This Advanced Seminar provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) for Urban Mobility applications. The presented work in this seminar is categorized based on how LLMs are employed to serve various urban mobility applications. This goes from employing LLMs as a black box with a bit of prompt engineering, to fine-tuning LLMs to fit urban mobility applications, to completely retrain a vanilla LLM architecture with urban mobility data, to modifying the internal LLM loss function to fit urban mobility applications. The seminar concludes by presenting a set of benchmarking and evaluation work while pointing out to research gaps, open problems, and future research directions for employing LLMs to urban mobility applications.

This tutorial provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) to spatial analysis queries. The tutorial categorizes the research in this area based on how LLMs are employed to serve such queries. This goes from employing LLMs as is, to fine-tuning LLMs, to completely retrain LLM architectures, to modifying the LLM internals to fit spatial queries. The tutorial concludes by a set of benchmarks and pointing out to research gaps and future research directions.

WiFi localization systems have become increasingly popular owing to the ubiquity of WiFi-enabled devices. However, traditional WiFi networks were not originally designed for localization purposes, and changes to the Access Point locations can significantly drop localization performance. Moreover, these systems are unable to generalize models trained in one building to other buildings due to dependencies on building characteristics and their WiFi configurations. To address this challenge, we propose GlobLoc: the first global indoor positioning system that can function in any environment in a plug-and-play manner without requiring recalibration. Towards this end, we introduce a novel concept called the “virtual environment” in which users are tracked rather than the physical counterpart. This enables the localization model trained on one environment to function in another, regardless of changes occurring in the network environment, as they share the same virtual environment. Our experiments demonstrate that GlobLoc outperforms existing state-of-the-art propagation- and fingerprinting-based systems by at least 65%. This superior accuracy is due to its robustness to changes in the WiFi environment and its adaptability to different building configurations.

Recently, deep learning-based localization systems have become one of the most promising techniques due to their accuracy in complex environments. However, these techniques require large amounts of data for training. Obtaining such data is usually a tedious and time-consuming process, which hinders their practical deployment. In this paper, we propose a data augmentation framework for deep learning-based localization systems. The basic idea is to use a conditional Generative Adversarial Network that is able to learn the complex structures in the original training data and then generate high-quality synthetic data that matches the original data distribution. Evaluation of the proposed data augmentation framework in a real testbed shows that our technique can increase the average localization accuracy by 22.2% compared to the case of not using data augmentation. This demonstrates the promise of the proposed framework for enhancing deep learning-based localization systems.