基于有限传感器数据的图神经网络代理模型用于事件驱动的行程时间预测

中文摘要 AI 辅助

局部道路事件可能在其发生地点之外产生大范围的拥堵，但使用微观仿真评估这些影响需要对每个场景进行单独运行。本文提出了一种时空图卷积网络（STGCN），用于在模拟的田纳西州纳什维尔道路网络上预测路线级行程时间，该网络包含1,037个交叉口和1,601个路段。模型仅使用来自129个信号交叉口的方向交通流量数据，这反映了交通管理部门通常可获得的数据。通过将这些交叉口表示为图，STGCN联合捕捉空间依赖性和时间交通演变，以学习局部扰动如何在网络中传播。我们将基于80个无事件仿真训练的基线模型与额外使用360个车道封闭场景（涵盖12个位置和三种持续时间）训练的事件包含模型进行比较。基线模型的相对平均绝对误差（MAE）为9.94%。事件包含模型总体误差为9.67%，在事件发生期间为6.45%，在完全未参与训练的30个事件位置处为13.62%。在受扰动的路线上，其预测平均在观测行程时间的2.0分钟以内。作为对比，在相同条件下，模拟行程时间在不同随机种子间变化7.4%，即1.3分钟。推理在单个CPU核心上约需53毫秒，展示了该模型作为交通韧性筛查、事件管理和主动重新路由的高效代理的潜力。

英文摘要

Localized roadway incidents can produce congestion well beyond their point of origin, but evaluating these effects with microscopic simulation requires a separate run for each scenario. This paper presents a Spatio-Temporal Graph Convolutional Network (STGCN) for forecasting route-level travel times on a simulated Nashville, Tennessee road network with 1,037 junctions and 1,601 road segments. The model uses directional traffic counts from only 129 signalized intersections, reflecting data commonly available to transportation agencies. By representing these intersections as a graph, the STGCN jointly captures spatial dependence and temporal traffic evolution to learn how localized disruptions propagate through the network. We compare a baseline trained on 80 incident-free simulations with an incident-inclusive model trained with an additional 360 lane-blockage scenarios across 12 locations and three durations. The baseline achieved a relative mean absolute error (MAE) of 9.94%. The incident-inclusive model achieved 9.67% overall, 6.45% during active incidents, and 13.62% at 30 incident locations withheld entirely from training. On disrupted routes, its predictions were within 2.0 minutes of observed travel time on average. For comparison, simulated travel times vary by 7.4%, or 1.3 minutes, across random seeds under identical conditions. Inference requires approximately 53 ms on a single CPU core, demonstrating the potential of the model as an efficient surrogate for transportation resilience screening, incident management, and proactive rerouting.

发表机构

• Oak Ridge National Laboratory（橡树岭国家实验室）

机构由 AI 辅助整理，请以论文原文为准。