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

Abstract

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.