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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10090624