P-DBL: A Deep Traffic Flow Prediction Architecture Based on Trajectory Data

Abstract

Predicting large-scale transportation network traffic flow has become an important and challenging topic in recent decades. However, accurate traffic flow prediction is still hard to realize. 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 taxi trajectory dataset in Chongqing and taxi trajectory dataset in Beijing with corresponding precipitation data from China Meteorological Data Service Center (CMDC). The experiment results demonstrate that the proposed model for traffic flow prediction obtains high accuracy compared with other models.