Traffic Flow Prediction is the process of using data fusion techniques to forecast traffic density, travel time, and speed in order to enhance routing, dispatching, and congestion management within intelligent transportation systems. AI generated definition based on: Information Fusion, 2023 ...
Traffic flow prediction is a key challenge in intelligent transportation, and the ability to accurately forecast future traffic flow directly affects the efficiency of urban transportation systems. However, existing deep learning-based prediction models suffer from the following issues: First, CNN- or RN...
(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatio...
title={KDD CUP 2017 Highway Tollgates Traffic Flow Prediction Dataset},url={https://tianchi.aliyun.com/dataset/dataDetail?dataId=60},author={Tianchi},year={2018},} 如果您发表的论文有使用本数据集,请发邮件到tianchi_open_dataset@alibabacloud.com,回复论文链接,我们工作人员会给您寄送天池数据集小...
As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN) models ...
(STGNNs) is a basic assumption of current traffic flow prediction models. However, due to structural limitations, existing STGNNs can only utilize short-range traffic flow data; therefore, the models cannot adequately learn the complex trends and periodic features in traffic flow. Besides, it is...
Data Preprocessing: Scripts to clean and prepare the dataset for training. Model Development: A robust LSTM model for time series forecasting. Model Evaluation: Performance metrics to assess prediction accuracy. Web Application: An interactive Streamlit app for traffic flow predictions. Data Sources Hist...
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow,...
Accurate prediction of traffic flow plays an important role in ensuring public traffic safety and solving traffic congestion. Because graph convolutional neural network (GCN) can perform effective fe...
Traffic flow prediction for urban road network is influenced by historical traffic flow and traffic flow at adjacent intersections, which has complex spatio-temporal correlation.For the lack of correlation analysis on traffic flow data, capturing small c