JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction |
| 영문제목(English Title) |
Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction |
| 저자(Author) |
Pengcheng Li
Changjiu Ke
Hongyu Tu
Houbing Zhang
Xu Zhang
|
| 원문수록처(Citation) |
VOL 19 NO. 01 PP. 0130 ~ 0138 (2023. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals. |
| 키워드(Keyword) |
Optimization Graph
Shared Attention
Spatio-temporal Attention
Traffic Flow Forecasting
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| 파일첨부 |
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