Deep neural networks (DNNs) have emerged as a dominant approach for developing traffic forecasting models. These models are typically trained to minimize error on averaged test cases and produce a single-point prediction, such as a scalar value for traffic speed or travel time. However, single-point predictions fail to account for prediction uncertainty that is critical for many transportation management scenarios, such as determining the best- or worst-case arrival time. We present QuanTraffic, a generic framework to enhance the capability of an arbitrary DNN model for uncertainty modeling. QuanTraffic requires little human involvement and does not change the base DNN architecture during deployment. Instead, it automatically learns a standard quantile function during the DNN model training to produce a prediction interval for the single-point prediction. The prediction interval defines a range where the true value of the traffic prediction is likely to fall. Furthermore, QuanTraffic develops an adaptive scheme that dynamically adjusts the prediction interval based on the location and prediction window of the test input. We evaluated QuanTraffic by applying it to five representative DNN models for traffic forecasting across seven public datasets. We then compared QuanTraffic against five uncertainty quantification methods. Compared to the baseline uncertainty modeling techniques, QuanTraffic with base DNN architectures delivers consistently better and more robust performance than the existing ones on the reported datasets.

翻译：深度神经网络（DNN）已成为交通预测模型开发的主流方法。这类模型通常通过最小化平均测试用例误差进行训练，并生成单点预测结果（如交通速度或行程时间的标量值）。然而，单点预测无法体现对许多交通管理场景至关重要的预测不确定性——例如确定最优或最差到达时间。我们提出QuanTraffic这一通用框架，旨在增强任意DNN模型对不确定性建模的能力。QuanTraffic几乎无需人工干预，且部署期间无需改变基础DNN架构。该框架在DNN模型训练过程中自动学习标准分位数函数，为单点预测生成预测区间。该区间定义了交通预测真实值可能落入的范围。此外，QuanTraffic开发了一种自适应机制，可根据测试输入的位置和预测窗口动态调整预测区间。我们将QuanTraffic应用于五个代表性DNN交通预测模型，并在七个公开数据集上评估其性能，同时与五种不确定性量化方法进行对比。实验结果表明，与现有基线不确定性建模技术相比，基于基础DNN架构的QuanTraffic在报告数据集上展现出更优且更稳健的性能。