TimeBird: Context-Aware Graph Convolution Network for Traffic Incident Duration Prediction

Fuyong Sun; Ruipeng Gao; Weiwei Xing; Yaoxue Zhang; Wei Lu; Jun Fang; Shui Liu; Nan Ma; Hua Chai

doi:10.1007/978-3-031-19208-1_16

ScienceGate Book Chapters

BOOK-CHAPTER

TimeBird: Context-Aware Graph Convolution Network for Traffic Incident Duration Prediction

Fuyong Sun Ruipeng Gao Weiwei Xing Yaoxue Zhang Wei Lu Jun Fang Shui Liu Nan Ma Hua Chai

Year: 2022 Lecture notes in computer science Pages: 185-195 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-19208-1_16

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Adjacency matrix Graph Context (archaeology) Duration (music) Convolution (computer science) Dependency (UML) Adjacency list Data mining Real-time computing Artificial intelligence Theoretical computer science Algorithm Geography Artificial neural network

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.47

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Traffic Prediction and Management Techniques

Physical Sciences → Engineering → Building and Construction

Transportation Planning and Optimization

Social Sciences → Social Sciences → Transportation

Traffic and Road Safety

Physical Sciences → Engineering → Safety, Risk, Reliability and Quality

TimeBird: Context-Aware Graph Convolution Network for Traffic Incident Duration Prediction

Metrics

Topics

Related Documents

Spatial-temporal incident-aware dynamic graph convolution networks for traffic flow prediction

Spatio-temporal aware graph convolution network for urban traffic speed prediction

Temporal attention aware dual-graph convolution network for air traffic flow prediction

Graph Convolution Network for Urban Mobile Traffic Prediction

Context-Aware Graph Convolution Network for Target Re-identification