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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
Article

Abstract

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.

Keywords

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

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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

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