Attention-based Gated Recurrent Unit for Links Traffic Speed Forecasting

With urge of demands on efficient transport planning policies along with surge of travel flow volumes due to fast urbanization, traffic speed forecasting becomes a canonical and thriving research domain. Furthermore, the vehicles speed plays a critical role in the level of congestion. Traffic speed estimation then helps transport authorities as well as network users to handle congestion over road infrastructures or at least provides a global picture of daily passenger flow. In this work, we propose the first methodology to forecast the future traffic speed over the road segments (i.e. links) exclusively based on traffic flow data using floating car data. For this study, we pre-process over one million vehicles flow for several network links spread all over the Greater Paris. A attention-based recurrent neural network is used to capture the correlation between the temporal sequences of traffic flow and that of speed. The attention layer learns patterns from weights of near-term traffic flow, thus extracts the inherent interdependency of traffic speed to many factors (e.g. incidents, rush hour, land use, etc.) in non-free-flow conditions. The results demonstrate the efficiency of the proposed model in traffic speed forecasting excluding additional data such as historic 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.