Work

Short-term traffic flow prediction research based on ICEEMDAN-MPE-PSO-DELM model

Year: 2025

Type: article

Abstract: Based on historical data, a new short-term traffic flow prediction model (ICEEMDAN-MPE-PSO-DELM-ARIMA) for intersections is proposed. In order to improve prediction accuracy, ICEEMDAN decomposition algorithm is applied on traffic flow time series to obtain multiple Intrinsic Mode Function (IMF) components. Then PSO-MPE algorithm is introduced to obtain the multi-scale permutation entropy values of each IMF component, to judge the randomness. According to the randomness, different prediction models are established. Prediction models based on PSO-DELM algorithm are established for IMF components with big randomness. ARIMA prediction models are established for IMF components with small randomness. In order to obtain the final predicted traffic flow values, multiple prediction results are added together. Finally, two actual intersections are selected to verify the proposed model. Results show that compared with other models, the proposed model has the smallest prediction errors and the best fitting effect with the real values, which can effectively improve prediction accuracy.

Source: Scientific Reports

DOI: 10.1038/s41598-025-11919-6

Authors: Xiujuan Tian, Jinyong Ding, Huanying Liu, Xue Xing, Jiaojiao Liu

Institutions: Jilin Jianzhu University, First Automotive Works (China), Jilin University of Chemical Technology

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,690