Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD)
Nowadays, PMDs (personal mobility device) are widely used in all over the world, especially in east Asia, as it is very eco-friendly and convenient for people to go to work or travel around the city. Meanwhile PMD riders usually go fast and this phenomenon will bring danger to both pedestrians and P...
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sg-ntu-dr.10356-787882023-07-04T16:12:32Z Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) Yuan, Liqiang Justin Dauwels School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Nowadays, PMDs (personal mobility device) are widely used in all over the world, especially in east Asia, as it is very eco-friendly and convenient for people to go to work or travel around the city. Meanwhile PMD riders usually go fast and this phenomenon will bring danger to both pedestrians and PMD riders. This paper aims to find out the proper speed for different PMDs when they encounter a pedestrian on the sidewalk, so that both pedestrian and PMD rider can be safe. Based on the Social Force Model (SFM) and Cross-Entropy (CE) method, by using PTV Vissim, simulation on PMDs and pedestrians can be implemented to first find out the SFM parameter value for different customized PMDs. After getting the parameters of SFM for different PMDs, simulation can be done to find out the correlation between PMD speed and lateral distance for PMD rider when encountering a pedestrian. According to the emotion test in the field experiment, the corresponding comfort of the pedestrian will be used to determine whether the speed of PMD is safe or not. Master of Science (Computer Control and Automation) 2019-06-27T05:31:03Z 2019-06-27T05:31:03Z 2019 Thesis http://hdl.handle.net/10356/78788 en 76 p. application/pdf |
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Engineering::Electrical and electronic engineering |
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Engineering::Electrical and electronic engineering Yuan, Liqiang Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) |
description |
Nowadays, PMDs (personal mobility device) are widely used in all over the world, especially in east Asia, as it is very eco-friendly and convenient for people to go to work or travel around the city. Meanwhile PMD riders usually go fast and this phenomenon will bring danger to both pedestrians and PMD riders. This paper aims to find out the proper speed for different PMDs when they encounter a pedestrian on the sidewalk, so that both pedestrian and PMD rider can be safe.
Based on the Social Force Model (SFM) and Cross-Entropy (CE) method, by using PTV Vissim, simulation on PMDs and pedestrians can be implemented to first find out the SFM parameter value for different customized PMDs. After getting the parameters of SFM for different PMDs, simulation can be done to find out the correlation between PMD speed and lateral distance for PMD rider when encountering a pedestrian. According to the emotion test in the field experiment, the corresponding comfort of the pedestrian will be used to determine whether the speed of PMD is safe or not. |
author2 |
Justin Dauwels |
author_facet |
Justin Dauwels Yuan, Liqiang |
format |
Theses and Dissertations |
author |
Yuan, Liqiang |
author_sort |
Yuan, Liqiang |
title |
Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) |
title_short |
Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) |
title_full |
Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) |
title_fullStr |
Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) |
title_full_unstemmed |
Carlibration parameters of social force model (SFM) for customized personal mobility devices (PMD) |
title_sort |
carlibration parameters of social force model (sfm) for customized personal mobility devices (pmd) |
publishDate |
2019 |
url |
http://hdl.handle.net/10356/78788 |
_version_ |
1772828921848397824 |