CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform

• URL: http://arxiv.org/abs/2607.12086v1
• Date: Mon, 13 Jul 2026 19:03:25 GMT
• Title: CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform
• Abstract summary: We present CityBehavEx, an interactive LLM-assisted urban simulation platform that scales to city-size populations.<n>Instead of invoking large language models for every agent action, CityBehavEx combines established human mobility models with fine-tuned cross-encoders.<n>This design enables large-scale simulations, as demonstrated in a case study of 100,000 agents over 75 days in under one hour on a single consumer GPU.
• Score: 1.7205106391379026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent LLM-based multi-agent urban simulators can generate semantically rich city routines, but they remain costly to scale and are often weakly validated against empirical mobility patterns. We present CityBehavEx, an interactive LLM-assisted urban simulation platform that scales to city-size populations, exposes agent behavior for inspection, supports empirical validation, and generates mobility patterns that better match real-world spatial, temporal, and semantic distributions. Instead of invoking large language models for every agent action, CityBehavEx combines established human mobility models with fine-tuned cross-encoders that estimate semantic alignment between agent profiles, schedules, and activity transitions. This design enables large-scale simulations, as demonstrated in a case study of 100,000 agents over 75 days in under one hour on a single consumer GPU. The platform allows users to define simulation regions, launch experiments, inspect trajectories and activity traces, debug unrealistic behaviors, and validate generated routines against real-world mobility, time-use, and semantic metrics.

Related papers

• When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation [1.7205106391379026]
  We introduce a validation framework for evaluating the mobility of generative agents of urban simulators against real-world data.<n>Our analysis reveals a substantial gap between narrative plausibility and empirical mobility realism.
  arXiv  Detail & Related papers  (2026-06-11T19:03:52Z)
• Mind the Sim2Real Gap in User Simulation for Agentic Tasks [101.69142591891234]
  We formalize the Sim2Real gap in user simulation and present the first study running the full $$-bench protocol with real humans.<n> Behaviorally, LLM simulators are excessively cooperative, stylistically uniform, and lack realistic frustration or ambiguity.<n>These findings highlight the importance of human validation when using LLM-based user simulators in the agent development cycle.
  arXiv  Detail & Related papers  (2026-03-11T19:12:31Z)
• CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation [1.2430809884830318]
  We envision an urban simulator (CitySim), capitalizing on breakthroughs in human-level intelligence exhibited by large language models.<n>In CitySim, agents generate realistic daily schedules using a value-driven approach that balances mandatory activities, personal habits, and situational factors.<n>CitySim exhibits closer alignment with real humans than prior work, both at micro and macro levels.
  arXiv  Detail & Related papers  (2025-06-26T23:11:42Z)
• MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation [24.339278159507035]
  We present MobileCity, a lightweight simulation platform designed to model realistic urban mobility with high computational efficiency.<n>We introduce a comprehensive transportation system with multiple transport modes, and collect questionnaire data from respondents to construct agent profiles.<n>To enable scalable simulation, agents perform action selection within a pre-generated action space and uses local models for efficient agent memory generation.
  arXiv  Detail & Related papers  (2025-04-18T07:01:05Z)
• TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation [3.8106509573548286]
  This work leverages Large Language Models (LLMs) to simulate human mobility, addressing challenges like high costs and privacy concerns in traditional models.<n>Our hierarchical framework integrates persona generation, activity selection, and destination prediction, using real-world demographic and psychological data.
  arXiv  Detail & Related papers  (2025-02-26T00:13:26Z)
• OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents [10.919679349212426]
  Large Language Models (LLMs) have led to the development of LLM agents capable of simulating urban activities with unprecedented realism. We propose OpenCity, a scalable simulation platform optimized for both system and prompt efficiencies. OpenCity achieves a 600-fold acceleration in simulation time per agent, a 70% reduction in LLM requests, and a 50% reduction in token usage.
  arXiv  Detail & Related papers  (2024-10-11T13:52:35Z)
• GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
  We propose a novel large language model (LLMs)-based simulation platform called textitGenSim.<n>Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts.<n>To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
  arXiv  Detail & Related papers  (2024-10-06T05:02:23Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.