Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction

Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting,...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2023-05, Vol.11 (11), p.2509
Main Authors: Liu, He-Wei, Wang, Yi-Ting, Wang, Xiao-Kang, Liu, Ye, Liu, Yan, Zhang, Xue-Yang, Xiao, Fei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
cloud model
Cluster analysis
Energy consumption
Forecasting
Forecasts and trends
fuzzy C-means
fuzzy inference system
Fuzzy logic
Fuzzy sets
Inference
Mathematics
Methods
Neural networks
Peak hour traffic
Time series
Traffic congestion
Traffic flow
traffic flow prediction
Traffic models
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11112509