Singapore

In this paper, we study the vehicle routing problem with a finite time horizon. In this routing problem, the objective is to maximize the number of customer requests served within a finite time horizon. We present a novel routing network embedding module which creates local node embedding vectors and a context-aware global graph representation. The proposed Markov decision process for the vehicle routing problem incorporates the node features, the network adjacency matrix and the edge features as components of the state space. We incorporate the remaining finite time horizon into the network embedding module to provide a proper routing context to the embedding module. We integrate our embedding module with a policy gradient-based deep Reinforcement Learning framework to solve the vehicle routing problem with finite time horizon. We trained and validated our proposed routing method on real-world routing networks, as well as synthetically generated Euclidean networks. Our experimental results show that our method achieves a higher customer service rate than the existing routing methods. Additionally, the solution time of our method is significantly lower than that of the existing methods.

AAAI 2026

Vehicle Routing with Finite Time Horizon using Deep Reinforcement Learning with Improved Network Embedding

intelligent transportation

planning and learning

vehicle routing

reinforcement learning

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




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ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

workshop paper

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.

Urban development plays a crucial role in promoting environmental sustainability. Most existing AI-driven urban studies focus on using satellite imagery and deep learning for monitoring and understanding past urban development, whereas urban development is inherently a forward-looking, generative process aimed at envisioning future cities. We present UrbanControlNet, a multimodal generative AI framework that reimagines global urban development through high-fidelity and controllable satellite imagery generation. By integrating digital elevation models, road networks, and urban density metric conditioning, UrbanControlNet generates realistic and counterfactual urban imagery. Trained on millions of samples from 500 global cities, UrbanControlNet supports visual cross-city transfer, scenario exploration, and generative augmentation for downstream tasks (e.g., global fossil fuel carbon emission prediction). The framework bridges urban analytics and generative design, offering a powerful tool and assistant for global urban planning. The codes and models: https://github.com/kailaisun/UrbanControlNet.

UrbanControlNet: Reimaging Global Urban Development with Satellite Imagery

Computer graphics, comprising both raster and vector components, is a fundamental part of modern science, industry, and digital communication. While raster graphics offer ease of use, their pixel-based structure limits scalability. Vector graphics, defined by mathematical primitives, provide scalability without quality loss, it is more complex to produce. For design and architecture, the versatility of vector graphics is paramount, despite its computational demands. This paper introduces a novel method for generating vector residential plans from the textual descriptions. Our approach surpasses existing solutions by approximately 5% in CLIPScore-based visual quality, benefiting from its inherent handling of right angles and flexible settings. Additionally, we present a new algorithm for vectorizing raster plans into structured vector images.

Text-to-Vector Conversion for Residential Floor Plan Design

The widespread deployment of electric vehicles (EVs) has made charging demand prediction crucial for urban resource allocation. However, existing studies primarily focus on improving overall predictive accuracy while overlooking prediction fairness. More importantly, explanation fairness has been overlooked across the broader traffic forecasting literature, despite its critical role in ensuring transparency and accountability in public resource allocation. To address these limitations, we introduce two fairness metrics that evaluate regional disparities in prediction errors and explanation quality. We then develop $DualFairNet$, a general fairness-enhancing framework compatible with various spatiotemporal graph neural networks $(ST\text{-}GNNs)$. The framework includes an adaptive attention module jointly guided by prediction error and explanation quality, which dynamically adjusts the graph structure to emphasize regions with poor prediction accuracy or weak explanation quality, and a multi-objective loss function with dual fairness regularization terms, enabling simultaneous optimization of accuracy, result fairness, and explanation fairness. Experiments on a real-world dataset demonstrate that $DualFairNet$ substantially improves fairness—reducing prediction error and explanation quality disparities by 20.18% and 27.37% respectively—with negligible impact on accuracy (e.g., 0.16% increase in Mean Absolute Percentage Error). $DualFairNet$ contributes to building fair intelligent charging systems and serves as a general framework for multiple $ST\text{-}GNN$ models.

DualFairNet: Enhancing Prediction and Explanation Fairness in Spatiotemporal Graphs With Electric Vehicle Charging Demand as a Case Study

Text-to-image diffusion models help visualize urban futures but can amplify group-level harms. We propose collective recourse: structured community "visual bug reports" that trigger fixes to models and planning workflows. We (1) formalize collective recourse and a practical pipeline (report, triage, fix, verify, closure); (2) situate four recourse primitives—counter-prompts, negative prompts, dataset edits, reward-model tweaks—within the diffusion stack; (3) define mandate thresholds via a mandate score combining severity, volume saturation, representativeness, and evidence; and (4) evaluate a synthetic program of 240 reports. Prompt-level fixes were fastest (median $2.1$-$3.4$ days) but less durable (21-38\% recurrence); dataset edits and reward tweaks were slower (13.5 and 21.9 days) yet more durable (12-18\% recurrence) with higher planner uptake (30-36\%). A threshold of $0.12$ yielded 93\% precision and 75\% recall; increasing representativeness raised recall to 81\% with little precision loss. We discuss integration with participatory governance, risks (overfitting to vocal groups), and safeguards (dashboards, rotating juries).

Collective Recourse for Generative Urban Visualizations

Urban digital twins represent a paradigm shift in city modeling, yet current data-driven approaches operate as black boxes, fundamentally incapable of reasoning about the regulatory constraints and policy frameworks that govern urban development. We introduce Neuro-Symbolic Urban Twins (NS-UT), a novel architecture that integrates neural perception with symbolic reasoning to enable compliant, interpretable urban simulation. Our framework features: (1) multimodal encoders for satellite imagery, IoT sensor data, and demographic information; (2) a symbolic knowledge base encoding zoning codes, building regulations, and sustainability policies; (3) an LLM-based semantic parser that automatically translates natural language regulations into executable logical constraints; and (4) a differentiable neuro-symbolic interface enabling joint optimization. We evaluate NS-UT on a newly curated Urban Regulatory Compliance (URC) benchmark comprising 15,000 development scenarios across five major U.S. cities. NS-UT achieves state-of-the-art performance with 94.3% compliance prediction accuracy (p < 0.001 vs. baselines) while providing human-interpretable explanations for violations and recommendations. This work establishes a new foundation for policy-aware urban AI that respects the complex rule systems shaping our cities.

Neuro-Symbolic Urban Twins: Embedding Reasoning in Data-Driven City Models

Ensuring safe and inclusive mobility for vulnerable older adults is an emerging priority in urban planning. However, existing data sources such as surveys or GIS-based audits provide limited insight into how micro-scale built environment (BE) features influence real-world behavior and perception. This study presents a novel multimodal data-fusion approach that integrates wearable and environmental sensing to dynamically represent human-environment interactions and quantify the BE impacts on mobility among vulnerable older adults, specifically, those with knee osteoarthritis or a history of falls. Data collected during naturalistic walking sessions in Singapore, are used to demonstrate this framework of synchronized streams from eye tracking, kinematic sensors, physiological monitors, GPS, and video recordings. Preliminary results show how AI-driven data fusion can uncover behaviorally and perceptually significant urban segments, providing a basis for actionable insights in inclusive design. This human-centered analytical approach advances the representation of urban environments from the perspective of vulnerable pedestrians, establishing a foundation for evidence-based, age-friendly city planning.

Multimodal Data Fusion to Capture Dynamic Interactions between Built Environment and Vulnerable Older Adults

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.

Reasoning Is All You Need for Urban Planning AI

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}.

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UrbanControlNet: Reimaging Global Urban Development with Satellite Imagery

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