Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)

This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of...

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Main Author: Hyesop Shin
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:MethodsX
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2215016122000577
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author Hyesop Shin
author_facet Hyesop Shin
author_sort Hyesop Shin
collection DOAJ
description This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model’s main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city’s air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster. • An ODD protocol is used to document the agent-based model TRAPSim. • Sensitivity experiments and calibration are explained. • The step-by-step codes and annotations are attached in the protocol and HPC sections.
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spelling doaj.art-d154e853d4a24457a7e9ceb167d33b8c2022-12-22T03:00:32ZengElsevierMethodsX2215-01612022-01-019101673Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)Hyesop Shin0School of Geographical and Earth Sciences, University of Glasgow, G12 8QQ, UKThis paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model’s main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city’s air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster. • An ODD protocol is used to document the agent-based model TRAPSim. • Sensitivity experiments and calibration are explained. • The step-by-step codes and annotations are attached in the protocol and HPC sections.http://www.sciencedirect.com/science/article/pii/S2215016122000577Agent-based modellingTraffic simulationAir pollutionExposureNetLogo
spellingShingle Hyesop Shin
Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
MethodsX
Agent-based modelling
Traffic simulation
Air pollution
Exposure
NetLogo
title Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_full Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_fullStr Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_full_unstemmed Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_short Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM)
title_sort quantifying the health effects of exposure to non exhaust road emissions using agent based modelling abm
topic Agent-based modelling
Traffic simulation
Air pollution
Exposure
NetLogo
url http://www.sciencedirect.com/science/article/pii/S2215016122000577
work_keys_str_mv AT hyesopshin quantifyingthehealtheffectsofexposuretononexhaustroademissionsusingagentbasedmodellingabm