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Affiliation of Author(s):能源与动力学院

Journal:SCIENCE CHINA-TECHNOLOGICAL SCIENCES

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

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.

ISSN No.:1674-7321

Translation or Not:no

Date of Publication:2018-05-01

Co-author:Chen, MingNuo,Lu, XiaoDing,Zhao Wanzhong

Correspondence Author:李玉芳