Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical observations for a pre-defined prediction task. Different from the existing research, this paper aims to provide a generic and dynamic representation learning method for crowd flow modeling. The main idea of our method is to maintain a continuous-time representation for each node, and update the representations of all nodes continuously according to the streaming observed data. Along this line, a particular encoder-decoder architecture is proposed, where the encoder converts the newly happened transactions into a timestamped message, and then the representations of related nodes are updated according to the generated message. The role of the decoder is to guide the representation learning process by reconstructing the observed transactions based on the most recent node representations. Moreover, a number of virtual nodes are added to discover macro-level spatial patterns and also share the representations among spatially-interacted stations. Experiments have been conducted on two real-world datasets for four popular prediction tasks in crowd flow modeling. The result demonstrates that our method could achieve better prediction performance for all the tasks than baseline methods.

Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate this planning process and come up with the ideal urban plans. While remarkable achievements have been obtained, they have exhibited limitations in lacking awareness of: 1) the hierarchical dependencies between functional zones and spatial grids; 2) the peer dependencies among functional zones; and 3) human regulations to ensure the usability of generated configurations. To address these limitations, we develop a novel human-instructed deep hierarchical generative model. We rethink the urban planning generative task from a unique functionality perspective, where we summarize planning requirements into different functionality projections for better urban plan generation. To this end, we develop a three-stage generation process from a target area to zones to grids. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. We propose a novel module: functionalizer to project the embedding of human instructions and geospatial contexts to the zone-level plan to obtain such projections. Each projection includes the information of land-use portfolios and the structural dependencies across spatial grids in terms of a specific urban function. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations. Finally, we present extensive experiments to demonstrate the effectiveness of our framework. 

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, that produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile. In the experiment, we evaluate our proposed framework on real-world datasets. The extensive experimental results validate the superiority of our method comparing to baseline algorithms.

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

Posters & Demos IV

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

DMKM: Mining of Spatial, Temporal Data / Linked Open Data, Knowledge Graphs

Main Track

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

Yanjie Fu

3

Presentations

Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling

Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

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