Short-Term Traffic Flow Prediction Based on a K-Nearest Neighbor and Bidirectional Long Short-Term Memory Model
Source abstract
Abstract
In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to predict the spatio-temporal characteristics of short-term traffic flow by combining the k-nearest neighbor algorithm and bidirectional long short-term memory network model. By selecting the real-time traffic flow data observed on high-speed roads in the United Kingdom, the K-nearest neighbor algorithm is used to spatially screen the station data to determine the points with high correlation and then input the BILSTM model for prediction. The experimental results show that compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models, the model proposed in this paper has better prediction accuracy, and its performance has been improved by 77%, 19%, 18%, 22%, and 13%, respectively. The proposed K-nearest neighbor-bidirectional long short-time memory model shows better prediction performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Traffic control and management · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China
School of Transportation, Fujian University of Technology, Fuzhou 350118, China
Funding
National Social Science Foundation of China · Fujian Zhi-lian-yun Supply Chain Technology and Economy Integration Service Platform · Fujian–Kenya Silk Road Cloud Joint R&D Center · Fujian Social Sciences Federation Planning Project · General Program of the Fujian Natural Science Foundation · the 2022 Scientific and Technological Innovation Think Tank Project of the Fujian Association for Science and Technology
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