Science China Technological Sciences

, Volume 61, Issue 5, pp 782–790 | Cite as

Research on optimized GA-SVM vehicle speed prediction model based on driver-vehicle-road-traffic system

  • YuFang Li
  • MingNuo Chen
  • XiaoDing Lu
  • WanZhong Zhao


The accurate prediction of vehicle speed plays an important role in vehicle’s real-time energy management and online optimization control. However, the current forecast methods are mostly based on traffic conditions to predict the speed, while ignoring the impact of the driver-vehicle-road system on the actual speed profile. In this paper, the correlation of velocity and its effect factors under various driving conditions were firstly analyzed based on driver-vehicle-road-traffic data records for a more accurate prediction model. With the modeling time and prediction time considered separately, the effectiveness and accuracy of several typical artificial-intelligence speed prediction algorithms were analyzed. The results show that the combination of niche immunegenetic algorithm-support vector machine (NIGA-SVM) prediction algorithm on the city roads with genetic algorithm-support vector machine (GA-SVM) prediction algorithm on the suburb roads and on the freeway can sharply improve the accuracy and timeliness of vehicle speed forecasting. Afterwards, the optimized GA-SVM vehicle speed prediction model was established in accordance with the optimized GA-SVM prediction algorithm at different times. And the test results verified its validity and rationality of the prediction algorithm.


driver-vehicle-road-traffic data records vehicle speed forecast optimized GA-SVM mode 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang S, Xiong R. Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Appl Energy, 2015, 155: 68–78CrossRefGoogle Scholar
  2. 2.
    Chindamo D, Economou J T, Gadola M, et al. A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle. Proc Inst Mech Eng Part D-J Auto Eng, 2014, 228: 1034–1050CrossRefGoogle Scholar
  3. 3.
    Baker D, Asher Z, Bradley T. Investigation of vehicle speed prediction from neural network fit of real world driving data for improved engine on/off control of the EcoCAR3 hybrid Camaro. SAE Technical Papers, 2017Google Scholar
  4. 4.
    Mozaffari L, Mozaffari A, Azad N L. Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads. Eng Sci Tech Int J, 2015, 18: 150–162CrossRefGoogle Scholar
  5. 5.
    Rumschlag G, Palumbo T, Martin A, et al. The effects of texting on driving performance in a driving simulator: The influence of driver age. Accident Anal Prev, 2015, 74: 145–149CrossRefGoogle Scholar
  6. 6.
    Birrell S A, Young M S. The impact of smart driving aids on driving performance and driver distraction. Trans Res Part F-Traffic Psychol Behav, 2011, 14: 484–493CrossRefGoogle Scholar
  7. 7.
    Garber N J, Gadiraju R. Factors affecting speed variance and its influence on accidents. Trans Res Record, 1989, 1213: 64–71Google Scholar
  8. 8.
    Hu W. Raising the speed limit from 75 to 80 mph on Utah rural interstates: Effects on vehicle speeds and speed variance. J Saf Res, 2017, 61: 83–92CrossRefGoogle Scholar
  9. 9.
    Ashley W S, Strader S, Dziubla D C, et al. Driving blind: Weatherrelated vision hazards and fatal motor vehicle crashes. Bull Amer Meteorol Soc, 2015, 96: 755–778CrossRefGoogle Scholar
  10. 10.
    Beratis I N, Pavlou D, Papadimitriou E, et al. Mild cognitive impairment and driving: Does in-vehicle distraction affect driving performance? Accident Anal Prevention, 2017, 103: 148–155CrossRefGoogle Scholar
  11. 11.
    Zuin D, Ortiz H, Boromei D, et al. Motor vehicle crashes and abnormal driving behaviours in patients with dementia in Mendoza, Argentina. Eur J Neurol, 2002, 9: 29–34CrossRefGoogle Scholar
  12. 12.
    Alvarez R, López A, De la Torre N. Evaluating the effect of a driver’s behaviour on the range of a battery electric vehicle. Proc Inst Mech Eng Part D-J Auto Eng, 2015, 229: 1379–1391CrossRefGoogle Scholar
  13. 13.
    Lawoyin S, Fei D Y, Bai O. Accelerometer-based steering-wheel movement monitoring for drowsy-driving detection. Proc Inst Mech Eng Part D-J Auto Eng, 2015, 229: 163–173CrossRefGoogle Scholar
  14. 14.
    Jägerbrand A K, Sjöbergh J. Effects of weather conditions, light conditions, and road lighting on vehicle speed. Springerplus, 2016, 5: 505CrossRefGoogle Scholar
  15. 15.
    Davies G M, Patel D. The influence of car and driver stereotypes on attributions of vehicle speed, position on the road and culpability in a road accident scenario. Legal Criminol Psychol, 2005, 10: 45–62CrossRefGoogle Scholar
  16. 16.
    Jiang B, Fei Y. Vehicle speed prediction by two-level data driven models in vehicular networks. Trans Intel Trans Sys, 2017, 99: 1–9Google Scholar
  17. 17.
    Lai W K, Kuo T H, Chen C H. Vehicle speed estimation and forecasting methods based on cellular floating vehicle data. Appl Sci, 2016, 6: 47CrossRefGoogle Scholar
  18. 18.
    Vapnik V N, Chervonenkis A Y A. On the uniform convergence of relative frequencies of events to their probabilities. Theor Probab Appl, 1971, 1: 264–280CrossRefzbMATHGoogle Scholar
  19. 19.
    Vapnik V N. The Nature of Statistical Learning Theory. Berlin: Springer-Verlag, 1995. 988–999CrossRefzbMATHGoogle Scholar
  20. 20.
    Cortes C, Vapnik V. Support-Vector Networks. Dordrecht: Kluwer Academic Publishers, 1995. 273–297zbMATHGoogle Scholar
  21. 21.
    Holland J H. Adaptation in natural and artificial systems. Quart Rev Biol, 1975, 6: 126–137Google Scholar
  22. 22.
    Horn J. The nature of niching: Genetic algorithms and the evolution of optimal, cooperative populations. Ph.D. thesis, Urbana, University of Illinois, Illinois, Genetic Algorithm Lab, 1997.Google Scholar
  23. 23.
    Chun J S, Jung H K, Hahn S Y. A Study on comparison of optimization performances between immune algorithm and other heuristic algorithms. In: IEEE Transactions on Magnetics, 1998, 34: 2972–2975CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • YuFang Li
    • 1
  • MingNuo Chen
    • 1
  • XiaoDing Lu
    • 1
  • WanZhong Zhao
    • 1
  1. 1.College of Energy & Power EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

Personalised recommendations