Research on optimized GA-SVM vehicle speed prediction model based on driver-vehicle-road-traffic system-Reference-Cited by-同舟云学术

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

Published:2018-04-10 Issue:5 Volume:61 Page:782-790
ISSN:1674-7321
Container-title:Science China Technological Sciences
language:en
Short-container-title:Sci. China Technol. Sci.

Author:

Li YuFang,Chen MingNuo,Lu XiaoDing,Zhao WanZhong

Publisher

Springer Science and Business Media LLC

Subject

General Engineering,General Materials Science

Link

http://link.springer.com/article/10.1007/s11431-017-9213-0/fulltext.html

Reference23 articles.

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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: 1034–1050

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, 2017

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: 150–162

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: 145–149

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