Framework for pedestrian walking behaviour recognition to minimize road accident
Pedestrian walking misbehaviour represents a severe problem to road safety. Therefore, pedestrian behaviour classification is a perfect solution in providing safety for both pedestrians and vehicles by exchanging movement information among entities via wireless communication. However, wireless...
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2021
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my-uthm-ep.41282022-02-03T02:31:11Z Framework for pedestrian walking behaviour recognition to minimize road accident 2021-03 Hashim Kareem, Zahraa T Technology (General) HE331-380 Traffic engineering. Roads and highways. Streets TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Pedestrian walking misbehaviour represents a severe problem to road safety. Therefore, pedestrian behaviour classification is a perfect solution in providing safety for both pedestrians and vehicles by exchanging movement information among entities via wireless communication. However, wireless communication has critical issues with network failure, and these issues significantly affect the communication system. Thus, the framework involved two modules for pedestrian walking behaviour classification in a vehicle-to-pedestrian (V2P) context is proposed. In the methodology, this study discloses five useful stages. Firstly, mobile phone users' irregular walking behaviour is investigated using a questionnaire to determine their options on mobile usage in the street. Secondly, four different testing scenarios are chosen to acquire pedestrian walking data using the gyroscope sensor, where the essential features were extracted and selected. Thirdly, the pedestrian's behaviour is recognized using grid optimizer in machine learning. Fourthly, four standard vectors for pedestrian walking behaviour are developed. Fifthly, the performance of the proposed classification methods is validated and evaluated against multiple scenarios and features. Two sets of real-time data are presented in this work. The first one is related to the questionnaire data, consisting of 262 respondent samples, while the second set has 263 samples of pedestrian walking signals. The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. In contrast, for chatting and running behaviour, the accuracy is 100% and 80%, respectively. This study's implication serves the safety system in the V2P context by programming the proposed framework as an application in smartphones for exchanging pedestrian information to the vehicles for avoiding accidents. 2021-03 Thesis http://eprints.uthm.edu.my/4128/ http://eprints.uthm.edu.my/4128/1/24p%20ZAHRAA%20HASHIM%20KAREEM.pdf text en public http://eprints.uthm.edu.my/4128/2/ZAHRAA%20HASHIM%20KAREEM%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/4128/3/ZAHRAA%20HASHIM%20KAREEM%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik |
| institution |
Universiti Tun Hussein Onn Malaysia |
| collection |
UTHM Institutional Repository |
| language |
English English English |
| topic |
T Technology (General) T Technology (General) T Technology (General) |
| spellingShingle |
T Technology (General) T Technology (General) T Technology (General) Hashim Kareem, Zahraa Framework for pedestrian walking behaviour recognition to minimize road accident |
| description |
Pedestrian walking misbehaviour represents a severe problem to road safety.
Therefore, pedestrian behaviour classification is a perfect solution in providing safety
for both pedestrians and vehicles by exchanging movement information among entities
via wireless communication. However, wireless communication has critical issues
with network failure, and these issues significantly affect the communication system.
Thus, the framework involved two modules for pedestrian walking behaviour
classification in a vehicle-to-pedestrian (V2P) context is proposed. In the
methodology, this study discloses five useful stages. Firstly, mobile phone users'
irregular walking behaviour is investigated using a questionnaire to determine their
options on mobile usage in the street. Secondly, four different testing scenarios are
chosen to acquire pedestrian walking data using the gyroscope sensor, where the
essential features were extracted and selected. Thirdly, the pedestrian's behaviour is
recognized using grid optimizer in machine learning. Fourthly, four standard vectors
for pedestrian walking behaviour are developed. Fifthly, the performance of the
proposed classification methods is validated and evaluated against multiple scenarios
and features. Two sets of real-time data are presented in this work. The first one is
related to the questionnaire data, consisting of 262 respondent samples, while the
second set has 263 samples of pedestrian walking signals. The results indicate the
following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile
phones for calling and chatting, respectively. (2) 263 samples of participants are
obtained and analysed, and 90 features are extracted from each sample. (3) 100%
classification accuracy are obtained for each class (normal walking, calling, chatting,
and running) using the grid optimiser method in machine learning. (4) The precision
of classification using Euclidean algorithm for normal walking and calling is 70%. In
contrast, for chatting and running behaviour, the accuracy is 100% and 80%,
respectively. This study's implication serves the safety system in the V2P context by
programming the proposed framework as an application in smartphones for
exchanging pedestrian information to the vehicles for avoiding accidents. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Hashim Kareem, Zahraa |
| author_facet |
Hashim Kareem, Zahraa |
| author_sort |
Hashim Kareem, Zahraa |
| title |
Framework for pedestrian walking behaviour recognition to minimize road accident |
| title_short |
Framework for pedestrian walking behaviour recognition to minimize road accident |
| title_full |
Framework for pedestrian walking behaviour recognition to minimize road accident |
| title_fullStr |
Framework for pedestrian walking behaviour recognition to minimize road accident |
| title_full_unstemmed |
Framework for pedestrian walking behaviour recognition to minimize road accident |
| title_sort |
framework for pedestrian walking behaviour recognition to minimize road accident |
| granting_institution |
Universiti Tun Hussein Onn Malaysia |
| granting_department |
Fakulti Kejuruteraan Elektrik dan Elektronik |
| publishDate |
2021 |
| url |
http://eprints.uthm.edu.my/4128/1/24p%20ZAHRAA%20HASHIM%20KAREEM.pdf http://eprints.uthm.edu.my/4128/2/ZAHRAA%20HASHIM%20KAREEM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/4128/3/ZAHRAA%20HASHIM%20KAREEM%20WATERMARK.pdf |
| _version_ |
1747831051182407680 |