Intelligent Spatiotemporal Computing

Sun, 08 Mar 2026 00:00:00 +0000

1. The Grand Vision

In the era of Cyber-Physical Systems (CPS) and Autonomous Mobility, understanding the physical world through spatiotemporal data (e.g., trajectories, sensor networks, traffic flows) is the cornerstone of urban intelligence. However, the real physical environment is highly dynamic, strictly resource-constrained, and fraught with uncertainty.

This research direction aims to develop next-generation Intelligent Spatiotemporal Computing paradigms. The ultimate goal is to bridge the fundamental gap between imperfect physical sensing and the urgent need for highly reliable, real-time, and resource-efficient decision-making in complex urban environments.

2. Key Research Questions

As a long-term research agenda, this project seeks to answer three overarching scientific questions in the field of Spatiotemporal AI:

RQ1: Robustness against Physical Uncertainty: How can AI models reliably perceive and reconstruct continuous spatiotemporal dynamics from highly fragmented, sparse, and noise-corrupted sensor streams?
RQ2: Efficiency and Scalability under Constraints: How do we design data-centric computing paradigms (e.g., active sampling, intelligent compression) that dynamically adapt to strict system budgets (bandwidth, edge computing power) without compromising downstream utility?
RQ3: Explainable and Causal Spatiotemporal Reasoning: Beyond black-box forecasting, how can we endow spatiotemporal models with the ability to disentangle complex physical mechanisms and provide causal attributions for safety-critical decisions?

3. Current Milestones & Publications

To address these grand challenges, my ongoing research has established several foundational frameworks, serving as the initial milestones for this agenda:

Tackling RQ1 (Data Recovery): We introduced a progressive chunked Transformer approach that effectively captures long-range dependencies and local topologies, significantly enhancing trajectory reconstruction from low-quality GPS data. (Published in Communications in Transportation Research)

Tackling RQ3 (Interpretability): We advanced the paradigm of spatiotemporal prediction by developing sequential decomposition and attribution mechanisms, enhancing both predictive accuracy and model transparency. (Published in Knowledge-Based Systems)

4. Future Impact

Looking forward, the methodologies developed in this direction will serve as the algorithmic infrastructure for broader applications, including Urban Digital Twins, Low-Altitude Economy (UAV routing), and Autonomous Fleet Management, driving the sustainable evolution of future smart cities.

Urban Knowledge and Semantic Intelligence

Sun, 08 Mar 2026 00:00:00 +0000

1. The Grand Vision

While trajectory coordinates (longitude/latitude) describe how things move in a city, they lack the context of why they move. Real-world urban spaces are not just empty geometric networks; they are profoundly rich in semantics (e.g., points of interest, functional zones, road properties). However, existing spatial graph learning paradigms often struggle to ingest highly heterogeneous, unstructured, and cross-domain urban knowledge.

This research direction aims to build Foundation Models for Urban Spaces. By elegantly bridging the reasoning capabilities of Large Language Models (LLMs) with the structural modeling power of Graph Learning, we seek to create a unified, generalizable semantic representation of urban road networks. This will transform rigid transportation graphs into “knowledge-aware” spatial cognitive systems.

2. Key Research Questions

To realize this vision, my research investigates three critical scientific challenges at the intersection of AI and Urban Computing:

RQ1: Cross-Domain Semantic Alignment: Urban data across different cities or platforms often follow completely different taxonomies (e.g., varying POI categorization standards). How can we leverage the zero-shot reasoning of LLMs to automatically align and unify these heterogeneous labels across disjoint spatial networks?
RQ2: Generalizable Spatial Representation: How do we design a unified semantic graph learning framework that transcends specific local datasets, learning universal road network representations that are highly transferable to new, unseen cities?
RQ3: Regulated Knowledge Integration: Injecting massive textual semantics into geometric graphs can cause “negative transfer” or semantic noise. How can we develop regulated, context-aware gating mechanisms to ensure that only task-relevant urban semantics are fused into road-centric graph representations?

3. Current Milestones & Publications

My recent work has laid the theoretical and methodological groundwork for this agenda:

Tackling RQ1 (Semantic Alignment): We pioneered the use of prompt-driven Large Language Models to solve the notorious cross-taxonomy label alignment problem in transportation networks. This drastically enhances model transferability across diverse urban datasets. (Published in IEEE ITSC 2025)
Tackling RQ2 (Generalizable Frameworks): We developed a comprehensive semantic graph learning architecture specifically designed for road network representation, establishing a new baseline for capturing complex urban structures. (Published in IEEE ITSC 2025)
Tackling RQ3 (Regulated Access): We are currently exploring advanced regulated access mechanisms to intelligently filter and integrate urban semantics into road-centric graphs, ensuring high signal-to-noise ratios in downstream spatial tasks. (Under Review)

4. Future Impact

This agenda pushes the boundaries of Science in the urban domain. The resulting semantic intelligence will empower a wide array of high-level applications, including LLM-driven Urban Planning, Semantic HD Map Generation for Autonomous Driving, and Context-Aware Traffic Management, aligning perfectly with the strategic goals of next-generation smart city governance.

Projects |

Intelligent Spatiotemporal Computing

1. The Grand Vision

2. Key Research Questions

3. Current Milestones & Publications

4. Future Impact

Urban Knowledge and Semantic Intelligence

1. The Grand Vision

2. Key Research Questions

3. Current Milestones & Publications

4. Future Impact