Work

Adaptive-XGBoost for Improved Long-Term Multi-Step Traffic Flow Prediction

Year: 2024

Type: conference-paper

Abstract: Long-term multi-step traffic flow prediction is crucial for effective traffic management.Recently, XGBoost has demonstrated its capability in multi-step prediction applications.However, optimal hyperparameter tuning using adaptive intelligent methods like the Particle Swarm Optimization (PSO) algorithm has yet to be explored.This paper presents an Adaptive eXtreme Gradient Boosting (Adaptive-XGBoost) model utilising the direct method with the combination of Grid Search and PSO algorithms for adaptive hyperparameter tuning for multi-step prediction.This approach aims to demonstrate the potential of Adaptive-XGBoost compared to existing deep learning models like Long-Short Term Memory (LSTM) and Transformer in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).Experimental results show that the Adaptive-XGBoost model achieved a 3.10% and 4.98% improvement in MAE over the Transformer and LSTM models, respectively, and a 0.26% and 5.69% improvement in RMSE.These findings highlight the potential of Adaptive-XGBoost for improved long-term multi-step traffic flow prediction.

Source: Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES)

DOI: 10.5109/7323333

Authors: Mas Omar, Fitri Yakub, Andika Aji Wijaya, Azizul Azizan, Shamsul Faisal Mohd Hussein, Niranjana Govindan

Institutions: University of Technology Malaysia, University of Business and Technology, Universiti Teknikal Malaysia Melaka, SRM Institute of Science and Technology

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4404647714