1 code implementation • 30 Jan 2024 • Weijia Zhang, Jindong Han, Zhao Xu, Hang Ni, Hao liu, Hui Xiong
Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments.
no code implementations • 14 Oct 2023 • Jindong Han, Weijia Zhang, Hao liu, Hui Xiong
In this article, we present a comprehensive survey of ML-based air quality analytics, following a roadmap spanning from data acquisition to pre-processing, and encompassing various analytical tasks such as pollution pattern mining, air quality inference, and forecasting.
no code implementations • 31 Aug 2023 • Weijia Zhang, Le Zhang, Jindong Han, Hao liu, Jingbo Zhou, Yu Mei, Hui Xiong
Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system.
1 code implementation • 23 Feb 2023 • Kehua Chen, Yuxuan Liang, Jindong Han, Siyuan Feng, Meixin Zhu, Hai Yang
Accurate traffic prediction is essential for effective urban management and the improvement of transportation efficiency.
no code implementations • 30 Dec 2020 • Jindong Han, Hao liu, HengShu Zhu, Hui Xiong, Dejing Dou
Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.
no code implementations • 25 Oct 2018 • Mingtao Dong, Jindong Han
This paper proposes a new approach based on deep learning and traditional feature engineering called HAR-Net to address the issue related to HAR.