相关论文: GATSim: Urban Mobility Simulation with Generative …

Assessing the quality of public transportation services requires the analysis of large quantities of data on the scheduled and actual trips and documents listing the quality constraints each service needs to meet. Interrogating such…

Simulation has the potential to massively scale evaluation of self-driving systems enabling rapid development as well as safe deployment. To close the gap between simulation and the real world, we need to simulate realistic multi-agent…

In literature, scientists describe human mobility in a range of granularities by several different models. Using frameworks like MATSIM, VehiLux, or Sumo, they often derive individual human movement indicators in their most detail. However,…

We present GATSBI, a generative model that can transform a sequence of raw observations into a structured latent representation that fully captures the spatio-temporal context of the agent's actions. In vision-based decision-making…

This report presents results from an M1 internship dedicated to agent-based modelling and simulation of daily mobility choices. This simulation is intended to be realistic enough to serve as a basis for a serious game about the mobility…

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…

Human mobility plays a crucial role in transportation, urban planning, and public health. Advances in deep learning and the availability of diverse mobility data have transformed mobility modeling. However, existing deep learning models…

The rapidly growing field of network analytics requires data sets for use in evaluation. Real world data often lack truth and simulated data lack narrative fidelity or statistical generality. This paper presents a novel, mixed-membership,…

Understanding human behavior in urban environments is a crucial field within city sciences. However, collecting accurate behavioral data, particularly in newly developed areas, poses significant challenges. Recent advances in generative…

We built a multiagent simulation of urban traffic to model both ordinary traffic and emergency or crisis mode traffic. This simulation first builds a modeled road network based on detailed geographical information. On this network, the…

The mobility of people is at the center of transportation planning and decision-making of the cities of the future. In order to accelerate the transition to zero-emissions and to maximize air quality benefits, smart cities are prioritizing…

This work leverages Large Language Models (LLMs) to simulate human mobility, addressing challenges like high costs and privacy concerns in traditional models. Our hierarchical framework integrates persona generation, activity selection, and…

This report outlines the concepts, mechanisms and inner dynamics of the BEAM (Behavior, Energy, Autonomy, and Mobility) modeling framework. BEAM is an open-source large-scale high-resolution transportation model that harnesses the…

Web-based participatory urban sensing has emerged as a vital approach for modern urban management by leveraging mobile individuals as distributed sensors. However, existing urban sensing systems struggle with limited generalization across…

Designing and evaluating personalized and proactive assistant agents remains challenging due to the time, cost, and ethical concerns associated with human-in-the-loop experimentation. Existing Human-Computer Interaction (HCI) methods often…

As intelligent systems and multi-agent coordination become increasingly central to real-world applications, there is a growing need for simulation tools that are both scalable and accessible. Existing high-fidelity simulators, while…

Estimating the effects of introducing a range of smart mobility solutions within an urban area is a crucial concern in urban planning. The lack of a Decision Support System (DSS) for the assessment of mobility initiatives, forces local…

In transportation system demand modeling and simulation, agent-based models and microsimulations are current state-of-the-art approaches. However, existing agent-based models still have some limitations on behavioral realism and resource…

This paper presents an agent-based model of mobility choice, influenced by human factors such as habits and perception biases. It is implemented in a Netlogo simulator, calibrated from results of an online survey about perceptions of…

Travel demand management measures/policies are important to sustain positive changes among individuals' travel behaviour. An integrated agent-based microsimulation platform provides a rich framework for examining such interventions to…