TY - GEN
T1 - Analysis of a Stochastic Model for Coordinated Platooning of Heavy-duty Vehicles
AU - Xiong, Xi
AU - Xiao, Erdong
AU - Jin, Li
N1 - Funding Information:
This work was supported in part by NYU Tandon School of Engineering and C2SMART Department of Transportation Center. The authors appreciate the discussion with Profs. Pravin Varaiya, Saurabh Amin, Karl H. Johansson, and Zhong-Ping Jiang.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Platooning of heavy-duty vehicles (HDVs) is a key component of smart and connected highways and is expected to bring remarkable fuel savings and emission reduction. In this paper, we study the coordination of HDV platooning on a highway section. We model the arrival of HDVs as a Poisson process. Multiple HDVs are merged into one platoon if their headways are below a given threshold. The merging is done by accelerating the following vehicles to catch up with the leading ones. We characterize the following random variables: (i) platoon size, (ii) headway between platoons, and (iii) travel time increment due to platoon formation. We formulate and solve an optimization problem to determine the headway threshold for platooning that leads to minimal cost (time plus fuel). We also compare our results with that from Simulation of Urban MObility (SUMO).
AB - Platooning of heavy-duty vehicles (HDVs) is a key component of smart and connected highways and is expected to bring remarkable fuel savings and emission reduction. In this paper, we study the coordination of HDV platooning on a highway section. We model the arrival of HDVs as a Poisson process. Multiple HDVs are merged into one platoon if their headways are below a given threshold. The merging is done by accelerating the following vehicles to catch up with the leading ones. We characterize the following random variables: (i) platoon size, (ii) headway between platoons, and (iii) travel time increment due to platoon formation. We formulate and solve an optimization problem to determine the headway threshold for platooning that leads to minimal cost (time plus fuel). We also compare our results with that from Simulation of Urban MObility (SUMO).
KW - Automated highways
KW - Poisson point process
KW - connected and autonomous vehicles
KW - vehicle platooning
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U2 - 10.1109/CDC40024.2019.9029179
DO - 10.1109/CDC40024.2019.9029179
M3 - Conference contribution
AN - SCOPUS:85082465324
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3170
EP - 3175
BT - 2019 IEEE 58th Conference on Decision and Control, CDC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th IEEE Conference on Decision and Control, CDC 2019
Y2 - 11 December 2019 through 13 December 2019
ER -