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Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics
Joint Authors
Wang, Chao
Lv, Jiarun
Zhang, Zhiqi
Wang, Jiangfeng
Source
Journal of Advanced Transportation
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-14
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
A route choice prediction model is proposed considering the connected vehicle guidance characteristics.
This model is proposed to prevent the delay in the release of guidance information and route planning due to inaccurate timing predictions of the traditional guidance systems.
Based on the analysis of the impact of different connected vehicle (CV) guidance strategies on traffic flow, an indexes system for CV guidance characteristics is presented.
Selecting five characteristic indexes, a route choice prediction model is designed using the logistic model.
A simulation scenario is established by programming different agents for controlling the flow of vehicles and for information acquisition and transmission.
The prediction model is validated using the simulation scenario, and the simulation results indicate that the characteristic indexes have a significant influence on the probability of choosing a particular route.
The average root mean square error (RMSE) of the prediction model is 3.19%, which indicates that the calibration model shows a good prediction performance.
In the implementation of CV guidance, the penetration rate can be considered an optional index in the adjustment of the guidance effect.
American Psychological Association (APA)
Wang, Jiangfeng& Lv, Jiarun& Wang, Chao& Zhang, Zhiqi. 2017. Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1170910
Modern Language Association (MLA)
Wang, Jiangfeng…[et al.]. Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics. Journal of Advanced Transportation No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1170910
American Medical Association (AMA)
Wang, Jiangfeng& Lv, Jiarun& Wang, Chao& Zhang, Zhiqi. Dynamic Route Choice Prediction Model Based on Connected Vehicle Guidance Characteristics. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1170910
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170910