Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism  

作　　者：Jing-Doo Wang Chayadi Oktomy Noto Susanto 

机构地区：[1]Departement of Computer Science and Information Engineering,Asia University,Taichung,Taiwan,China [2]Departement of Information Technology,Universitas Muhammadiyah Yogyakarta,Yogyakarta,Indonesia

出　　处：《Computer Modeling in Engineering & Sciences》2024年第8期1711-1728,共18页工程与科学中的计算机建模（英文）

摘　　要：A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.

关 键 词：Traffic flow prediction sptiotemporal data heterogeneous data Conv-BiLSTM DATA-CENTRIC intra-data 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]