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Stochastic modeling for vehicle platoons: 2, Statistical characteristics

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journal contribution
posted on 2016-09-06, 13:51 authored by Baibing LiBaibing Li
This two-part paper presents a new approach to stochastic dynamic modeling for vehicle platoons. Part I develops a vehicle platoon model to capture the dynamics of vehicles’ grouping behavior and proposes an online platoon recognition algorithm. On the basis of the developed platoon model, Part II investigates various important characteristics of vehicle platoons and derives their statistical distribution models, including platoon size, within-platoon headway, between-platoon headway and platoon speed. It is shown that the derived statistical distributions include some important existing models in the literature as their special cases. These statistical distribution models are crucial for us to understand the traffic platooning phenomenon. In practice, they can be used as the inputs for the design of traffic management and control algorithms for traffic with a platoon structure. Real traffic data is used to illustrate the obtained theoretical results.

History

School

  • Business and Economics

Department

  • Business

Published in

Transportation Research Part B: Methodological

Volume

95

Pages

378-393

Citation

LI, B., 2016. Stochastic modeling for vehicle platoons: 2, Statistical characteristics. Transportation Research: Part B, Methodological, 95, pp. 378-393.

Publisher

© Elsevier

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016-08-11

Copyright date

2017

Notes

This paper was accepted for publication in the journal Transportation Research: Part B, Methodological and the definitive published version is available at http://dx.doi.org/10.1016/j.trb.2016.07.017

ISSN

1879-2367

eISSN

0191-2615

Language

  • en