Prabin Bhandari

Simulating human behavior in real-world environments is important for synthetic data generation, policy analysis, and scenario planning. Agent-based models (ABMs) are widely used for this purpose, but they often rely on hand-crafted rules and simplified assumptions that limit their realism, behavioral diversity, and adaptation to changing conditions. Large language models (LLMs) offer a promising alternative as decision engines for ABMs since they can condition agent behavior on agent state, environmental context, memory, and world knowledge. We present UrbanLLMind, a scalable generative agent-based model for urban mobility simulation. In UrbanLLMind, agents inhabit a synthetic city constructed from geographic and demographic data and act through a daily loop of LLM-powered planning, step-level mobility decisions, and end-of-day reflection. Agents also maintain a long-term memory system that allows them to recall past experiences and preserve consistent personas over time. UrbanLLMind is designed to run with open-weight LLMs on local cluster infrastructure. In our evaluations, UrbanLLMind generates plausible mobility patterns and, when powered by a fine-tuned LLM, aligns closely with observed distributions (surveys). Moreover, the model is sensitive to external behavioral drivers, such as extreme weather. We show that UrbanLLMind is capable of simulating 10,000 agents powered by an open-weight 120B-parameter model using a vLLM architecture on local cluster infrastructure. Together, these results position UrbanLLMind as the first controllable, city-grounded testbed for large-scale urban mobility simulation and scenario analysis.

In urban science, understanding mobility patterns is essential for improving the quality of life and designing livable, efficient, and sustainable cities. However, collecting such data through user tracking or travel surveys poses challenges due to privacy concerns, non-compliance, and high cost. This work proposes an AI-based approach for synthesizing travel surveys by prompting large language models (LLMs), leveraging their background knowledge and text generation capabilities. We evaluate the effectiveness of this method across five major U.S. metropolitan areas by comparing LLM-generated results with existing survey data at three different levels of granularity: (i) pattern level, which compares aggregated metrics like average locations visited and travel time, (ii) trip level, which compares trips as whole units using transition probabilities, and (iii) activity chain level, which examines the sequence of visited places. Our results indicate that fine-tuning an open-weight Llama-3.1 model on a balanced dataset helps approximate age-specific mobility profiles, producing outputs that closely resemble demographic realities. Furthermore, we observe that the model captures implicit seasonal variations in mobility patterns, reproducing fluctuations associated with temperature despite not being given explicit climate inputs. These findings suggest that LLMs, when fine-tuned on as few as 10,000 survey records, can generate synthetic travel data that approximates behavioral realism in major urban contexts. While further validation is needed for non-metropolitan areas and granular transportation metrics, this approach offers a scalable, low-cost, and privacy-preserving complement for urban mobility analysis.

Epidemiological datasets are essential for public health analysis and decision-making, yet they remain scarce and often difficult to compile due to inconsistent data formats, language barriers, and evolving political boundaries. Traditional methods of creating such datasets involve extensive manual effort and are prone to errors in accurate location extraction. To address these challenges, we propose utilizing large language models (LLMs) to automate the extraction and geotagging of epidemiological data from textual documents. Our approach significantly reduces the manual effort required, limiting human intervention to validating a subset of records against text snippets and verifying the geotagging reasoning, as opposed to reviewing multiple entire documents manually to extract, clean, and geotag. Additionally, the LLMs identify information often overlooked by human annotators, further enhancing the dataset’s completeness. Our findings demonstrate that LLMs can be effectively used to semi-automate the extraction and geotagging of epidemiological data, offering several key advantages: (1) comprehensive information extraction with minimal risk of missing critical details; (2) minimal human intervention; (3) higher-resolution data with more precise geotagging; and (4) significantly reduced resource demands compared to traditional methods.

In urban science, understanding mobility patterns and analyzing how people move around cities helps improve the overall quality of life and supports the development of more livable, efficient, and sustainable urban areas. A challenging aspect of this work is the collection of mobility data through user tracking or travel surveys, given the associated privacy concerns, noncompliance, and high cost. This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs), aiming to leverage their vast amount of relevant background knowledge and text generation capabilities. Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different granularity levels. These levels include (i) pattern level, which compares aggregated metrics such as the average number of locations traveled and travel time, (ii) trip level, which focuses on comparing trips as whole units using transition probabilities, and (iii) activity chain level, which examines the sequence of locations visited by individuals. Our work covers several proprietary and open-source LLMs, revealing that open-source base models like Llama-2, when fine-tuned on even a limited amount of actual data, can generate synthetic data that closely mimics the actual travel survey data and, as such, provides an argument for using such data in mobility studies.

Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension of geographic data and related ability to facilitate informed geospatial decision-making. This paper investigates the extent of geospatial knowledge, awareness, and reasoning abilities encoded within such pretrained LLMs. With a focus on autoregressive language models, we devise experimental approaches related to (i) probing LLMs for geo-coordinates to assess geospatial knowledge, (ii) using geospatial and non-geospatial prepositions to gauge their geospatial awareness, and (iii) utilizing a multidimensional scaling (MDS) experiment to assess the models’ geospatial reasoning capabilities and to determine locations of cities based on prompting. Our results confirm that it does not only take larger but also more sophisticated LLMs to synthesize geospatial knowledge from textual information. As such, this research contributes to understanding the potential and limitations of LLMs in dealing with geospatial information.

Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in generating children’s stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of children’s stories generated by LLMs using various measures, and we compare and contrast our results with both old and new children’s stories to better assess their significance. Our findings suggest that LLMs still struggle to generate children’s stories at the level of quality and nuance found in actual stories.