Cooperative scheduling and speed planning of vehicles on highways based on transportation cost

被引：0
|
作者：
Lu Z.-B. [1 ,2 ]
Tian K.-J. [1 ,2 ]
Fang M.-X. [1 ,2 ]
Qu L.-G. [1 ]
机构：
[1] College of Physics and Electronic Information, Anhui Normal University, Wuhu
[2] Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot, Wuhu
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 06期
关键词：
clustering algorithm; intelligent connected vehicles; speed planning; transportation cost; vehicle cooperative scheduling;
D O I：
10.13195/j.kzyjc.2021.1797
中图分类号：
学科分类号：
摘要：
This paper presents a new cooperative scheduling scheme for large-scale freight vehicles in the road network, which combines fuel consumption cost and time cost. This scheme can solve the problem of vehicle platoon coordination optimization while maximizing transportation cost saving. Firstly, a vehicle transportation cost model based on fuel consumption cost and time cost is established, the transportation cost saving rate of any two vehicles traveling in a group is calculated cyclically by fine-tuning the speed and path of some vehicles, and the vehicles meeting the conditions of the group are constructed as the vehicle group coordination graph. Then, an algorithm based on central clustering division is used to transform the problem of vehicle formation into a cluster solution problem, and the lead vehicle with the maximum transportation cost saving is solved, so that it can form a platoon to drive in public sections. Finally, considering the road slope and speed limit, a dynamic programming method based on spatial sampling is adopted to optimize the speed of the merged vehicles, so as to further reduce the transportation cost of vehicles. Simulation results verify the feasibility and effectiveness of the proposed optimization scheme and solution idea. © 2023 Northeast University. All rights reserved.
引用
收藏
页码：1637 / 1645
页数：8
相关论文
共 24 条
  • [1] Liu C S, Shen L Z, Sheng H Y, Et al., Research on low-carbon time-dependent vehicle routing problem with traffic congestion avoidance approaches, Control and Decision, 35, 10, pp. 2486-2496, (2020)
  • [2] Besselink B, Turri V, Van de Hoef S H, Et al., Cyber-physical control of road freight transport, Proceedings of the IEEE, 104, 5, pp. 1128-1141, (2016)
  • [3] Guo L L, Gao B Z, Chen H., Optimal ecodriving control of vehicles, Scientia Sinica: Informationis, 46, 5, pp. 560-570, (2016)
  • [4] Yang Q, Zhang J X, Cai W Y, Et al., Measuring the capacity utilization of China’s transportation industry under environmental constraints, China Journal of Highway and Transport, 33, 11, pp. 226-234, (2020)
  • [5] Yang H, Rakha H, Ala M V., Eco-cooperative adaptive cruise control at signalized intersections considering queue effects, IEEE Transactions on Intelligent Transportation Systems, 18, 6, pp. 1575-1585, (2017)
  • [6] Yang G, Zhang D H, Li K Q, Et al., Cooperative same-direction automated lane-changing based on vehicle-to-vehicle communication, Journal of Highway and Transportation Research and Development, 34, 1, pp. 120-129, (2017)
  • [7] Ma F W, Yang Y, Wang J W, Et al., Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication, Energy, 189, (2019)
  • [8] Liu J Q, Zhao W Z, Xu C., An efficient on-ramp merging strategy for connected and automated vehicles in multi-lane traffic, IEEE Transactions on Intelligent Transportation Systems, 23, 6, pp. 5056-5067, (2022)
  • [9] Lin N, Liu L, Ma L, Et al., Mass identification method for commercial vehicle with air resistance adaptability, China Journal of Highway and Transport, 29, 8, pp. 143-151, (2016)
  • [10] Na G, Park G, Turri V, Et al., Disturbance observer approach for fuel-efficient heavy-duty vehicle platooning, Vehicle System Dynamics, 58, 5, pp. 748-767, (2020)
123