Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data

An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction

Overall, the study shows how conversational surveys, structured data processing, conventional behavioral modeling, machine learning, and multimodal LLM prediction can be coordinated within an auditable multi-agent workflow.

Paris as a 15-Minute City: An Explainable AI Perspective

The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficult to quantify. We investigate this relationship in the Paris metropolitan area using mobility trajectories from the NetMob 2025 Data Challenge, enriched with INSEE sociodemographic data and OpenStreetMap points of interest (POIs), yielding approximately 70,000 trip segments after stop-based segmentation and data cleaning. We construct walking- and cycling-based indicators of local service availability and examine their associations with trip duration, transport mode, and short-trip car use. Higher POI availability is associated with less private motorized travel and more active mobility, although this relationship is substantially weaker in the outer agglomeration. Gradient-boosted tree models interpreted with explainable machine-learning methods consistently identify trip purpose, home--work distance, local service availability, vehicle ownership, public-transport subscription, and sociodemographic context as important predictors. For short trips, high POI density is associated with lower car use, while car ownership and driving-licence availability are associated with higher predicted car use; where services are sparse, public-transport subscription is associated with lower predicted car dependence. Finally, explainable AI (XAI) methods are used to examine how feature attributions change under alternative assumed variable orderings. The results are consistent with central assumptions of the 15-minute city while revealing substantial spatial and demographic heterogeneity. They also demonstrate how explainable machine-learning methods can complement accessibility indicators and identify locally relevant hypotheses for urban-mobility policy.

Mobility tokens with routine-aware learning for LLM-based human mobility prediction

CityWeave: Weaving User Needs and World Constraints for Personalized and Reliable Mobility Planning

Urban door-to-door (D2D) mobility planning is a core task for AI-powered smart cities, requiring models to capture individual mobility behavior and generate optimized plans under real-world urban constraints such as network connectivity and service schedules. Existing methods face fundamental limitations. Optimization-based approaches rely on static costs and fail to capture individual-specific preferences. LLM-agent-based approaches often have weak spatio-temporal reasoning and unstable constraint tracking, which reduces feasibility and reliability. In this study, we propose CityWeave, a VLM-based framework for urban D2D mobility planning that integrates the Who--When--Where--How (3W1H) reasoning paradigm with a two-stage training scheme. CityWeave learns this paradigm through supervised fine-tuning and is further improved by reinforcement learning based enhancement. A dataset of 180,000 real-world samples from 80,000 users is constructed to support training and evaluation. The model learns to identify user needs (Who), reason over departure and arrival time windows (When), read maps and spatial topology (Where), and invoke routing tools (How) to generate feasible plans. We further introduce a unified User--World Grounding (UWG) module that enforces navigation-based world constraints and evaluates personalization with respect to the user profile. Extensive experiments show that CityWeave achieves a state-of-the-art Final Pass Rate of 64.7% and a Commonsense Pass Rate of 92.4%, outperforming both conventional non-LLM planning pipelines and strong LLM-agent baselines. These results demonstrate that structured reasoning over human mobility behavior, combined with explicit user and world grounding, offers a practical path toward reliable and personalized planning agents for smart urban transportation systems.

LifePlanner: Evaluating LLM Agents for Geo-spatial Planning with Social Media Data

Results show that failures mainly stem from incomplete evidence acquisition from such a large multimodal database, imprecise tool use and weak constraint integration rather than model size or reasoning length, suggesting that future progress requires effective grounded planning instead of scaling alone.

CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents

Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that large language models, when prompted as agents, can generate lifelike daily routines at city scale. Yet these methods typically rely on few-shot prompting, causing agents to reproduce the LLM's behavioral priors rather than the target population. We introduce CityReal, a modular framework for human-aligned urban simulation. CityReal models agents as intention-driven decision makers that pursue coherent mobility and activity plans rather than isolated step-by-step choices. They adapt over time by learning habits and preferences based on experience and constraints. To improve population-level realism, we learn textual adapters for behavior modules that align agent decisions with observed population statistics. Experiments show that CityReal improves alignment with real-world human behavior at both micro and macro levels. Scaling to tens of thousands of agents, it supports analysis of crowd density, place popularity, mobility flows, and well-being under different urban scenarios, offering a scalable testbed for urban simulation and forecasting.