A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

Abstract

Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend to apply deep learning models; these methods are unexplainable and ignore the a priori characteristics of traffic flow. To address these issues, a temporal directed graph convolution network (T-DGCN) is proposed. A directed graph is first constructed to model the movement characteristics of vehicles, and based on this, a directed graph convolution operator is used to capture spatial dependence. For temporal dependence, we couple a keyframe sequence and transformer to learn the tendencies and periodicities of traffic flow. Using a real-world dataset, we confirm the superior performance of the T-DGCN through comparative experiments. Moreover, a detailed discussion is presented to provide the path of reasoning from the data to the model design to the conclusions.

Keywords

• traffic flow forecasting
• Markov chain
• directed graph convolution
• transformer structure
• spatial dependence
• temporal dependence