A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction  

作　　者：Bodong Zhou Jiahui Liu Songyi Cui Yaping Zhao 

机构地区：[1]Technical Consulting Department,Shanghai EchoBlend Internet Technology Co.Ltd.,Shanghai 201111,China [2]Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong 999077,China [3]Department of Industrial and Manufacturing Systems Engineering,The University of Hong Kong,Hong Kong 999077,China

出　　处：《Big Data Mining and Analytics》2024年第3期621-636,共16页大数据挖掘与分析（英文）

摘　　要：Traffic prediction is crucial for urban planning and transportation management,and deep learning techniques have emerged as effective tools for this task.While previous works have made advancements,they often overlook comprehensive analyses of spatio-temporal distributions and the integration of multimodal representations.Our research addresses these limitations by proposing a large-scale spatio-temporal multimodal fusion framework that enables accurate predictions based on location queries and seamlessly integrates various data sources.Specifically,we utilize Convolutional Neural Networks(CNNs)for spatial information processing and a combination of Recurrent Neural Networks(RNNs)for final spatio-temporal traffic prediction.This framework not only effectively reveals its ability to integrate various modal data in the spatio-temporal hyperspace,but has also been successfully implemented in a real-world large-scale map,showcasing its practical importance in tackling urban traffic challenges.The findings presented in this work contribute to the advancement of traffic prediction methods,offering valuable insights for further research and application in addressing real-world transportation challenges.

关 键 词：SPATIO-TEMPORAL traffic prediction multimodal fusion learning representation 

分 类 号：U495[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]