Abstract. Traffic speed prediction can benefit a wide range of IoT appli- cations in intelligent transportation and smart city. Recent su-.

Transfer Learning for Traffic Speed Prediction: A Preliminary Study. June 20, 2018. Authors. Track: Papers. Downloads: Download PDF.

Traffic speed prediction can benefit a wide range of downstream applications of machine learning for intelligent transportation systems.

Transfer Learning for Traffic Speed Prediction: A Preliminary Study. Bill Y. Lin, Frank F. Xu, Eve Q. Liao, Kenny Q. Zhu. Transfer Learning for Traffic Speed ...

2024/04/25 · Bill Y. Lin, Frank F. Xu, Eve Q. Liao, Kenny Q. Zhu: Transfer Learning for Traffic Speed Prediction: A Preliminary Study.

The proposed model utilizes a predictor-regularizer architecture to embed the spatial-temporal data correlation of traffic dynamics in the prediction process.

This paper has proposed a framework with DNN and transfer learning to improve the transferability of a machine learning based short-term traffic prediction ...

2024/04/14 · This paper proposes a method based on the clustering algorithm, deep learning, and transfer learning (TL) for short-term traffic prediction with limited data.

Zhu, “Transfer learning for traffic speed prediction: A preliminary study,” in Proc. Workshops Thirty-Second AAAI Conf. Artificial Intelligence, 2018, pp ...

2021/11/29 · To tackle the above problems, we propose a TransfEr lEarning approach with graPh nEural nEtworks (TEEPEE) for traffic prediction that can ...