Singapore

AI has proven highly successful at urban planning analysis---learning patterns from data to predict future conditions. The next frontier is AI-assisted decision-making: agents that recommend sites, allocate resources, and evaluate trade-offs while reasoning transparently about constraints and stakeholder values. Recent breakthroughs in reasoning AI---CoT prompting, ReAct, and multi-agent collaboration frameworks---now make this vision achievable. This position paper presents the Agentic Urban Planning AI Framework for reasoning-capable planning agents that integrates three cognitive layers (Perception, Foundation, Reasoning) with six logic components (Analysis, Generation, Verification, Evaluation, Collaboration, Decision) through a multi-agents collaboration framework. We demonstrate why planning decisions require explicit reasoning capabilities that are value-based (applying normative principles), rule-grounded (guaranteeing constraint satisfaction), and explainable (generating transparent justifications)---requirements that statistical learning alone cannot fulfill. We compare reasoning agents with statistical learning, present a comprehensive architecture with benchmark evaluation metrics, and outline critical research challenges. This framework shows how AI agents can augment human planners by systematically exploring solution spaces, verifying regulatory compliance, and deliberating over trade-offs transparently---not replacing human judgment but amplifying it with computational reasoning capabilities.

AAAI 2026

Reasoning Is All You Need for Urban Planning AI

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

workshop paper

Project Sidewalk is a web-based platform that enables crowdsourcing accessibility of sidewalks at city-scale by virtually walking through city streets using Google Street View. The tool has been used in 40 cities across the world, including the US, Mexico, Chile, and Europe. In this paper, we describe adaptation efforts to enable deployment in Chandigarh, India, including modifying annotation types, provided examples, and integrating VLM-based mission guidance, which adapts instructions based on a street scene and metadata analysis. Our evaluation with 3 annotators indicates the utility of AI-mission guidance with an average score of 4.66. Using this adapted Project Sidewalk tool, we conduct a Points of Interest (POI)-centric accessibility analysis for three sectors in Chandigarh with very different land uses---residential, commercial and institutional covering about 40 km of sidewalks. Across ~40 km of roads audited in three sectors and around 230 POIs, we identified 1,644 of 2,913 locations where infrastructure improvements could enhance accessibility.

Towards Human-AI Accessibility Mapping in India: VLM-Guided Annotations and POI-Centric Analysis in Chandigarh

In many real-world sequential predictions, such as forecasting taxi zone profitability, uncertainty quantification—which is often overlooked in traditional prediction models—is essential for trustworthy decision-making by taxi drivers. Traditional conformal prediction (CP) offers a distribution-free, model-agnostic approach to construct prediction intervals, but its assumption that data points are identically and independently distributed (i.i.d.) fails in temporal data. This work applies sequential CP to quantify uncertainty in time-series profitability predictions for taxi zones, marking the first such application in this context. Feedback from prediction residuals is utilized to ensure user-specified statistical coverage while minimizing interval widths. Based on the empirical Hong Kong taxi trip and trajectory data from February to July 2025, we evaluate state-of-the-art time-series models under split and sequential CP variants. Results demonstrate superior empirical coverage compared to baselines, highlighting the advantage of utilizing residuals for sequential uncertainty quantification. This advances uncertainty-aware forecasting and bridges theoretical guarantees with practical mobility applications.

Enhancing Trust in Taxi Zone Profit Predictions with Sequential Conformal Inference

Human mobility prediction is a critical task but remains challenging due to its complexity and variability across populations and regions. Recently, large language models (LLMs) have made progress in zero-shot prediction, but existing methods suffer from limited interpretability (due to black-box reasoning), lack of iterative learning from new data, and poor transferability. In this paper, we introduce \textbf{ARMove}, a fully transferable framework for predicting human mobility through agentic reasoning. To address these limitations, ARMove employs standardized feature management with iterative optimization and user-specific customization: four major feature pools for foundational knowledge, user profiles for segmentation, and an automated generation mechanism integrating LLM knowledge. Robust generalization is achieved via agentic decision-making that adjusts feature weights to maximize accuracy while providing interpretable decision paths. Finally, large-small model synergy distills strategies from large LLMs (e.g., 72B) to smaller ones (e.g., 7B), reducing costs and enhancing performance ceilings. Extensive experiments on four global datasets show ARMove outperforms state-of-the-art baselines on 6 out of 12 metrics (gains of 0.78\% to 10.47\%), with transferability tests confirming robustness across regions, users, and scales. The other 4 items also achieved suboptimal results. Transferability tests confirm its 19 robustness across regions, user groups, and model scales, while interpretability 20 analysis highlights its transparency in decision-making. Our codes are available at: \url{https://anonymous.4open.science/r/ARMove-F847}.

ARMove: Learning to Predict Human Mobility through Agentic Reasoning

Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir’s metadata, reconciling DG’s invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.

HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

Landmine contamination poses a significant threat in post-conflict regions, endangering civilian populations even decades after hostilities end. Demining efforts are resource-intensive, and prioritizing areas for clearance is particularly challenging in regions with widespread contamination. This paper presents a country-wide Digital Twin for landmine risk assessment, addressing key challenges: (a) modeling of a country-wide Digital Twin, (b) data integration, enrichment and augmentation, and (c) efficient machine learning (ML) training. The system is deployed in Cambodia premises, where it streamlines the development of ML workflows and achieves up to 91% accuracy in assessing risks of landmine areas.

Country-Wide Digital Twin for Humanitarian AI: The Case Study of Cambodian Mine Action

Financial risk assessment plays a vital role in accelerating the global energy transition and achieving the 1.5°C climate target. This study develops a three-stage machine learning framework to analyze the impact of fossil fuel price volatility on the weighted average cost of capital (WACC) for energy transition projects. First, a hybrid Long Short-Term Memory-Gaussian Regression Process (LSTM-GRP) model forecasts fossil fuel prices, followed by an LSTM-based inflation predictor. In the final stage, an AutoML (AutoGluon) regression model evaluates how variations in fossil fuel prices and inflation influence WACC across nine energy sectors and multiple global regions. 

The results show that the WACC in energy-importing regions like Europe (R²=0.67) and China(R²=0.64) is highly sensitive to price shocks, making their decarbonization pathways vulnerable to external market instability. Conversely, the energy-exporting countries in Middle East (R²=0.54) exhibit lower sensitivity, indicating a fiscal cushion that may reduce the economic impetus for transition. The findings demonstrate how energy price volatility shapes financing conditions and investment risks for renewable energy deployment. This approach integrates AI-driven forecasting with climate finance modeling, providing an interpretable and data-driven tool to support policy design, green investment strategies, and climate-resilient energy planning.

Fossil Fuel Prices and Capital Cost: A Machine Learning Driven Study on Energy Transition

Ecological studies on depression have explored the complex associations between environmental, social, and economic factors and mental health outcomes, but they often fall short of answering causal questions, which can limit the effectiveness of the interventions. To demonstrate the potential of machine learning in uncovering the causal mechanisms behind antidepressant prescriptions, which are overwhelmingly used to treat depressive disorders, we systematically examined 28,640 small geographical areas in England, each labeled with 27 socio-demographic and environmental indicators, as well as with the total annual per capita antidepressant prescriptions. Specifically, we employed a novel approach that integrates statistical analysis, machine learning, and domain expertise. Our results highlight the pivotal roles of ethnicity, green spaces, and dense urban structure as indirect causal links shaping antidepressant prescriptions, potentially mediated by hidden variables such as cultural attitudes and the likelihood of experiencing depressive symptoms. To validate our findings, we compared them with previous research, statistical modeling of ecological data, and results obtained through querying Large Language Models about causal links. Our causal inference approach showed efficacy in determining information flow directions and unveiling subtle relationships by considering a web of causation. Specifically, the results aligned for the most part with existing research, such as the complex associations with employment and economic conditions. Moreover, the findings also brought up some connections that warrant further research with individual-level data, including different effects from tree cover versus NDVI greenery.

Ecological Determinants of Antidepressants Prescriptions in England: Using Machine Learning for Causal Discovery

The widespread adoption of dynamic Time-of-Use (dToU) electricity tariffs requires accurately identifying households that would benefit from such pricing structures. However, the use of real consumption data poses serious privacy concerns, motivating the adoption of synthetic alternatives. In this study, we conduct a comparative evaluation of four synthetic data generation methods, Wasserstein–GP Generative Adversarial Networks (WGAN), Conditional Tabular GAN (CTGAN), Diffusion Models, and Gaussian noise augmentation, under different synthetic regimes. We assess classification utility, distribution fidelity, and privacy leakage. Our results show that architectural design plays a key role: diffusion models achieve the highest utility (macro-F1 up to 88.2%), while CTGAN provide the strongest resistance to reconstruction attacks. These findings highlight the potential of structured generative models for developing privacy-preserving, data-driven energy systems.

Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data

Weather forecasting offers an ideal testbed for artificial in-telligence to learn complex, multiscale physical systems. Traditional numerical weather prediction remains compu-tationally costly for frequent regional updates, as high-resolution nests require intensive boundary coupling. We introduce Multi-Resolution Graph Neural Forecasting (MR-GNF), a lightweight, physics-aware model that per-forms short-term regional forecasts directly on an ellipsoi-dal, multi-scale graph of the Earth. The framework cou-ples a 0.25° region of interest with a 0.5° context belt and 1.0° outer domain, enabling continuous cross-scale mes-sage passing without explicit nested boundaries. Its axial graph-attention network alternates vertical self-attention across pressure levels with horizontal graph attention across surface nodes, capturing implicit 3-D structure in just 1.6 M parameters. Trained on 40 years of ERA5 rea-nalysis (1980–2024), MR-GNF delivers stable +6 h to +24 h forecasts for near-surface temperature, wind, and precip-itation over the UK-Ireland sector. Despite a total com-pute cost below 80 GPU-hours on a single RTX 6000 Ada, the model matches or exceeds heavier regional AI systems while preserving physical consistency across scales. These results demonstrate that graph-based neural operators can achieve trustworthy, high-resolution weather prediction at a fraction of NWP cost opening a practical path toward AI-driven early-warning and renewable-energy forecasting systems.

MR-GNF: Multi-Resolution Graph Neural Forecasting on Ellipsoidal Meshes for Efficient Regional Weather Prediction

The accelerating pace of global warming, confirmed by the Intergovernmental Panel on Climate Change (IPCC), underscores the urgent need for continuous environmental monitoring and adaptive modeling frameworks. Therefore, this study explores AI-driven temperature forecasting across New Zealand using real observations from the national MetService network within the TAIAO data science programme. We evaluate a diverse set of models, from statistical baselines to deep neural networks, foundation models, and graph-based architectures, to assess their capacity for adaptive, spatially aware prediction. Our results show that graph-based representations substantially improve the modeling of spatial and temporal dependencies, while foundation models demonstrate robust generalization across diverse climatic regions. The integration of these paradigms produces forecasts that are both more accurate and more interpretable. The findings highlight the potential of adaptive AI frameworks to improve environmental monitoring, detect regional anomalies, and strengthen climate resilience strategies in New Zealand.

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Towards Human-AI Accessibility Mapping in India: VLM-Guided Annotations and POI-Centric Analysis in Chandigarh

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