【摘要】 【目标】作为人类生存、生产、生活与发展所必需的关键基础，城市道路交通系统中普遍存在着供给与需求在时间和空间上高度不匹配的现象，引发较为严峻的拥堵、事故、排放以及低效等问题。城市道路交通拥堵溯源研究对于实现路网的交通供需动态平衡具有关键性理论意义和实用价值。本研究旨在系统梳理其在智能交通领域的研究现状与发展趋势。【方法】首先，基于城市道路交通出行需求的分布特征、瓶颈状态的演化规律及拥堵问题的深层致因，构建了城市道路交通拥堵溯源分析的三层次方法论体系：第一层次为起讫点溯源分析，追溯导致城市道路拥堵的交通流的起讫点等信息；第二层次为交通瓶颈溯源分析，对城市道路瓶颈交通状态的时空演变规律进行刻画；第三层次为致因溯源分析，探究造成城市道路供需不匹配问题的本质原因。在此基础上，以CNKI核心数据库和Web of Science核心合集为数据源，(时间跨度分别为2003—2025年、1994—2026年),采用“拥堵溯源”“OD估计”“瓶颈识别”“致因分析”等组合关键词进行文献检索，最终纳入183篇核心文献，结合VOSviewer文献计量方法，对现有研究成果进行系统归纳与评述。【结果】研究表明，现有的溯源分析已初步实现了从单一断面的流量统计向路网级时空轨迹重构的跨越：起讫点溯源能够有效掌握导致拥堵的交通需求在路网上的分布情况，实现道路的车源定位；交通瓶颈溯源实现了对拥堵瓶颈产生的时空信息分析确定，追溯交通状态的演化规律；致因溯源则尝试从规划、设计与管理层面探寻拥堵生成的深层根源。而随着交通大数据技术的不断进步，精准可靠的多源异构数据持续积累，正推动着城市道路交通拥堵溯源分析方法体系及其实践的创新发展，有效地提高了拥堵溯源分析结果的精准性和实时性。【结论】本文所构建的三层次方法论体系，对于推动城市道路交通拥堵溯源分析研究及未来城市交通系统的可持续创新具有重要指导意义。【展望】在未来的研究趋势上，城市道路交通拥堵溯源分析方法应结合多源数据与模型方法，形成多层次溯源体系，重点应用分析城市路网中的关键路段。未来研究发展方向归纳为以下3个方面：数据驱动与机理模型相结合的溯源分析方法、交通拥堵溯源与城市交通系统重构、从溯源诊断到靶向治理的转化应用。更多还原

【Abstract】 [Objective] As a key foundation for human survival, production, life and development, the urban transportation system suffers from spatiotemporal imbalance between supply and demand, leading to severe problems, e.g., congestion, accident, emission and inefficiency. Research on urban road traffic congestion tracing is vital for achieving the dynamic balance between supply and demand on road networks. This study systematically reviews its research status and development trends. [Method] First, the study was based on the distribution characteristics of travel demand, the evolution of traffic bottlenecks, as well as the deep-rooted causes of traffic congestion. It constructed a three-layer methodological framework for urban road traffic congestion tracing analysis. The first layer was Origin-Destination(OD) tracing analysis, which traced the origins and destinations of traffic flows causing congestion. The second layer was traffic bottleneck tracing analysis, which characterized the spatiotemporal evolution patterns of traffic states at bottlenecks. The third layer was congestion causality tracing analysis, which investigated the fundamental reasons behind the supply-demand mismatch. Building upon this framework, a literature search was conducted utilizing CNKI database(from 2003 to 2025) and Web of Science core collection(from 1994 to 2026) as data sources. The search used combined keywords, e.g., congestion tracing, OD estimation, bottleneck identification, and causality analysis. A total of 183 papers were included. Finally, existing research was systematically summarized and reviewed through VOSviewer for bibliometric analysis. [Result] The current tracing analysis has initially achieved a transition from single-section traffic volume statistics to network-level spatiotemporal trajectory reconstruction. OD tracing can effectively capture the network distribution of traffic demand leading to congestion, achieving vehicle source localization. The traffic bottleneck tracing enables spatiotemporal analysis on congestion bottleneck formation, revealing the evolution patterns of traffic states. The congestion causality tracing attempts to investigate the deep-rooted origins of congestion generation from the perspectives of plan, design, and management. The accurate and reliable multi-source heterogeneous data continue to accumulate with the development of traffic information technology. It promotes the innovation of urban road traffic congestion tracing analysis and related applications, improving the accuracy and real-time capabilities of results. [Conclusion] The findings offer guidance for urban road traffic congestion tracing analysis, holding significance for the sustainable and innovative development of urban transportation systems in the future. [Prospect] Regarding future research trends, the urban road traffic congestion tracing analysis should integrate multi-source data and modeling approaches, with a specific focus on analyzing critical links within the urban road network. Future research directions are summarized into three main aspects, i.e., the tracing analysis methods combining data-driven approaches and traffic flow models, the traffic congestion tracing and urban transportation system reconstruction, and the translational application from tracing diagnosis to targeted governance.更多还原