Traffic congestion is forecast for neighborhoods within a region using a deep learning model. The model is based on Long Short-Term Memory (LSTM) neural network architecture. It forecasts a congestion score, defined as the ratio of the vehicle accumulation inside a neighborhood to its trip completion rate. Inputs include congestion scores measured at earlier times in neighborhoods within a region, and three other real-time measures of regional traffic.

The ideas are tested using Newell’s simplified theory of kinematic waves. Simplified street networks are featured first. Initial tests demonstrate the suitability of the congestion score for characterizing neighborhood traffic conditions, and that the score can be predicted using the four inputs. Further tests of the simplified networks illustrate the value of the deep learning approach, as compared against the use of three benchmark models. A next round of tests shows that the model can be made robust, even to adverse settings. A final round of tests features a pared-down version of the freeway network in the San Francisco Bay Area. The final tests show that the model is scalable. The model is thereafter improved by representing the inputs through weighted undirected graphs that incorporate the route-choice of individuals, and learning features through graph convolutions. A framework for better interpreting the contributions of the model’s inputs to its output is developed. A demonstration of the model’s usefulness in designing traffic control schemes is presented as well.

使用深度学习模型预测区域内社区的交通拥堵。该模型基于长短期记忆（LSTM）神经网络体系结构。它预测了拥堵分数，该分数定义为邻域内车辆积累与其行驶完成率之比。输入的信息包括一个区域内社区在较早时间测得的拥挤评分，以及其他三个实时的区域交通量度。

这些想法是使用Newell的运动波简化理论进行测试的。简化的街道网络是第一个特色。初步测试证明了拥塞分数对表征邻里交通状况的适用性，并且可以使用四个输入来预测该分数。与使用三个基准模型相比，对简化网络的进一步测试说明了深度学习方法的价值。下一轮测试表明，即使在不利的条件下，也可以使模型更健壮。最后一轮测试以旧金山湾区的高速公路网的简化版为特色。最终测试表明该模型是可伸缩的。此后，该模型通过使用包含个人路线选择的加权无向图表示输入来进行改进，通过图卷积学习特征。建立了一个更好地解释模型输入对其输出贡献的框架。还演示了该模型在设计交通控制方案中的有用性。