectiveness with connected vehicle data. Presented at 91st Annual Meeting of the Transportation Research Board, Washington, D.C (2012)Google Scholar
  17. 17.
    Taleb, T., Benslimane, A., Ben Letaief, K.: Toward an effective risk-conscious and collaborative vehicular collision avoidance system. IEEE Trans. Veh. Technol. 59(3), 1474–1486 (2010)Google Scholar
  18. 18.
    Kinafar, J., Edara, P.: Placement of Roadside Equipment (RSE) in connected vehicle environment for travel time estimation. Presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Louisiana State UniversityBaton RougeUSA

Personalised recommendations