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Title	A Novel Adaptive Ensemble Model Framework For Short-Term Traffic Flow Prediction Based On Model Selection And Multi-Objective Optimization
ID_Doc	3199
Authors	Li Y.; Liu H.; Li Y.; Cao Z.; Duan Z.
Year	2021
Published	Proceedings of SPIE - The International Society for Optical Engineering, 12058
DOI	http://dx.doi.org/10.1117/12.2620238
Abstract	Short-term traffic prediction is an important technology for smart city management and scheduling, which can provide accurate predictions for the future status of various traffic systems in the city, such as road traffic and subway traffic. In this paper, an adaptive ensemble prediction model that integrates single-objective model selection, multi-objective ensemble, and deep learning models is proposed. Several deep learning models are trained first, which forms a prediction model pool. A single-objective optimization algorithm is used to perform model selection by selecting several base models from the pool, then a multi-objective optimization algorithm is used for integrating the dynamically selected base models. The model performance is evaluated in a dataset collected from a real-world road node. The results reveal that the proposed adaptive model framework has superior prediction ability. © 2021 SPIE
Author Keywords	adaptive ensemble learning; compromise solution; deep learning; model selection; multi-objective optimization; Pareto front; single-objective optimization; traffic prediction

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