Traffic Safety Assessment of Deceleration Function Area Based on TTC Model

  • Weiwei QiEmail author
  • Zhexuan Wang
  • Bin Shen
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 185)


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.


Deceleration functional area Potential collision risk Traffic safety assessment 



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.


  1. 1.
    Yulong, P.: Road Traffic Safety, pp. 249. People’s Communications Press, Beijing (2007)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    Violanti, J.M., Marshal, J.R.: Cellular phones and traffic accidents: an epidemiological approach. Accid. Anal. Prev. 28(2), 265–270 (1996)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Abdu, R., Shinar, D., Meiran, N.: Situational (state) anger and driving. Transp. Res. Part F: Traffic Psychol. Behav. 15(5), 575–580 (2012)CrossRefGoogle Scholar
  6. 6.
    Brodsky, H., Hakkert, A.S.: Risk of a road accident in rainy weather. Accid. Anal. Prev. 20(3), 161–176 (1988)CrossRefGoogle Scholar
  7. 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. 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)CrossRefGoogle Scholar
  9. 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. 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. 11.
    Meng, Q., Qu, X.: Estimation of vehicle crash frequencies in road tunnels. Accid. Anal. Prev. 48, 254–263 (2012)CrossRefGoogle Scholar
  12. 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. Scholar
  13. 13.
    Kuang, Y., Qu, X., Wang, S.: A tree-structured crash surrogate measure for freeways. Accid. Anal. Prev. 77, 137–148 (2015)CrossRefGoogle Scholar
  14. 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. 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)CrossRefGoogle Scholar
  16. 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. 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. 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. 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)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 22.
    Hirst, S., Graham, R.: The format and presentation of collision warnings. Ergonomics Saf. Intell. Driver Interfaces 2, 203–219 (1997)Google Scholar
  23. 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

Copyright information

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

Authors and Affiliations

  1. 1.School of Civil Engineering and TransportationSouth China University of TechnologyGuangzhouChina

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