A survey about perceptions of mobility to inform an agent-based simulator of subjective modal choice

• URL: http://arxiv.org/abs/2502.12058v1
• Date: Mon, 17 Feb 2025 17:25:18 GMT
• Title: A survey about perceptions of mobility to inform an agent-based simulator of subjective modal choice
• Authors: Carole Adam, Benoit Gaudou,
• Abstract summary: This is an extended and translated version of a demo paper published in French at JFSMA-JFMS 2024 "Un simulateur multi-agent de choix modal subjectif"
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In order to adapt to the issues of climate change and public health, urban policies are trying to encourage soft mobility, but the share of the car remains significant. Beyond known constraints, we study here the impact of perception biases on individual choices. We designed a multi-criteria decision model, integrating the influence of habits and biases. We then conducted an online survey, which received 650 responses. We used these to calculate realistic mobility perception values, in order to initialise the environment and the population of a modal choice simulator, implemented in Netlogo. This allows us to visualize the adaptation of the modal distribution in reaction to the evolution of urban planning, depending on whether or not we activate biases and habits in individual reasoning. This is an extended and translated version of a demo paper published in French at JFSMA-JFMS 2024 "Un simulateur multi-agent de choix modal subjectif"

Related papers

• MF-LLM: Simulating Collective Decision Dynamics via a Mean-Field Large Language Model Framework [53.82097200295448]
  Mean-Field LLM (MF-LLM) framework explicitly models the feedback loop between micro-level decisions and macro-level population. MF-LLM alternates between two models: a policy model that generates individual actions based on personal states and group-level information, and a mean field model that updates the population distribution. We evaluate MF-LLM on a real-world social dataset, where it reduces KL divergence to human population distributions by 47 percent over non-mean-field baselines.
  arXiv  Detail & Related papers  (2025-04-30T12:41:51Z)
• MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation [22.340422693575547]
  We present a virtual city that features multiple functional buildings and transportation modes. We then conduct extensive surveys to model behavioral choices and mobility preferences among population groups. We introduce a simulation framework that captures the complexity of urban mobility while remaining scalable, enabling the simulation of over 4,000 agents.
  arXiv  Detail & Related papers  (2025-04-18T07:01:05Z)
• Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations [49.908708778200115]
  We are the first to specialize large language models (LLMs) for simulating survey response distributions. As a testbed, we use country-level results from two global cultural surveys. We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions.
  arXiv  Detail & Related papers  (2025-02-10T21:59:27Z)
• AI-Driven Day-to-Day Route Choice [15.934133434324755]
  "LLMTraveler" is an agent that learns from past experiences and makes decisions by balancing retrieved data and personality traits.<n>This paper systematically evaluates the LLMTraveler's ability to replicate human-like decision-making through two stages of day-to-day congestion games.<n>This capability offers valuable insights for transportation policymaking, such as simulating traveler responses to new policies or changes in the network.
  arXiv  Detail & Related papers  (2024-12-04T14:13:38Z)
• ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents [70.17229548653852]
  We introduce ElectionSim, an innovative election simulation framework based on large language models. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
  arXiv  Detail & Related papers  (2024-10-28T05:25:50Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• An agent-based model of modal choice with perception biases and habits [0.0]
  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 mobility.
  arXiv  Detail & Related papers  (2024-06-04T07:44:57Z)
• A survey to measure cognitive biases influencing mobility choices [0.0]
  This paper describes a survey about the perceptions of 4 mobility modes (car, bus, bicycle, walking) and the preferences of users for 6 modal choice factors. This survey has gathered 650 answers in 2023, that are published as open data. Work is ongoing to design a simulation-based serious game where the player takes the role of an urban manager faced with planning choices to make their city more sustainable.
  arXiv  Detail & Related papers  (2024-05-06T08:12:13Z)
• Identifying and modelling cognitive biases in mobility choices [0.0]
  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 transition.
  arXiv  Detail & Related papers  (2024-02-15T12:58:27Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Dual policy as self-model for planning [71.73710074424511]
  We refer to the model used to simulate one's decisions as the agent's self-model. Inspired by current reinforcement learning approaches and neuroscience, we explore the benefits and limitations of using a distilled policy network as the self-model.
  arXiv  Detail & Related papers  (2023-06-07T13:58:45Z)
• Simulating the impact of cognitive biases on the mobility transition [0.0]
  This paper explores various cognitive biases that can explain lack of adaptation to climate change. Our approach is to design simple interactive simulators that users can play with in order to understand biases.
  arXiv  Detail & Related papers  (2023-02-07T16:06:26Z)
• Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning [55.41644538483948]
  Current approaches either do not generalize well beyond the training data or are not capable to consider a variable number of traffic participants. We propose an invariant environment representation from the perspective of the ego vehicle. We show that the agents are capable to generalize successfully to unseen scenarios, due to the abstraction.
  arXiv  Detail & Related papers  (2021-02-12T20:37:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.