The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada
The heterogenous nature of crash data has always been a challenging barrier in interpreting data processing results and in unrevealing the hidden relationships between crash contributing factors. Traffic researchers aim to eliminate the interruption of insignificant factors , identify influential fa...
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| Outros Autores: | |
| Formato: | Final Year Project (FYP) |
| Idioma: | English |
| Publicado em: |
Nanyang Technological University
2020
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| Acesso em linha: | https://hdl.handle.net/10356/141902 |
| _version_ | 1826123820238372864 |
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| author | Dong, Qiutong |
| author2 | Zhu Feng |
| author_facet | Zhu Feng Dong, Qiutong |
| author_sort | Dong, Qiutong |
| collection | NTU |
| description | The heterogenous nature of crash data has always been a challenging barrier in interpreting data processing results and in unrevealing the hidden relationships between crash contributing factors. Traffic researchers aim to eliminate the interruption of insignificant factors , identify influential factors and sort out complicated logic relationships among those factors. Understanding the influential factors leading to traffic accidents is essential for designing effective countermeasures. A method commonly employed to address systematic heterogeneity in crash data is to focus on each subgroup of data. However, this approach neglects the independent relationships among factors, and does not ensure homogeneity within each subgroup. In this project, Latent Class Cluster analysis is applied to segment a whole cyclist crash dataset into homogenous subgroups with meaningful influential factors. The manuscript employs data from recorded crashes involving cyclists from 2008 to 2018 by the police in Toronto, Canada. The analyses demonstrate that dividing cyclists’ crash data into seven clusters most efficiently helps in reducing the systematic heterogeneity of the data and aids in understanding the relationships between socio-demographic characteristics, environmental characteristics, maneuver-related characteristics etc. and further identifying determining reasons for the crashes involving cyclists . Based on the clustering results, some significant factors are studied in detail along with socio-economic backgrounds and some countermeasures are proposed. Overall, this study suggests that a latent class clustering approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. |
| first_indexed | 2024-10-01T06:10:35Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/141902 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:10:35Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1419022020-06-11T08:24:54Z The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada Dong, Qiutong Zhu Feng School of Civil and Environmental Engineering zhufeng@ntu.edu.sg Engineering::Civil engineering The heterogenous nature of crash data has always been a challenging barrier in interpreting data processing results and in unrevealing the hidden relationships between crash contributing factors. Traffic researchers aim to eliminate the interruption of insignificant factors , identify influential factors and sort out complicated logic relationships among those factors. Understanding the influential factors leading to traffic accidents is essential for designing effective countermeasures. A method commonly employed to address systematic heterogeneity in crash data is to focus on each subgroup of data. However, this approach neglects the independent relationships among factors, and does not ensure homogeneity within each subgroup. In this project, Latent Class Cluster analysis is applied to segment a whole cyclist crash dataset into homogenous subgroups with meaningful influential factors. The manuscript employs data from recorded crashes involving cyclists from 2008 to 2018 by the police in Toronto, Canada. The analyses demonstrate that dividing cyclists’ crash data into seven clusters most efficiently helps in reducing the systematic heterogeneity of the data and aids in understanding the relationships between socio-demographic characteristics, environmental characteristics, maneuver-related characteristics etc. and further identifying determining reasons for the crashes involving cyclists . Based on the clustering results, some significant factors are studied in detail along with socio-economic backgrounds and some countermeasures are proposed. Overall, this study suggests that a latent class clustering approach is suitable for reducing heterogeneity and revealing important hidden relationships in traffic safety analyses. Bachelor of Engineering (Civil) 2020-06-11T08:24:53Z 2020-06-11T08:24:53Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141902 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Civil engineering Dong, Qiutong The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada |
| title | The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada |
| title_full | The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada |
| title_fullStr | The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada |
| title_full_unstemmed | The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada |
| title_short | The application of latent class analysis for investigating influential factors for crashes involved cyclists in Toronto, Canada |
| title_sort | application of latent class analysis for investigating influential factors for crashes involved cyclists in toronto canada |
| topic | Engineering::Civil engineering |
| url | https://hdl.handle.net/10356/141902 |
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