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

• Published in: ISPRS international journal of geo-information 2021-09, Vol.10 (9), p.624
• Main Authors: Chen, Kaiqi, Deng, Min, Shi, Yan
• Format: Article
• Language: English
• Subjects: Convolution; Dependence; directed graph convolution; Forecasting; Graph theory; Intelligent transportation systems; Machine learning; Markov chain; Mathematical models; Methods; Neural networks; Roads & highways; spatial dependence; temporal dependence; Time series; Traffic congestion; Traffic flow; traffic flow forecasting; Traffic management; transformer structure; Transportation networks; Transportation planning; Variables; Vehicles
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi10090624

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.