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This study proposes a ring-based Spatial Transformer to model non-linear spatial interactions between building distribution and pedestrian flow around railway stations in Tokyo, outperforming Geographically Weighted Regression and challenging compact city assumptions in urban planning.
This paper measures the implicit assumptions language models make about 'a city' by scoring anonymized urban profiles across 40 indicators, finding a shared preference for larger, faster-growing, and more infrastructure-rich cities. It uses open-weight checkpoints and replication data to make the default portrait of cities in LLMs empirically traceable.
Queensland will trial Australia's first AI-powered traffic lights, raising questions about fairness and who gets priority on public roads, with potential broader implications for urban design.
The article explores the 3-30-300 rule for urban tree coverage—three trees in view, 30% canopy coverage, and within 300m of a park—and discusses its adoption and impact on mental and physical health.
This paper introduces two inverse optimal transport estimators to recover latent choice costs from observed origin-destination flows, applied to Philippine school choice data to quantify how much perceived travel cost an education subsidy offsets.
This paper introduces UPBench, a benchmark to evaluate large language models on urban planning knowledge across four knowledge pillars and five cognitive levels, finding that models perform better on higher-order analysis than factual recall, and identifying epistemic limitations such as regulatory hallucination and phronetic deficit.
A new study in PNAS introduces the concept of an 'urban pulse' measured via remote sensing data, revealing three key vital signs of urbanization that could inform urban planning policy.
This paper introduces PlanBench-V, the first comprehensive benchmark for evaluating Vision-Language Models on spatial planning map interpretation, including an expert-annotated dataset and a four-dimension evaluation framework. Experiments show significant progress but highlight persistent challenges in implementation-oriented tasks.
This research introduces the Housing Potential Common Data Model (HPCDM) to integrate diverse datasets for housing analysis and demonstrates its application through a City Digital Twin pilot.
This academic paper introduces an AI-enabled analytics framework using existing CCTV infrastructure to evaluate the impact of soft traffic interventions on vehicle speed and safety at urban intersections.