An AI-Powered Location-Based Recommendation System for Personalized Urban Mobility

A collective intelligence framework for sequential service recommendation in dynamic social IoT environments

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.

Wanderly: An AI-Powered Full-Stack Travel Planning Web Application Using Large Language Models

The rapid advancement of large language models (LLMs) has created transformative opportunities for intelligent consumer applications. This paper presents Wanderly, a full-stack AI-powered travel planning web application that leverages the LLaMA 3.3 70B model via Groq API to generate personalized, structured travel itineraries from user-specified preferences encompassing budget constraints, travel interests, trip duration, traveler count, and destination. The system architecture integrates a React 18 + Vite frontend with a Node.js/Express backend, Supabase for PostgreSQL-based authentication and data persistence, and a split-deployment pipeline across Vercel and Render. Wanderly addresses the fragmented nature of conventional travel planning by delivering end-to-end itinerary generation, categorical budget breakdowns, curated activity recommendations, contextual insider guidance, destination photo collages, and aggregated booking resource links within a unified platform. Systematic evaluation across 50 test scenarios demonstrates 100% structurally valid JSON responses, 98% destination adherence accuracy, and average AI inference times of 11.3 seconds. This work details system architecture, prompt engineering methodology, multi-interest UX paradigm, deployment pipeline, and a future enhancement roadmap for real-time API integration.

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.

AI-Powered Multi-Agent Personalized Trip Planning Platform Using LLMs, RAG, and Cloud Services

Travel planning is a complex, multi-dimensional challenge requiring travelers to manually synthesize information across fragmented platforms—flight aggregators, hotel portals, weather services, and restaurant guides. The process is time-consuming, error-prone, and poorly adaptive to real-time disruptions. This paper presents an AI-Powered Personalized Trip Planner—a full-stack intelligent system that dynamically generates end-to-end itineraries tailored to individual preferences, budget constraints, and live environmental conditions. The architecture integrates a multi-agent orchestration layer, Large Language Model (LLM) inference augmented by Retrieval-Augmented Generation (RAG), a hybrid collaborative and content-based recommendation engine, and asynchronous real-time APIs for weather, geospatial Points of Interest (POI), transport scheduling, and local events. A Genetic Algorithm solves the multi-constraint Constraint Satisfaction Problem (CSP) to optimize daily schedules. The system generates complete, bookable itineraries in under 30 seconds. Evaluation over 50 diverse trip scenarios demonstrates 91.4% recommendation precision, 96.2% budget adherence, a Mean Opinion Score of 4.6/5, and 88.7% real-time re-planning success rate—substantially advancing the state of the art in intelligent travel planning. Index Terms — Artificial Intelligence, Trip Planner, Large Language Models, Retrieval-Augmented Generation, Multi-Agent Systems, Recommendation Engine, Constraint Satisfaction, Real-Time APIs, Streamlit, NLP.

An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark

The need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories is highlighted, with results highlighting the need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories.