no code implementations • 23 Feb 2024 • Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang, Zihan Yu, Ruikun Li, Hongyi Chen, Jinghua Piao, Huandong Wang, Jiazhen Liu, Yong Li
Complex networks pervade various real-world systems, from the natural environment to human societies.
no code implementations • 19 Feb 2024 • Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, Yong Li
Urban spatio-temporal prediction is crucial for informed decision-making, such as transportation management, resource optimization, and urban planning.
1 code implementation • 19 Feb 2024 • Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions.
no code implementations • 15 Feb 2024 • Chenyang Shao, Fengli Xu, Bingbing Fan, Jingtao Ding, Yuan Yuan, Meng Wang, Yong Li
In this paper, we design a novel Mobility Generation as Reasoning (MobiGeaR) framework that prompts LLM to recursively generate mobility behaviour.
1 code implementation • 8 Feb 2024 • Hongyi Chen, Jingtao Ding, Yong Li, Yue Wang, Xiao-Ping Zhang
In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap.
1 code implementation • 7 Feb 2024 • Jinwei Zeng, Yu Liu, Jingtao Ding, Jian Yuan, Yong Li
To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE).
no code implementations • 19 Dec 2023 • Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, Yong Li
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
1 code implementation • 19 Sep 2023 • Zhilun Zhou, Jingtao Ding, Yu Liu, Depeng Jin, Yong Li
To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions.
no code implementations • 28 Aug 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Nian Li, Lingling Yi, Depeng Jin, Yong Li
Micro-videos platforms such as TikTok are extremely popular nowadays.
no code implementations • 8 Jun 2023 • Can Rong, Jingtao Ding, Zhicheng Liu, Yong Li
The Origin-Destination~(OD) networks provide an estimation of the flow of people from every region to others in the city, which is an important research topic in transportation, urban simulation, etc.
1 code implementation • 22 May 2023 • Yu Zheng, Hongyuan Su, Jingtao Ding, Depeng Jin, Yong Li
Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs.
2 code implementations • 21 May 2023 • Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, Yong Li
To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
1 code implementation • 15 Mar 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, Yong Li
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
1 code implementation • 25 Feb 2023 • Yu Liu, Xin Zhang, Jingtao Ding, Yanxin Xi, Yong Li
To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction.
1 code implementation • 9 Feb 2023 • Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, Yong Li
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
1 code implementation • 10 Aug 2022 • Yu Zheng, Chen Gao, Jingtao Ding, Lingling Yi, Depeng Jin, Yong Li, Meng Wang
Recommender systems are prone to be misled by biases in the data.
no code implementations • 1 Nov 2021 • Yu Liu, Jingtao Ding, Yong Li
Specifically, motivated by distilled knowledge and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with cities' key elements and semantic relationships captured.
1 code implementation • NeurIPS 2020 • Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.