Skip to main content
SpringerLink
Log in

Eco-Driving for Public Transit in Cyber-Physical Systems Using V2I Communication

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

Abstract

This paper studies the eco-driving of public transit using the information of the traffic signal and the passengers obtained with V2I infrastructure. An optimal control is formulated to minimize the vehicle exhaust emission and avoid the extra stops at the intersection. The numerical algorithm is proposed by integrating the rolling horizon optimization with the branch and bound method. The optimal driving velocity is displayed to the drivers via mobile applications. A traffic network in Xicheng District, Beijing, is chosen to investigate the performance of the developed eco-driving approach.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Dib, W., et al.: Optimal energy management for an electric vehicle in eco-driving applications. Control. Eng. Pract. 29(6), 299–307 (2014)

    Article  Google Scholar 

  2. Miyatake, M., Kuriyama, M., Takeda, Y.: Theoretical study on eco-driving technique for an electric vehicle considering traffic signals. In: IEEE Conference on Power Electronics and Drive Systems, pp. 733–738 (2011)

  3. Mahler, G., Vahidi, A.: Reducing idling at red lights based on probabilistic prediction of traffic signal timings, pp. 6557–6562 (2012)

  4. Katsaros, K. , et al.: Performance study of a green light optimized velocity advisory (GLOSA) application using an integrated cooperative its simulation platform. In: International Wireless Communications and Mobile Computing Conference, pp. 918–923 (2011)

  5. De Nunzio, G., et al.: Eco-driving in urban traffic networks using traffic signal information. In: IEEE Conference on Decision and Control, pp. 892–898 (2013)

  6. Rakha, H., Kamalanathsharma, R.K.: Eco-driving at signalized intersections using V2I communication. In: International IEEE Conference on Intelligent Transportation Systems IEEE, pp. 341–346 (2011)

  7. Asadi, B., Vahidi, A.: Predictive cruise control: utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Trans. Control Syst. Technol. 19(3), 707–714 (2011)

    Article  Google Scholar 

  8. Ma, X.: Towards intelligent fleet management: local optimal speeds for fuel and emissions. In: International IEEE Conference on Intelligent Transportation Systems, pp. 2201–2206 (2013)

  9. Noori, H., Valkama, M.: Impact of VANET-based V2X communication using IEEE 802.11p on reducing vehicles traveling time in realistic large scale urban area, pp. 654–661 (2013)

  10. Li, J., Wu, G., Zou, N.: Investigation of the impacts of signal timing on vehicle emissions at an isolated intersection. Transp. Res. Part D: Transp. Environ. 16(16), 409–414 (2011)

  11. Jiménez-Palacios, L.: Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing. In: Massachusetts Institute of Technology, (1999)

  12. Furth, P.G., Muller, T.H.J.: Conditional bus priority at signalized intersections: better service with less traffic disruption. Transp. Res. Rec. 1731(1), 23–30 (2000)

    Article  Google Scholar 

  13. Zheng, Y., et al.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 222–235 (2014)

    Google Scholar 

  14. Koukoumidis, E., Martonosi, M., Peh, L.S.: Leveraging smartphone cameras for collaborative road advisories. IEEE Trans. Mob. Comput. 11(5), 707–723 (2011)

    Article  Google Scholar 

  15. Rakha, H., Dion, F., Snare, M.: Vehicle dynamics model for estimating maximum light-duty vehicle acceleration levels. Transp. Res. Board. 1883(1), (2004)

  16. Wang, T., et al.: Analysis of the bus rapid transit (BRT) platform stop delay and the boarding time. The Fifth Chinese Transport Forum, Shanghai (2008)

    Google Scholar 

  17. Daganzo, C.F.: A headway-based approach to eliminate bus bunching: systematic analysis and comparisons. Transp. Res. B Methodol. 43(10), 913–921 (2009)

    Article  Google Scholar 

  18. Hao, Y., et al.: Analysis of driving behavior and emission characteristics for diesel transit buses using PEMS’ measurements. J. Clin. Nurs. 17(20), 2741–2749 (2010)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their useful comments, which helped us to improve the quality of this work.

Author information

Authors and Affiliations

  1. School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, People’s Republic of China

    Liguo Zhang, Wenjia Liang & Xiuzheng Zheng

Authors
  1. Liguo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjia Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiuzheng Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liguo Zhang.

Additional information

This work is supported by National Natural Science Foundation of China (NSFC), NO.61374076

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Liang, W. & Zheng, X. Eco-Driving for Public Transit in Cyber-Physical Systems Using V2I Communication. Int. J. ITS Res. 16, 79–89 (2018). https://doi.org/10.1007/s13177-017-0139-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-017-0139-1

Keywords

Search

Navigation