This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.

Urbanization and technological advancements are reshaping urban mobility, presenting both challenges and opportunities. This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic congestion dynamics and explores their potential to enhance transportation systems' efficiency. Specifically, we assess the role of AI innovations, such as autonomous vehicles and intelligent traffic management, in mitigating congestion under varying regulatory frameworks. Autonomous vehicles reduce congestion through optimized traffic flow, real-time route adjustments, and decreased human errors. The study employs Ordinary Differential Equations (ODEs) to model the dynamic relationship between AI adoption rates and traffic congestion, capturing systemic feedback loops. Quantitative outputs include threshold levels of AI adoption needed to achieve significant congestion reduction, while qualitative insights stem from scenario planning exploring regulatory and societal conditions. This dual-method approach offers actionable strategies for policymakers to create efficient, sustainable, and equitable urban transportation systems. While safety implications of AI are acknowledged, this study primarily focuses on congestion reduction dynamics.

In modern urban centers, effective transportation management poses a significant challenge, with traffic jams and inconsistent travel durations greatly affecting commuters and logistics operations. This study introduces a novel method for enhancing urban mobility by combining machine learning algorithms with live traffic information. We developed predictive models for journey time and congestion analysis using data from New York City's yellow taxi trips. The research employed a spatiotemporal analysis framework to identify traffic trends and implemented real-time route optimization using the GraphHopper API. This system determines the most efficient paths based on current conditions, adapting to changes in traffic flow. The methodology utilizes Spark MLlib for predictive modeling and Spark Streaming for processing data in real-time. By integrating historical data analysis with current traffic inputs, our system shows notable enhancements in both travel time forecasts and route optimization, demonstrating its potential for widespread application in major urban areas. This research contributes to ongoing efforts aimed at reducing urban congestion and improving transportation efficiency through advanced data-driven methods.

'Urban Mobility' is emerging as the backbone of the entire city ecosystem ensuring its growth and overall success. Today's call for a greener planet and active'Climate Change' combatting agenda inevitably encourages the need for smarter, greener, and safer urban mobility channels. The emerging smart cities today are increasingly integrating mobility solutions that are based on cleaner energy usage and shared resources with an elevated level of infrastructure integration among its inhabitants. None of this can be achieved without a substantial focus on the design, planning, and delivery of urban infrastructure that enables greater efficiency in urban mobility. According to World Bank –'Traditionally, urban mobility is about moving people from one location to another location within or between urban areas.

Timothy Reuter, Head of Aerospace and Drones at the World Economic Forum explains, "Large amounts of capital have been flowing the sector, potentially accelerating its deployment. In February and March of 2021 three flying car companies, Archer, Joby, and Lilium, all became publicly traded through Special Purpose Acquisition Companies (SPACs). Significant sums of money are needed to not just design and manufacture these airframes, but also to get them certified as safe by major civil aviation authorities such as the FAA and European Aviation Safety Agency (EASA)."

Identifying the patterns in urban mobility is important for a variety of tasks such as transportation planning, urban resource allocation, emergency planning etc. This is evident from the large body of research on the topic, which has exploded with the vast amount of geo-tagged user-generated content from online social media. However, most of the existing work focuses on a specific setting, taking a statistical approach to describe and model the observed patterns. On the contrary in this work we introduce EigenTransitions, a spectrum-based, generic framework for analyzing spatio-temporal mobility datasets. EigenTransitions capture the anatomy of the aggregate and/or individuals' mobility as a compact set of latent mobility patterns.

Autonomous driving, connectivity, car sharing, electric vehicles, and the rise of renewable energy will all have powerful mutually reinforcing effects. For example, the introduction of self-driving cars in the 2020s will increase the use of EVs in high-use services such as ride-hailing because lower operating costs will offset the higher initial costs of these vehicles. The movement of people and goods is central to our society and economic activities. According to a BNEF-McKinsey & Company study, the change in how people move around cities will put the automotive and energy industries, as well as governments, under pressure. Light-duty vehicle fuel consumption could drop by up to 75% in some cities by 2030, prompting governments to look for new ways to recoup lost fuel taxes.

A new Smart City AI-Framework with a key focus on Sustainability and Liveability, The technological factors needs to be weighted in respect to societal integration. Cities are increasingly turning towards specialized technologies to address issues related to society, ecology, morphology and many others. The emerging concept of Smart Cities highly encourages this prospect by promoting the incorporation of sensors and Big Data through the Internet of Things (IoT). This surge of data brings new possibilities in the design and management of cities just as much as economic prospects. While Big Data processing through Artificial Intelligence (AI) can greatly contribute to the urban fabric, sustainability and liveability dimensions.

There is a strong belief on the internet that AI will solve basically all of future society's problems, if we just give it enough time. Christian Katzenbach took a close look at this myth to determine whether there is truth to it. In time for this year's Internet Governance Forum (IGF), Matthias Kettemann (HIIG) and Stephan Dreyer (Leibniz Insitut für Medienforschung Hans-Bredow-Institut (HBI)) will be publishing a volume called "Busted! As an exclusive sneak peek, we are publishing an assortment of these myths here on our blog – some of those have been busted by HIIGs own researchers and associates. The entire volume will be accessible soon at internetmyths.eu.