Traffic management authorities in metropolitan areas use real-time systems that analyze high-frequency measurements from fixed sensors, to perform short-term forecasting and incident detection for various locations of a road network. Published research over the last 20 years focused primarily on modeling and forecasting of traffic volumes and speeds. Traffic occupancy approximates vehicular density through the percentage of time a sensor detects a vehicle within a pre-specified time interval. It exhibits weekly periodic patterns and heteroskedasticity and has been used as a metric for characterizing traffic regimes (e.g. free flow, congestion). This article presents a Bayesian three-step model building procedure for parsimonious estimation of Threshold-Autoregressive (TAR) models, designed for location- day- and horizon-specific forecasting of traffic occupancy. In the first step, multiple regime TAR models reformulated as high-dimensional linear regressions are estimated using Bayesian horseshoe priors. Next, significant regimes are identified through a forward selection algorithm based on Kullback-Leibler (KL) distances between the posterior predictive distribution of the full reference model and TAR models with fewer regimes. Given the regimes, the forward selection algorithm can be implemented again to select significant autoregressive terms. In addition to forecasting, the proposed specification and model-building scheme, may assist in determining location-specific congestion thresholds and associations between traffic dynamics observed in different regions of a network. Empirical results applied to data from a traffic forecasting competition, illustrate the efficacy of the proposed procedures in obtaining interpretable models and in producing satisfactory point and density forecasts at multiple horizons.

中文翻译：

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子集阈值自回归的贝叶斯估计：交通占用的短期预测

大都市地区的交通管理机构使用实时系统分析来自固定传感器的高频测量值，对道路网络的各个位置进行短期预测和事故检测。过去 20 年发表的研究主要集中在交通量和速度的建模和预测上。交通占用率通过传感器在预先指定的时间间隔内检测到车辆的时间百分比来近似车辆密度。它表现出每周的周期性模式和异方差性，并已被用作表征交通状况（例如自由流动、拥堵）的度量。本文介绍了用于阈值自回归 (TAR) 模型的简约估计的贝叶斯三步模型构建过程，专为特定位置、日期和时间段的交通占用预测而设计。第一步，使用贝叶斯马蹄形先验估计重新制定为高维线性回归的多态 TAR 模型。接下来，通过基于完整参考模型的后验预测分布与具有较少制度的 TAR 模型之间的 Kullback-Leibler (KL) 距离的前向选择算法来识别重要制度。给定这些制度，可以再次实施前向选择算法以选择重要的自回归项。除了预测之外，所提出的规范和模型构建方案可能有助于确定特定位置的拥塞阈值以及在网络的不同区域观察到的交通动态之间的关联。