The simulation of pedestrian dynamics is a consolidated area of application for agent-based models: successful case studies can be found in the literature and off-the-shelf simulators are commonly employed by end-users, decision makers and consultancy companies. These models, however, generally neglect or treat in a simplistic way aspects like (i) the impact of cultural heterogeneity among individuals and (ii) the effects of the presence of groups and particular relationships among pedestrians. This work is aimed, on one hand, at introducing some fundamental anthropological considerations on which most pedestrian models are based, and in particular Edward T. Hall's work on proxemics. On the other hand, the paper describes an agent-based model encapsulating in the pedestrian's behavioural model effects representing both proxemics and a simplified account of influences related to the presence of groups in the crowd. The model is tested in a simple scenario to evaluate the implications of some modeling choices and the presence of groups in the simulated scenario. Results are discussed and compared to experimental observations and to data available in the literature.

An agent-based proxemic model for pedestrian and group dynamics: motivations and first experiments

Agent-based modeling and simulation (ABMS) had an increasing attention during the last decade. However, the weak validation and verification of agent-based simulation models makes ABMS hard to trust. There is no comprehensive tool set for verification and validation of agent-based simulation models which demonstrates that inaccuracies exist and/or which reveals the existing errors in the model. Moreover, on the practical side, there are many ABMS frameworks in use. In this sense, we designed and developed a generic testing framework for agent-based simulation models to conduct validation and verification of models. This paper presents our testing framework in detail and demonstrates its effectiveness by showing its applicability on a realistic agent-based simulation case study.

Towards a generic testing framework for agent-based simulation models

Agent-based modelling and simulation (ABMS) had an increasing attention during the last decade. However, the weak validation and verification of agent-based simulation models makes ABMS hard to trust. There is no comprehensive tool set for verification and validation of agent-based simulation models, which demonstrates that inaccuracies exist and/or reveals the existing errors in the model. Moreover, on the practical side, many ABMS frameworks are in use. In this sense, we designed and developed a generic testing framework for agent-based simulation models to conduct validation and verification of models. This paper presents our testing framework in detail and demonstrates its effectiveness by showing its applicability on a realistic agent-based simulation case study.

/pdf/a-generic-testing-framework-for-agent-based-simulation-lwbjukmnld.pdf

A generic testing framework for agent-based simulation models

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

This paper presents our ongoing work on modeling agents with human-like decision making and behavior execution capabilities in crowd simulation. We aim to provide a generic framework that reflects the major cognitive and physical processes as observed from human behaviors in real-life situations. The design of the framework is based on some basic assumptions and related cognitive theories on human behaviors in various real-life situations. In this paper, the cognitive architecture of our framework is presented, which emphasizes the role of experiences in human's decision making. The paper also briefly describes the design of agent's decision making process and presents a case study to show some results in a crowd simulation scenario.

Toward a Generic Framework for Modeling Human Behaviors in Crowd Simulation.

Agent-based modeling as a methodology for understanding natural phenomena is becoming increasingly popular in many disciplines of scientific research. Validation is still a significant problem for agent-based modelers and while various validation methodologies have been proposed, none have been widely adopted. Data plays a key role in the validation of any simulation system, typically large amounts of observable real world data are necessary to compare with model outputs. However, the complex nature of the studied natural systems will often make data collection difficult. This is certainly true for crowd and egress simulation, where data is limited and difficult to collect. In this paper we propose a new technique for validation of agent-based models, particularly those which relate to human behavior. This methodology adopts ideas from the field of Human Computation as a means of collecting large amounts of contextual behavioral data. The key principle is to use games as a means of framing behavioral questions to try and capture people's natural and instinctive decisions. We outline some key design challenges for such games and present one example game in the form of Escape. Escape is an egress based game where people are tasked to escape from rooms inhabited by other people. We show some preliminary studies which highlight some interesting applications of the game in addressing validation of behavioral based crowd and egress simulation.

Validation of agent-based simulation through human computation: an example of crowd simulation

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hu Nan

Author Tools

Chat about Author

Papers

Validation of agent-based simulation through human computation: an example of crowd simulation