An integrated model for coordinating adaptive platoons and parking decision-making based on deep reinforcement learning

被引:1
作者
Li, Jia [1 ]
Guo, Zijian [1 ]
Jiang, Ying [1 ,2 ,3 ,4 ]
Wang, Wenyuan [1 ]
Li, Xin [1 ]
机构
[1] Dalian Univ Technol, State Key Lab Coastal & Offshore Engn, Dalian, Peoples R China
[2] Southeast Univ, Jiangsu Key Lab Urban ITS, Nanjing, Peoples R China
[3] Southeast Univ, Jiangsu Prov Collaborat Innovat Ctr Modern Urban T, Nanjing, Peoples R China
[4] Southeast Univ, Sch Transportat, Nanjing, Peoples R China
基金
中国国家自然科学基金;
关键词
Intelligent automatic vehicle; Platoon strategy; Parking area; Reinforcement learning; Automated container terminal; YARD CRANE; QUAY CRANE; CONTAINER; TRUCK; TERMINALS; DESIGN; PORT;
D O I
10.1016/j.cie.2025.110962
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Improving transportation efficiency is a key challenge in operating automated container terminals (ACTs), particularly in managing yard intersections and optimizing parking resources. However, existing studies often treat these two aspects independently, failing to consider their combined impact on vehicle operation efficiency. To this end, this study proposes a hierarchical control framework, named CAP-PDM, to integrate intersection platoon strategy and parking resource management for Intelligent Autonomous Vehicles (IAVs). The CAP-PDM comprises two layers: (1) the adaptive platoon layer leverages real-time traffic data to determine optimal platoon sizes at intersections, addressing localized congestion and reducing delays; (2) the parking strategy optimization layer achieves dynamic IAV scheduling and task allocation within the horizontal transportation network by considering multiple objectives (i.e., the current efficiency of IAVs and task completion). The Dual Deep Deterministic Policy Gradient (DDPG) algorithm is employed to determine platoon sizes and manage realtime IAV assignments to parking areas. Simulation results demonstrate that compared with other control methods, CAP-PDM demonstrates superior adaptability to varying traffic conditions, minimizes delays, and significantly enhances the operational efficiency of IAVs in ACTs. This study highlights the importance of integrating traffic control with resource optimization to improve the efficiency of automated port operations. The findings provide port managers with innovative insights for optimizing horizontal transportation systems.
引用
收藏
页数:14
相关论文
共 48 条
[1]   Space-time routing in dedicated automated vehicle zones [J].
An, Yunlong ;
Li, Meng ;
Lin, Xi ;
He, Fang ;
Yang, Haolin .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 120
[2]  
Bahnes Nacera, 2016, Journal of Innovation in Digital Ecosystems, V3, P22, DOI 10.1016/j.jides.2016.05.002
[3]   Parking management of automated vehicles in downtown areas [J].
Bahrami, Sina ;
Vignon, Daniel ;
Yin, Yafeng ;
Laberteaux, Ken .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 126
[4]   About auction strategies for intersection management when human-driven and autonomous vehicles coexist [J].
Cabri, Giacomo ;
Gherardini, Luca ;
Montangero, Manuela ;
Muzzini, Filippo .
MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (10) :15921-15936
[5]   Autonomous truck scheduling for container transshipment between two seaport terminals considering platooning and speed optimization [J].
Chen, Shukai ;
Wang, Hua ;
Meng, Qiang .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2021, 154 :289-315
[6]   Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework [J].
Chen, Xuchao ;
He, Shiwei ;
Zhang, Yongxiang ;
Tong, Lu ;
Shang, Pan ;
Zhou, Xuesong .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 114 :241-271
[7]   Design and optimization of parking lot in an underground container logistics system [J].
Gao, Yinping ;
Chang, Daofang ;
Fang, Ting ;
Luo, Tian .
COMPUTERS & INDUSTRIAL ENGINEERING, 2019, 130 (327-337) :327-337
[8]   Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption [J].
He, Junliang ;
Huan, Youfang ;
Yan, Wei ;
Wang, Shuaian .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (05) :2464-2487
[9]   Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning [J].
Hu, Hongtao ;
Yang, Xurui ;
Xiao, Shichang ;
Wang, Feiyang .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2023, 61 (01) :65-80
[10]   Integrated scheduling in automated container terminals considering AGV conflict-free routing [J].
Ji, Shouwen ;
Luan, Di ;
Chen, Zhengrong ;
Guo, Dong .
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH, 2021, 13 (07) :501-513