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

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...

Full description

Bibliographic Details
Main Authors: Kaiqi Chen, Min Deng, Yan Shi
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:ISPRS International Journal of Geo-Information
Subjects:
traffic flow forecasting
Markov chain
directed graph convolution
transformer structure
spatial dependence
temporal dependence
Online Access:https://www.mdpi.com/2220-9964/10/9/624

Internet

https://www.mdpi.com/2220-9964/10/9/624

Similar Items