Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary across time-of-day patterns. This study is conducted on the Ohio Department of Transportation (ODOT) traffic count data and corresponding ODOT crash records. This work utilizes a Spatio-Temporal Transformer (STT) model with Adaptive Conformal Prediction (ACP) to produce multi-horizon forecasts with calibrated uncertainty. We propose a piecewise Coefficient of Variation (CV) strategy that models hour-to-hour traveltime variability using a log-normal distribution, enabling the construction of a per-hour dynamic adjacency matrix. We further perturb edge weights using incident-related severity signals derived from the ODOT crash dataset that comprises incident clearance time, weather conditions, speed violations, work zones, and roadway functional class, to capture localized disruptions and peak/off-peak transitions. This dynamic graph construction replaces a fixed-CV assumption and better represents changing traffic conditions within the forecast window. For validation, we generate extended trips via multi-hour loop runs on the Columbus, Ohio, network in SUMO simulations and apply a Monte Carlo simulation to obtain travel-time distributions for a Vehicle Under Test (VUT). Experiments demonstrate improved long-horizon accuracy and well-calibrated prediction intervals compared to other baseline methods.

翻译：可靠的多时域交通预测具有挑战性，因为网络条件具有随机性，事故干扰是间歇性的，且有效的空间依赖性随一天中的时段模式而变化。本研究基于俄亥俄州交通部（ODOT）的交通流量数据及相应的ODOT事故记录开展。本工作利用时空Transformer（STT）模型与自适应保形预测（ACP）方法，生成具有校准不确定性的多时域预测。我们提出了一种分段变异系数（CV）策略，该策略使用对数正态分布对逐小时的行程时间变异性进行建模，从而能够构建每小时动态邻接矩阵。我们进一步利用源自ODOT事故数据集的事故相关严重性信号（包括事故清除时间、天气条件、超速违规、施工区域和道路功能等级）对边权重进行扰动，以捕捉局部干扰和高峰/非高峰时段的转换。这种动态图构建方法取代了固定CV假设，能更好地表示预测窗口内不断变化的交通状况。为进行验证，我们在SUMO仿真中通过俄亥俄州哥伦布市网络上的多小时循环运行生成扩展行程，并应用蒙特卡洛模拟来获取被测车辆（VUT）的行程时间分布。实验表明，与其他基线方法相比，本方法在长时域预测精度方面有所提升，并生成了校准良好的预测区间。