Short-term Traffic Flow Prediction Based on KELM Optimized By Improved Slime Mould Algorithm

Traffic flow prediction plays a crucial role in improving transportation efficiency and enhancing Intelligent Transportation Systems (ITS). However, the temporal, spatial, and nonlinear nature of traffic flow data presents challenges for accurate short-term prediction. We propose a short-term traffic flow prediction model based on Kernel Extreme Learning Machine (KELM) optimized by the improved Slime Mould Algorithm (SMA). KELM is an improved version of Extreme Learning Machine (ELM) that incorporates kernel functions for improved generalization and stability. SMA is a meta-heuristic algorithm inspired by the behavior of slime mould in foraging, known for its strong global searching ability. For better performance, three strategies are introduced: the Good Point Set method for optimizing the initial population, the combination of Opposition Based Learning (OBL) and Differential Evolution (DE) to improve the slime mould generation mechanism, and the use of adaptive t distribution mutation to enhance convergence speed. After comparing the performance of these improved SMAs on twelve test functions, the ISMA improved by integrating three strategies as mentioned above is best. Then the ISMA is applied to search for the optimal parameters of KELM model. Finally, the optimized KELM with optimal parameters is applied to predict the short-term traffic flow on given traffic data set. Experimental results demonstrate that the proposed model, KELM optimized by ISMA namely ISMA-KELM, outperforms existing models such as Random Forest (RF), Least Squares Support Vector Machine (LSSVM), KELM optimized by Tuna Swarm Optimization Algorithm (TSO-KELM), and KELM optimized by SMA (SMA-KELM) in terms of traffic flow prediction accuracy. The proposed model ISMA-KELM provides a promising approach for addressing the challenges of traffic flow prediction, offering improved accuracy and efficiency in real-time traffic management systems.