基于连接车辆数据的动脉网络交通状态预测：一种异常感知的时空网络
Arterial Network Traffic State Prediction with Connected Vehicle Data: An Abnormality-Aware Spatiotemporal Network
AI总结：
本文提出了一种基于连接车辆数据的动脉网络交通预测框架，通过异常感知的时空图卷积网络实现对正常和异常交通的联合建模，提升大尺度动脉网络的交通状态预测能力。
摘要：
新兴的连接车辆（CV）数据在城市交通监测和预测中展现出巨大潜力。然而，先前基于CV的动脉交通测量预测研究局限于模拟高渗透率场景或小型网络，这些在实际城市规模动脉网络中难以应用。为解决这些差距，我们开发了一个基于CV数据的动脉交通预测框架，包含两个组成部分：（1）一种两阶段交通状态提取方法，该方法从CV轨迹估计车辆级交通测量并将其聚合为网络级交通状态测量；（2）一种异常感知的时空图卷积网络（AASTGCN），采用双专家架构分别建模正常和异常交通，并通过具有门控融合机制的时空图卷积网络联合捕捉短期交通动态和长期周期性。使用真实世界的CV数据在包含1,050个链接的大型动脉网络中测试我们的方法。实验结果表明：1）所提出的交通估计方法对大型动脉网络有效，能够提供实时交通测量（例如链接级平均旅行延误和排队长度），这对于城市交通运行和评估至关重要。2）异常交通预测对于现有方法通常是具有挑战性的。通过在两个专门专家中分别建模异常情况和正常交通，AASTGCN在正常和异常交通条件下均优于现有模型。3）门控融合机制能够自适应地平衡实时和历史信息：它在正常交通中利用更多的历史周期性信息，并在异常交通突然偏离历史模式时增加对实时交通动态的权重。
英文摘要：
Emerging connected-vehicle (CV) data shows great potential in urban traffic monitoring and forecasting. However, prior CV-based studies on arterial traffic measures prediction are limited to simulated high-penetration scenarios or small networks, which are challenging to apply in real-world city-scale arterial networks. To address such gaps, we develop a CV data-based arterial traffic prediction framework with two components: (1) a two-stage traffic state extraction method that estimates vehicle-level traffic measures from CV trajectories and then aggregates them into network-level traffic state measures; (2) an Abnormality-aware spatiotemporal graph convolution network (AASTGCN) that adopts a dual-expert architecture to separately model normal and abnormal traffic, and jointly captures short-term traffic dynamics and long-term periodicity via spatiotemporal GCN with a gated-fusion mechanism. Real-world CV data are used to test our method in a large arterial network with 1,050 links. Experimental results show that: 1) The proposed traffic estimation method is effective for large arterial networks to provide real-time traffic measures (e.g., link-level average travel delay and queue length), which are critical for urban traffic operation and evaluation. 2) Abnormal traffic prediction is typically challenging for existing methods. By modeling abnormal cases separately from normal traffic in two dedicated experts, AASTGCN outperforms existing models for both normal and abnormal traffic conditions. 3) The gate-fusion mechanism adaptively balances real-time and historical information: it leverages more historical-periodic information in normal traffic and shifts a higher weight to real-time traffic dynamics for abnormal traffic deviating abruptly from historical patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
