Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving, and large-scale models in the traffic domain, lane-level traffic prediction has emerged as an indispensable direction. However, further progress in this field is hindered by the absence of comprehensive and unified evaluation standards, coupled with limited public availability of data and code. This paper extensively analyzes and categorizes existing research in lane-level traffic prediction, establishes a unified spatial topology structure and prediction tasks, and introduces a simple baseline model, GraphMLP, based on graph structure and MLP networks. We have replicated codes not publicly available in existing studies and, based on this, thoroughly and fairly assessed various models in terms of effectiveness, efficiency, and applicability, providing insights for practical applications. Additionally, we have released three new datasets and corresponding codes to accelerate progress in this field, all of which can be found on https://github.com/ShuhaoLii/TITS24LaneLevel-Traffic-Benchmark.

翻译：交通预测长期以来一直是研究中的焦点与核心领域，近年来见证了从城市级到道路级预测的重大进展。随着车联网（V2X）技术、自动驾驶及交通领域大规模模型的发展，车道级交通预测已成为不可或缺的研究方向。然而，该领域的进一步发展受到缺乏全面统一评估标准以及公开数据和代码有限的阻碍。本文对现有车道级交通预测研究进行了深入分析与分类，建立了统一的空间拓扑结构与预测任务，并提出了基于图结构和MLP网络的简单基线模型GraphMLP。我们复现了现有研究中未公开的代码，并在此基础上从有效性、效率和适用性三个方面对多种模型进行了全面公平的评估，为实际应用提供了见解。此外，我们发布了三个新数据集及相应代码以加速该领域进展，所有资源均可通过https://github.com/ShuhaoLii/TITS24LaneLevel-Traffic-Benchmark获取。