Impacts of road network connectivity on quality of in abuja city
Development of well-connected road network to guarantee quality living in cities is a major concern in the current era. Efforts have been made to establish the underlying relation between road network and societal well-being. However, the causal relationship remains poorly understood due to inabilit...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/81724/1/NuhuHonneyTiniPFAB2018.pdf http://eprints.utm.my/id/eprint/81724/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126527 |
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| Summary: | Development of well-connected road network to guarantee quality living in cities is a major concern in the current era. Efforts have been made to establish the underlying relation between road network and societal well-being. However, the causal relationship remains poorly understood due to inability to consider personal quality of life in the appraisal technique. This research introduces a novel model with multidimensional analytical approach for empirical exploration of road connectivity impact on quality of life (QOL) in Abuja City, Nigeria. Forty planning districts were used as spatial units for road network analysis. QOL survey data were generated from 367 respondents in the 15 sampled districts. Graph theory metrics comprising alpha, beta, cyclomatic number, eta, gamma and aggregate transportation score (ATS) indices were applied to determine the connectivity of road networks. Exploratory factor analysis (EFA) was used to examine the components of road connectivity and quality of life indicators for the model development. Structural equation modelling (SEM) was applied for confirmatory factor analysis (CFA) to determine the model fitness between the components of road connectivity and the latent indicators of quality of life. Weighted average score (WAS) and analysis of variance (ANOVA) were used to compare the quality of life among the districts with different levels of road connectivity. Finding revealed that most districts (60%) have low road connectivity (6.66 – 46.23 ATS). About 22.5% of the districts have moderate connectivity (51.04 – 91.00 ATS), while 17.5% districts have high road connectivity (100.98 – 146.13 ATS). Factor analysis affirmed that four connectivity components, six latent factors and 26 observable factors were fit for model development. The structural equation modelling showed high factor loading (R2 = 0.66), implying that road connectivity components explained 66% of QOL. Path coefficient was 0.81, indicating that every one unit increase in connectivity, contributes 0.81 unit increase in QOL. Analysis of variance showed a statistical significant difference in quality of life at < .05 level between low and high connected districts. However, quality of life slightly varied between the moderate and low connected districts as well as between the moderate and high connected districts. Overall, the results of this research have contributed by revealing how road connectivity empirically affects QOL. Hence, the study suggests a multidimensional model that can be employed in future analyses. The model would be useful to researchers, planners and engineers for examining the impact of transportation network on societal quality of life. |
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