复杂度学习驱动的无人电卡集疏运路径优化

doi:10.16097/j.cnki.1009-6744.2025.01.026

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 270-288.DOI: 10.16097/j.cnki.1009-6744.2025.01.026

复杂度学习驱动的无人电卡集疏运路径优化

缪鸿志^1a，李嘉威^1a，李江晨²，贾洪飞³，李振福^*1a，李歆蔚^1b

1. 大连海事大学，a.交通运输工程学院，b.法学院，辽宁大连116026；2.南京航空航天大学，民航学院，南京211106；3. 吉林大学，交通学院，长春130000

收稿日期:2024-09-27 修回日期:2024-12-14 接受日期:2024-12-27 出版日期:2025-02-25 发布日期:2025-02-24
作者简介:缪鸿志(1992—)，男，山东东营人，讲师，博士。
基金资助:
教育部人文社会科学研究项目(23YJC790101)；辽宁省社会科学规划基金(L21CGL008)；辽宁省经济社会发展研究课题(2025lslqnkt-048)。

Complexity Learning-driven Path Optimization for Automated Electric Container Trucks in Port Collection and Distribution

MIAO Hongzhi^1a, LI Jiawei^1a, LI Jiangchen², JIA Hongfei³, LI Zhenfu^*1a, LI Xinwei^1b

1a. College of Transportation Engineering, 1b. School of Law, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; 3. School of Transportation, Jilin University, Changchun 130000, China

Received:2024-09-27 Revised:2024-12-14 Accepted:2024-12-27 Online:2025-02-25 Published:2025-02-24
Supported by:
MOE Liberal Arts and Social Sciences Foundation(23YJC790101)；Liaoning Province Social Science Planning Foundation (L21CGL008)；Liaoning Province Economic and Social Development Research Project (2025lslqnkt-048)。

摘要/Abstract

摘要： 无人驾驶电动集卡(AECT)在港口内部物流中展现出优异的经济和环保效益，但其在港口外部集疏运环节的应用仍面临道路环境复杂多变和能源供给不稳定等挑战。为将AECT扩展至港口邻近半开放区域，本文提出利用多属性复杂度学习驱动AECT集疏运路径优化。首先，针对港口集疏运场景的动态不确定特征，设计融合多属性决策和机器学习的大规模路网复杂度评估方法；其次，在集疏运路径优化模型中引入自动驾驶系统设计工况(ODD)约束和续航里程限制，综合考虑AECT自动驾驶能力、能耗特性和运输时效。实例分析表明：不同等级道路在复杂度水平和空间分布上具有显著差异，低等级道路的复杂度普遍高于高等级道路；当ODD边界复杂度达到0.55及以上时，复杂度学习驱动的路径优化模型可使AECT人工接管率降低5.04%~16.83%，在半开放场景下实现“完全自动驾驶”；随着ODD边界复杂度提高，AECT的自动驾驶性能和节能效果逐步提升，当ODD边界复杂度达到0.55和0.70时，AECT分别在限定路线和任意路线实现自动驾驶，运输成本相比传统集卡节省24.03%和29.26%。

关键词: 智能交通, 路径优化, 机器学习, 无人电卡, 港口集疏运

Abstract: Automated electric container trucks (AECTs) have demonstrated outstanding economic and environmental benefits in port internal logistics. However, their application in external drayage operations still faces challenges such as complex and dynamic road environments and unstable energy supply. To extend AECTs to semi-open areas near ports, this paper proposes a multi-attribute complexity learning-driven approach for optimizing AECT drayage routes. Considering the dynamic and uncertain characteristics of port drayage scenarios, a large-scale road network complexity assessment method is designed by integrating multi-attribute decision-making and machine learning. Then, the operational design domain (ODD) constraints and driving range limitations are introduced into the drayage route optimization model, comprehensively considering the autonomous driving capabilities, energy consumption characteristics, and transportation efficiency of AECTs. Case studies show that: (1) roads of different grades exhibit significant differences in complexity levels and spatial distributions, with lower-grade roads generally having higher complexity than higher-grade roads; (2) when the ODD boundary complexity reaches 0.55 and above, the complexity learning-driven route optimization model can reduce the manual takeover rate of AECTs by 5.04%~16.83%, achieving “fully autonomous driving”in semi-open scenarios; (3) as the ODD boundary expands, AECT's autonomous driving performance and energy- saving effects gradually improve. When the ODD boundary complexity reaches 0.55 and 0.70, AECTs achieve autonomous driving on designated routes and arbitrary routes, respectively, with transportation costs reduced by 24.03% and 29.26% compared to traditional container trucks.

Key words: intelligent transportation, path optimization, machine learning, automated electric container trucks, port collection and distribution

中图分类号:

U495

缪鸿志, 李嘉威, 李江晨, 贾洪飞, 李振福, 李歆蔚. 复杂度学习驱动的无人电卡集疏运路径优化[J]. 交通运输系统工程与信息, 2025, 25(1): 270-288.

MIAO Hongzhi, LI Jiawei, LI Jiangchen, JIA Hongfei, LI Zhenfu, LI Xinwei. Complexity Learning-driven Path Optimization for Automated Electric Container Trucks in Port Collection and Distribution[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 270-288.

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复杂度学习驱动的无人电卡集疏运路径优化

Complexity Learning-driven Path Optimization for Automated Electric Container Trucks in Port Collection and Distribution

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