1 code implementation • 10 Mar 2024 • Liyue Chen, Jiangyi Fang, Tengfei Liu, Shaosheng Cao, Leye Wang
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing.
no code implementations • 17 Aug 2023 • Liyue Chen, Linian Wang, Jinyu Xu, Shuai Chen, Weiqiang Wang, Wenbiao Zhao, Qiyu Li, Leye Wang
For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit.
1 code implementation • 7 Jun 2023 • Liyue Chen, Di Chai, Leye Wang
To address these issues, we design and implement a spatiotemporal crowd flow prediction toolbox called UCTB (Urban Computing Tool Box), which integrates multiple spatiotemporal domain knowledge and state-of-the-art models simultaneously.
no code implementations • 5 Jun 2023 • Liyue Chen, Jiangyi Fang, Zhe Yu, Yongxin Tong, Shaosheng Cao, Leye Wang
In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e. g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem.
1 code implementation • 30 Jun 2021 • Liyue Chen, Xiaoxiang Wang, Leye Wang
Contextual features are important data sources for building citywide crowd mobility prediction models.
1 code implementation • 20 Sep 2020 • Leye Wang, Di Chai, Xuanzhe Liu, Liyue Chen, Kai Chen
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches.