Skip to main content
SpringerLink
Log in

Traffic Safety Assessment of Deceleration Function Area Based on TTC Model

  • Conference paper
  • First Online:
Smart Transportation Systems 2020

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 185))

  • 566 Accesses

Abstract

Sections of tunnel entrances, industrial and mining schools with deceleration function zones are high-traffic zones due to their special traffic conditions. The instability of the car during the deceleration process and the driver’s wrong deceleration operation may be important causes of traffic accidents. In order to improve the driving safety in the road deceleration function zone, the traffic flow at the entrance to Tianhe North Tunnel in Guangzhou City is taken as the research object, and we evaluate the traffic safety in the road deceleration function zone. The results show that speed standard deviation is a good predictor of potential risks, and speed standard deviation can be used to actively assess road safety. The research results help to further to optimize the driving behavior in the deceleration functional area and improve the safety of traffic flow in the deceleration functional area.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yulong, P.: Road Traffic Safety, pp. 249. People’s Communications Press, Beijing (2007)

    Google Scholar 

  2. Patel, M., Lala, S.K.L., Kavanagha, D., Rossiterb, P.: Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Syst. Appl. 38(6), 7235–7242 (2011)

    Article  Google Scholar 

  3. Violanti, J.M., Marshal, J.R.: Cellular phones and traffic accidents: an epidemiological approach. Accid. Anal. Prev. 28(2), 265–270 (1996)

    Article  Google Scholar 

  4. Yeo, M.V.M., Li, X.P., Shen, K., et al.: Can SVM be used for automatic EEG detection of drowsiness during car driving. Saf. Sci. 47(1), 115–124 (2009)

    Article  Google Scholar 

  5. Abdu, R., Shinar, D., Meiran, N.: Situational (state) anger and driving. Transp. Res. Part F: Traffic Psychol. Behav. 15(5), 575–580 (2012)

    Article  Google Scholar 

  6. Brodsky, H., Hakkert, A.S.: Risk of a road accident in rainy weather. Accid. Anal. Prev. 20(3), 161–176 (1988)

    Article  Google Scholar 

  7. Henry, E.L.: The effect of music volume on simulated interstate driving skills. The Florida State University College of Music, Florida (2006)

    Google Scholar 

  8. Sayed, R., Eskandarian, A.: Unobtrusive drowsiness detection by neural network learning of driver steering. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 215(9), 969–975 (2001)

    Article  Google Scholar 

  9. Makoto, U., Akio, K., Hirotsugu, M., et al.: Fatigue analysis based on synthesis of psychological and physiological responses measured simultaneously in follow-up driving. J. East. Asia Soc. Transp. Stud. (EASTS) 6, 3325–3340 (2005)

    Google Scholar 

  10. Chaovalit, P., Saiprasert, C., Pholprasit, T.: A method for driving event detection using SAX on smartphone sensors. In: Proceedings of 2013 13th International Conference on ITS Telecommunications, Tampere, pp. 450–455 (2013)

    Google Scholar 

  11. Meng, Q., Qu, X.: Estimation of vehicle crash frequencies in road tunnels. Accid. Anal. Prev. 48, 254–263 (2012)

    Article  Google Scholar 

  12. Xu, C., Yang, Y., Jin, S., Qu, Z., Hou, L.: Potential risk and its influencing factors for separated bicycle paths. Accid. Anal. Prev. 87, 59–67. https://doi.org/10.1016/j.aap.2015.11.014

  13. Kuang, Y., Qu, X., Wang, S.: A tree-structured crash surrogate measure for freeways. Accid. Anal. Prev. 77, 137–148 (2015)

    Article  Google Scholar 

  14. Derbel, O., Mourllion, B., Basset, M.: Extended safety descriptor measurements for relative safety assessment in mixed road traffic. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC) (2012)

    Google Scholar 

  15. Qu, X., Yu, Y., Zhou, M., Lin, C.T., Wang, X.: Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: a reinforcement learning based approach. Appl. Energy 257, 114030 (2020)

    Article  Google Scholar 

  16. Manit, K., Surachate, L.: Dissipation of traffic congestion using autonomous-based car-following model with modified optimal velocity. Phy. A: Statist. Mech. Appl. 123412 (2019)

    Google Scholar 

  17. Zhou, M., Qu, X., Jin, S.: On the impact of cooperative autonomous vehicles in improving freeway merging: A modified intelligent driver model based approach. IEEE Trans. Intell. Transp. Syst. 18(6), 1422–1428 (2017)

    Google Scholar 

  18. Sangmin, L., Younghoon, K., Hyungu K., Soon-Kyo L., Seokhyun C., Taesu C., Keeyong S., Jeehyuk P., Seoung B.K.: Intelligent traffic control for autonomous vehicle systems based on machine learning. Expert Syst. Appl. 144 (2020)

    Google Scholar 

  19. Zhou, M., Qu, X., Li, X.: A recurrent neural network based microscopic car following model to predict traffic oscillation. Transp. Res. Part C 84, 245–264 (2017)

    Article  Google Scholar 

  20. Zheng, L., Sayed, T.: A full Bayes approach for traffic conflict-based before-after safety evaluation using extreme value theory. Accid. Anal. Prev. 131, 308–315 (2019)

    Article  Google Scholar 

  21. Qu, X., Yang, Y., Liu, Z., Jin, S., Weng, J.: Potential crash risks of expressway on-ramps and off-ramps: A case study in Beijing China. Saf. Sci. 70, 58–62 (2014)

    Article  Google Scholar 

  22. Hirst, S., Graham, R.: The format and presentation of collision warnings. Ergonomics Saf. Intell. Driver Interfaces 2, 203–219 (1997)

    Google Scholar 

  23. Hogema, J.H., Janssen, W.H.: Effects of intelligent cruise control on driving behaviour : A simulator study. Soesterberg, The Netherlands. Report, TM-1996-C-12 (1996)

    Google Scholar 

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China under grant number 71701070, the Science and Technology Project of Guangzhou City under grant number 201804010466, the Fundamental Research Funds for the Central Universities under grant number 2019MS120.

Author information

Authors and Affiliations

  1. School of Civil Engineering and Transportation, South China University of Technology, 510641, Guangzhou, China

    Weiwei Qi, Zhexuan Wang & Bin Shen

Authors
  1. Weiwei Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhexuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiwei Qi .

Editor information

Editors and Affiliations

  1. Dept of Architecture and Civil Engineering, Chalmers University of Technology, Göthenburg, Västra Götalands Län, Sweden

    Xiaobo Qu

  2. Management Science and Engineering, Shanghai University, Shanghai, China

    Lu Zhen

  3. Bournemouth University, Poole, UK

    Robert J. Howlett

  4. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qi, W., Wang, Z., Shen, B. (2020). Traffic Safety Assessment of Deceleration Function Area Based on TTC Model. In: Qu, X., Zhen, L., Howlett, R.J., Jain, L.C. (eds) Smart Transportation Systems 2020. Smart Innovation, Systems and Technologies, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5270-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5270-0_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5269-4

  • Online ISBN: 978-981-15-5270-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation