Urban mobility systems face escalating challenges associated with sustainability, equity, and resilience, further compounded by environmental pressures. Traditional agent-based models (ABMs) often fail to capture cognitively rich, adaptive behaviors, limiting their ability to simulate realistic user responses to disruptions. In this work, we propose a cognitive agent architecture based on Large Language Models (LLMs), featuring multi-horizon memory-driven planning, reflection, and adaptation. Integrated into the SimFleet agent-based simulator with realistic sociodemographic profiles, the agents dynamically generate, adjust, and reflect upon travel plans across a 20-day simulation involving over 320 individuals. Experimental results reveal emergent adaptation patterns under both stable and disrupted transport conditions, and an ablation study under severe service disruption quantifies the contributions of short-term and long-term memory modules to memory-driven reasoning, demonstrating the potential of LLM-driven agents to enhance the realism, flexibility, and interpretability of urban mobility simulations.

Rural mobility research has been left aside in favor of urban transportation. Rural areas? low demand, the distance among settlements, and an older population on average make conventional public transportation inefficient and costly. This paper assesses the contribution that on-demand mobility has the potential to make to rural areas. First, demand-responsive transportation is described, and the related literature is reviewed to gather existing system configurations. Next, we describe and implement a proposal and test it on a simulation basis. The results show a clear potential of the demand-responsive mobility paradigm to serve rural demand at an acceptable quality of service. Finally, the results are discussed, and the issues of adoption rate and input data scarcity are addressed.

The public's awareness of pollution in cities is growing. The decrease of carbon dioxide emissions from the use of fossil-fuel-powered cars stands out among the different viable alternatives. To this purpose, more sustainable options, such as carsharing fleets, could be used to replace private automobiles and other services such as taxis. This type of vehicle, which is usually electric, is becoming more common in cities, providing a green mobility option. In this research, we use multi-agent simulations to examine the efficiency of the current taxi fleet in Valencia. After that, we evaluate various carsharing fleet arrangements. Our findings demonstrate the possibility for a mix of the two types of fleets to meet present demand while also improving the city's sustainability.

With the world population highly increasing, efficient methods of transportation are more necessary than ever. On the other hand, the sharing economy must be explored and applied where possible, aiming to palliate the effects of human development on the environment. In this paper we explore demand-responsive shared transportation as a system with the potential to serve its users’ displacement needs while being less polluting. In contrast with previous works, we focus on a distributed proposal that allows each vehicle to retain its private information. Our work describes a partially dynamic system in which the vehicles are self-interested: they decide which users to serve according to the benefit it reports them. With our modelling, the system can be adapted to mobility platforms of autonomous drivers and even simulate the competition among different companies.