货车轨迹数据在公路货运系统中应用研究综述  

Review on Application of Truck Trajectory Data in Highway Freight System

作　　者：甘蜜[1,2,3] 卿三东 刘晓波[1,2,3] 李丹丹[1,2] GAN Mi;QING San-dong;LIU Xiao-bo;LI Dan-dan(School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu 611756,China;National United Laboratory of Comprehensive Transportation,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学交通运输与物流学院,成都611756 [2]西南交通大学综合交通大数据应用技术国家工程实验室,成都611756 [3]西南交通大学综合交通国家地方联合实验室,成都611756

出　　处：《交通运输系统工程与信息》2021年第5期91-101,113,共12页Journal of Transportation Systems Engineering and Information Technology

基　　金：国家重点研发计划(2018YFB1601402)。

摘　　要：随着轨迹数据可获取性及精度的持续提高,货车轨迹数据被广泛应用于公路货运系统的规划与管理中,同时,人工智能和大数据分析技术的快速发展也为公路货运系统研究带来新的机遇与挑战。本文全面梳理并总结了公路货运轨迹数据应用领域的相关研究,从基于轨迹数据的货运出行信息辨识、货运系统关键特征预测、货运轨迹数据进一步应用3个方面回顾现有文献的研究目标、主要内容和研究方法。通过文献分析发现:货运出行信息辨识研究聚焦于货运停留点、车辆和货物、活动出行模式等热点主题,但现有辨识方法多移植于旅客出行研究,需要更多地考虑货运出行的独特特征。在货运系统关键特征预测方面,研究者主要针对货运行程时间、空间位置、出行需求等主题展开研究,并证明了基于轨迹数据预测货运特征的可行性,但预测时空范围较为局限,需要根据具体的货运任务、货车司机特征和货运政策进行深入研究。此外,轨迹数据也被应用于货运出行路径选择行为、货运停车休息行为、行驶安全、货运排放和能耗分析、货运政策评估等研究。最后,在总结现有研究不足的基础上,本文认为未来研究应重点将货运轨迹数据与其他多源数据相结合,从3个关键技术进行突破:一是针对货运实践个体,重点探索高效货车驾驶员的出行特征和出行模式,并在货运系统中进行推广应用;二是针对交通运输新技术和新形势,重点开发和优化自动驾驶技术和重大应急事件影响下的货运组织模式与策略;三是针对货运供需关系及匹配机制,重点研究货运全流程供需状态辨识与预测,并结合深度学习等方法训练和开发智能供需匹配模型,从而优化货运系统调度,助力社会散乱运力资源整合,提高货运系统的综合效率。With the continuous improvement of the accessibility and accuracy of trajectory tracking data,truck trajectory data has been widely used in the planning and management of highway freight system.At the same time,the rapid development of artificial intelligence and big data analysis technology also brings new opportunities and challenges to the study of highway freight system.This paper comprehensively summarizes the researches on the application of highway freight trajectory data,and reviews the research objectives,main contents and research methods of existing literatures from three aspects:identification of freight travel information,prediction of key features of freight system,and further application of freight trajectory data.Literature analysis shows that the research on freight travel information identification focuses on hot topics such as freight stop points,vehicles and goods,and activity travel patterns.However,the existing identification methods are mostly transplanted from the research of passenger travel,and more considerations need to be given to the unique characteristics of freight travel.In terms of forecasting the key features of the freight system,researchers mainly conduct research on topics such as freight travel time,spatial location,and travel demand,and proved the feasibility of forecasting freight characteristics based on trajectory data.However,the spatial and temporal range of prediction is relatively limited,further research is needed on specific freight tasks,characteristics of truck drivers,and freight policies.In addition,trajectory data are also further applied to freight travel route choice behavior,freight parking and rest behavior,driving safety,freight emissions and energy consumption analysis,freight policy evaluation.On the basis of analyzing the shortcomings of the existing research,this paper suggests that future research should focus on combining freight trajectory data with other multi-source data,make breakthroughs in three key technologies.First,in view of individuals of

关 键 词：公路运输 货运系统 轨迹数据 物流网络 

分 类 号：U492.3[交通运输工程—交通运输规划与管理]