Traffic speed forecasting becomes a thriving research area in modern transportation systems. The intensification of travel flow volumes due to fast urbanization, vehicle path planning, demands on efficient transport planning policies, commercial objectives, and many other factors contribute to fuel this revival dynamics. Moreover, predicting vehicle speed is of paramount importance in congestion management to help transport authorities as well as network users to handle congestion over road infrastructures or to provide a global overview of daily passenger flow. In this work, we propose a novel approach to forecast the future traffic speed of the road segments (links) based on traffic flow data without the need for previous traffic speed as input. To do this, we first pre-process floating car data of several million vehicles for multiples network links spread all over the Greater Paris area from 2016 to 2017. A convolutional attention-based recurrent neural network is used to capture the local-temporal features of traffic data to unveil the underlying pattern between the traffic flow and speed sequences for all links over the network. While the convolutional layer captures the local dependency, the attention layer learns patterns from weights of near-term traffic flow. It extracts the inherent interdependency of traffic speed due to many factors such as incidents, rush hour, land use, to cite a few, in non-free-flow conditions. The efficiency of the proposed model is evaluated using several metrics in traffic speed forecasting excluding additional data such as historical traffic speed and network graph contrary to cutting-edge work in the field. This is a substantial property since it allows avoiding the cumbersomeness in data mixing and facilitating resource availability. The proposed model is also evaluated on several roads located in the Greater Paris area separately on weekdays and weekends.

交通速度预测已成为现代交通系统中蓬勃发展的研究领域。快速的城市化进程，行车路线规划，对有效交通规划政策的要求，商业目标以及许多其他因素导致的旅行流量激增，助长了这种复兴动力。此外，预测车速在交通拥堵管理中至关重要，以帮助运输当局和网络用户处理道路基础设施上的交通拥堵或提供每日客流量的全球概览。在这项工作中，我们提出了一种新颖的方法，可以根据交通流数据预测路段（路段）的未来交通速度，而无需以前的交通速度作为输入。去做这个，我们首先处理了从2016年到2017年遍布大巴黎地区的多个网络链接的数百万辆汽车的浮动汽车数据。基于卷积注意力的循环神经网络用于捕获交通数据的时空特征，以进行揭示网络上所有链路的流量和速度序列之间的基本模式。在卷积层捕获局部依赖性的同时，注意力层从近期流量的权重中学习模式。在非自由流动的情况下，它会提取由于许多因素（例如事故，高峰时间，土地使用）而引起的交通速度固有的相互依赖性。在交通速度预测中使用几个指标评估了所提出模型的效率，但不包括其他数据，例如历史交通速度和网络图，这与该领域的前沿工作背道而驰。这是一个重要的属性，因为它可以避免繁琐的数据混合并简化资源可用性。还将在工作日和周末分别在大巴黎地区的多条道路上评估所提议的模型。