In this paper, we aim at developing new methods to join machine learning techniques and macroscopic differential models for vehicular traffic estimation and forecast. It is well known that data-driven and model-driven approaches have (sometimes complementary) advantages and drawbacks. We consider here a dataset with flux and velocity data of vehicles moving on a highway, collected by fixed sensors and classified by lane and by class of vehicle. By means of a machine learning model based on an LSTM recursive neural network, we extrapolate two important pieces of information: 1) if congestion is appearing under the sensor, and 2) the total amount of vehicles which is going to pass under the sensor in the next future (30 min). These pieces of information are then used to improve the accuracy of an LWR-based first-order multi-class model describing the dynamics of traffic flow between sensors. The first piece of information is used to invert the (concave) fundamental diagram, thus recovering the density of vehicles from the flux data, and then inject directly the density datum in the model. This allows one to better approximate the dynamics between sensors, especially if an accident happens in a not monitored stretch of the road. The second piece of information is used instead as boundary conditions for the equations underlying the traffic model, to better reconstruct the total amount of vehicles on the road at any future time. Some examples motivated by real scenarios will be discussed. Real data are provided by the Italian motorway company Autovie Venete S.p.A.
翻译:在本文中,我们旨在开发新的方法,将机器学习技术和宏观微分模型用于车辆交通估计和预测。众所周知,数据驱动和模型驱动方法都有优点和缺点。我们在这里考虑了一个数据集,其中包含了在高速公路上行驶的车辆的流量和速度数据,这些数据由固定传感器收集,并按车道和车辆类别进行分类。通过基于LSTM递归神经网络的机器学习模型,我们提取了两个重要的信息:1)如果拥堵在传感器下出现,以及2)在未来的下一个时间段(30分钟内)将通过传感器的车辆总数。然后,这些信息被用来提高一个基于LWR的描述传感器之间的交通流动动态的一阶多类模型的精度。第一个信息用于反演(凹)基础图,从流量数据中恢复车辆密度,然后直接将密度数据注入模型。这允许更好地近似传感器之间的动态,尤其是如果事故发生在未监测的道路区间。反之,第二个信息则用作方程的边界条件,以更好地预测未来任何时间道路上的车辆总量。本文还将讨论一些以实际场景为背景的案例。实际数据由意大利高速公路公司 Autovie Venete S.p.A提供。