Comfort zone prediction around commuter for personal mobility device : pedestrian

For an eco-friendly mode of transportation Personal Mobility Devices (PMDs) are being used for minimal distance travel inside the city environment. But before allowing Personal Mobility Devices (PMDs) in shared paths, there is a need for analysis in terms of safety factors and the impact of PMD with...

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Main Author: Murugesan, Jeyakaran
Other Authors: Justin Dauwels
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78626
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-786262023-07-04T16:22:47Z Comfort zone prediction around commuter for personal mobility device : pedestrian Murugesan, Jeyakaran Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering For an eco-friendly mode of transportation Personal Mobility Devices (PMDs) are being used for minimal distance travel inside the city environment. But before allowing Personal Mobility Devices (PMDs) in shared paths, there is a need for analysis in terms of safety factors and the impact of PMD with the users in shared paths. Mainly this study focuses on predicting the comfort zones for four different PMDs (Go Cycle, Inokim, Schaeffler, Zero). Inferences for comfort zone are being done in two ways, data visualization and analysis using machine learning classifier model. This data visualization is purely based on the data extracted from the videos and the responses from participants. Machine Learning analyzing is done using two classifier algorithm (Random forest and SVM), developed based on diverging distance and passing distance for predicting the comfort state ( comfortable or uncomfortable). The prediction model helps the rider to know whether they are causing discomfort to the pedestrians they meet. Weights of the features affecting the comfort zone are also analyzed by training the models with a different combination of features (age of the participant, type of PMD, the gender of the participant, diverging distance and passing distance). Master of Science (Computer Control and Automation) 2019-06-24T13:10:01Z 2019-06-24T13:10:01Z 2019 Thesis http://hdl.handle.net/10356/78626 en 86 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Murugesan, Jeyakaran
Comfort zone prediction around commuter for personal mobility device : pedestrian
description For an eco-friendly mode of transportation Personal Mobility Devices (PMDs) are being used for minimal distance travel inside the city environment. But before allowing Personal Mobility Devices (PMDs) in shared paths, there is a need for analysis in terms of safety factors and the impact of PMD with the users in shared paths. Mainly this study focuses on predicting the comfort zones for four different PMDs (Go Cycle, Inokim, Schaeffler, Zero). Inferences for comfort zone are being done in two ways, data visualization and analysis using machine learning classifier model. This data visualization is purely based on the data extracted from the videos and the responses from participants. Machine Learning analyzing is done using two classifier algorithm (Random forest and SVM), developed based on diverging distance and passing distance for predicting the comfort state ( comfortable or uncomfortable). The prediction model helps the rider to know whether they are causing discomfort to the pedestrians they meet. Weights of the features affecting the comfort zone are also analyzed by training the models with a different combination of features (age of the participant, type of PMD, the gender of the participant, diverging distance and passing distance).
author2 Justin Dauwels
author_facet Justin Dauwels
Murugesan, Jeyakaran
format Theses and Dissertations
author Murugesan, Jeyakaran
author_sort Murugesan, Jeyakaran
title Comfort zone prediction around commuter for personal mobility device : pedestrian
title_short Comfort zone prediction around commuter for personal mobility device : pedestrian
title_full Comfort zone prediction around commuter for personal mobility device : pedestrian
title_fullStr Comfort zone prediction around commuter for personal mobility device : pedestrian
title_full_unstemmed Comfort zone prediction around commuter for personal mobility device : pedestrian
title_sort comfort zone prediction around commuter for personal mobility device : pedestrian
publishDate 2019
url http://hdl.handle.net/10356/78626
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