A Deep Prediction Model of Traffic Flow Considering Precipitation Impact

Abstract

Traffic flow prediction is an important part of intelligent transportation systems (ITS). However, the performance of current traffic flow prediction methods does not meet the expectation. Weather factors such as precipitation in residential areas and tourist destinations affect traffic flow on the surrounding roads. In this paper, we attempt to take precipitation impact into consideration when predicting traffic flow. To realize this idea, we propose a deep traffic flow prediction architecture by introducing a deep bi-directional long short-term memory model, precipitation information, residual connection, regression layer and dropout training method. The proposed model has good ability to capture the deep features of traffic flow. Besides, it can take full advantage of time-aware traffic flow data and additional precipitation data. We evaluate the prediction architecture on the dataset from Caltrans Performance Measurement System (PeMS) with precipitation data from California Data Exchange Center (CDEC) and the dataset from KDD Cup 2017. The experiment results demonstrate that the proposed model for traffic flow prediction obtains high accuracy and generalizes well compared with other models.