Mohebbi Najmabad, Mohammadreza (Josef Ressel Center Vision2Move, University of Applied Sciences ), Hassan Zada, Mohammad Javad (Technical University of Munich), Rostami-Shahrbabaki, Majid (Technical University of Munich), Döller, Mario (University of Applied Sciences FH Kufstein Tirol), Yamnenko, Iuliia (NTUU KPI, TUM)

Network Traffic Co-Movement Assessment Via Oriented Basis Signal Processing and Ensemble Decision Trees

Scheduled for presentation during the Poster Session "Traffic prediction and estimation III" (ThAT13), Thursday, September 26, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical Machine Learning (ML) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid Random Forest (RF) and Extreme Gradient Boosting Tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise Oriented Basis wavelet Transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the Paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.