An agent-based model of modal choice with perception biases and habits
- URL: http://arxiv.org/abs/2406.02063v1
- Date: Tue, 4 Jun 2024 07:44:57 GMT
- Title: An agent-based model of modal choice with perception biases and habits
- Authors: Carole Adam, Benoit Gaudou,
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. The simulator can be played online. It allows to modify urban infrastructure and observe modal report.
Related papers
- 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) - ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing
Forecasting Models in Badminton [52.21869064818728]
Deep learning approaches for player tactic forecasting in badminton show promising performance partially attributed to effective reasoning about rally-player interactions.
We propose a turn-based feature attribution approach, ShuttleSHAP, for analyzing forecasting models in badminton based on variants of Shapley values.
arXiv Detail & Related papers (2023-12-18T05:37:51Z) - Decoding Susceptibility: Modeling Misbelief to Misinformation Through a
Computational Approach [63.67533153887132]
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) - Reinforcement Learning with Human Feedback for Realistic Traffic
Simulation [53.85002640149283]
Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge.
This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
arXiv Detail & Related papers (2023-09-01T19:29:53Z) - On Transferability of Driver Observation Models from Simulated to Real
Environments in Autonomous Cars [23.514129229090987]
This paper investigates the viability of transferring video-based driver observation models from simulation to real-world scenarios in autonomous vehicles.
We record a dataset featuring actual autonomous driving conditions and involving seven participants engaged in highly distracting secondary activities.
Our dataset was designed in accordance with an existing large-scale simulator dataset used as the training source.
arXiv Detail & Related papers (2023-07-31T10:18:49Z) - How to Estimate Model Transferability of Pre-Trained Speech Models? [84.11085139766108]
"Score-based assessment" framework for estimating transferability of pre-trained speech models.
We leverage upon two representation theories, Bayesian likelihood estimation and optimal transport, to generate rank scores for the PSM candidates.
Our framework efficiently computes transferability scores without actual fine-tuning of candidate models or layers.
arXiv Detail & Related papers (2023-06-01T04:52:26Z) - TrafficBots: Towards World Models for Autonomous Driving Simulation and
Motion Prediction [149.5716746789134]
We show data-driven traffic simulation can be formulated as a world model.
We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving.
Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
arXiv Detail & Related papers (2023-03-07T18:28:41Z) - Preference Enhanced Social Influence Modeling for Network-Aware Cascade
Prediction [59.221668173521884]
We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling.
Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
arXiv Detail & Related papers (2022-04-18T09:25:06Z) - Self-organising Urban Traffic control on micro-level using Reinforcement
Learning and Agent-based Modelling [0.0]
This work addresses traffic flow optimisation by self-organising micro-level control combining Reinforcement Learning and rule-based agents.
Results show that the deployment of micro-level vehicle navigation control just by learned individual decision making and re-routing based on local environmental sensors can increase the efficiency of mobility.
arXiv Detail & Related papers (2022-02-24T18:10:42Z)
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.