Multi-step traffic flow prediction using stacking ensemble learning model

Shi Yin; Hui Liu; Yanfei Li; Jing Tan; Jiakang Wang

doi:10.1117/12.2620226

22 December 2021 Multi-step traffic flow prediction using stacking ensemble learning model

Shi Yin, Hui Liu, Yanfei Li, Jing Tan, Jiakang Wang

Author Affiliations +

Proceedings Volume 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021); 120582M (2021) https://doi.org/10.1117/12.2620226
Event: 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 2021, Chongqing, China

Abstract

The prediction of traffic flow can supply a powerful reference for urban traffic planning. A multi-step traffic flow prediction model using stacking ensemble learning algorithm is constructed in this paper, which ensembles three Elman Neural Networks (ENN) as base learn and a Support Vector Machine (SVM) as meta-learner. Experimental results prove that the good multi-step prediction effect can be achieved by the proposed stacking ensemble model, and compared with Autoregressive Integrated Moving Average (ARIMA), ENN, SVM and Echo State Networks (ESN), the proposed model can achieve the highest prediction accuracy regardless of 1, 2 or 3 step forecasting.

Citation Download Citation

Shi Yin, Hui Liu, Yanfei Li, Jing Tan, and Jiakang Wang "Multi-step traffic flow prediction using stacking ensemble learning model", Proc. SPIE 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 120582M (22 December 2021); https://doi.org/10.1117/12.2620226

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