Region-wide congestion prediction and control using deep learning
Region-wide congestion prediction and control using deep learning
复制标题
DOI：
10.1016/j.trc.2020.102624
复制
发表时间：
2020-07-01
影响因子：
8.3
通讯作者：
Cassidy, Michael
中科院分区：
文献类型：
--
作者：
Mohanty, Sudatta;Pozdnukhov, Alexey;Cassidy, Michael
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）神经网络架构。它预测一个拥堵分数，定义为一个社区内的车辆积累与其行程完成率的比率。输入包括在一个区域内的社区在较早的时间测量的拥堵分数，和其他三个实时测量的区域交通。使用纽韦尔的简化理论的运动波的想法进行了测试。首先介绍简化的街道网络。初步测试表明，拥堵评分适用于表征社区交通状况，并且可以使用四个输入来预测该评分。对简化网络的进一步测试说明了深度学习方法的价值，与使用三个基准模型相比。下一轮测试表明，该模型可以变得健壮，即使是在不利的环境中。最后一轮测试的特点是在旧金山弗朗西斯科湾区的高速公路网络的精简版本。最后的测试表明该模型具有可扩展性。此后，该模型通过加权无向图表示的输入，结合个人的路线选择，并通过图卷积学习功能。一个框架，更好地解释模型的投入，其输出的贡献。该模型的实用性在设计交通控制方案的演示。
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.