arXiv AI
Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer
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arXiv AI
Aug 19
CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents
CityReal is a modular framework that uses large language model agents to simulate human-aligned urban behavior. It models agents as intention-driven decision makers who pursue coherent mobility and activity plans, learning habits and preferences over time. By training textual adapters to align agent decisions with observed population statistics, CityReal improves realism at both micro and macro levels and can scale to tens of thousands of agents for analyzing crowd density, place popularity, mobility flows, and well‑being under various urban scenarios.
arXiv AI
Jul 2
Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization
arXiv:2507. 02961v2 Announce Type: replace-cross Abstract: Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation.
arXiv AI
Oct 19
Towards Adaptive and Real-Survey-Aligned Simulated Urban Mobility Behaviour of Generative Agents
The paper presents an agent‑based model of daily multimodal mobility in Toulouse, comparing fifteen decision‑making approaches—including generative agents (GAs) with minimal and tuned prompts, simple baselines, survey‑fitted models, and a typed classifier—against real household travel survey data. Results show that GAs with tuned prompts approximate the modal split of a simulated day without disruptions but fall short on trip‑by‑trip accuracy, while the Jev‑1.13 TypeSafe classifier performs surprisingly well, indicating that generating output text is not essential. The study highlights the potential and current limitations of generative agent‑based modelling for urban mobility.
arXiv AI
Jun 16
ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing
arXiv:2606. 15315v1 Announce Type: new Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms.
arXiv AI
Jun 2
Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
arXiv:2606. 00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations.
arXiv AI
Oct 5
LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling
The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.