## 4 Results and Discussion
### Model 1--Intentions to Use Public Transport during COVID-19 Pandemic  
An ordinal regression model was developed to model the intentions to use public transport during the COVID-19 pandemic. Socio-economic and -demographic variables, and the underlying factors were entered as the explanatory variables. Correlations were \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Attitudes** & **Awareness Responsibility** & **Difficulty** \\ \hline I would consider public transport for travelling if all passengers are aware to maintain a social distance. & 0.885 & \\ I will only use public transport when vehicles are properly disinfected. & 0.839 & \\ I would consider public transport for travelling if all related facilities are properly disinfected. & 0.790 & \\ I will only use public transport when COVID-19 related safety guidelines are fully followed. & 0.776 & \\ I would consider public transport for travelling if all passengers wear a facemask. & 0.734 & \\ I would consider public transport for travelling if the vehicles are uncongested. & 0.731 & \\ I do/would avoid using public transport as I have a fear of being infected. & 0.540 & \\ I believe that wearing a face mask protects us from COVID-19 while travelling on public transport. & 0.847 & \\ I believe that maintaining a social distance helps in avoiding COVID-19 infection while travelling on public transport. & 0.825 & \\ I believe that the use of hand sanitizers or disinfectants can prevent us from COVID-19 infection & 0.641 & \\ while travelling on public transport. & & \\ I believe that avoid travelling with COVID-19 symptoms can reduce the spread of disease. & 0.619 & \\ I feel a moral obligation to wear a facemask while & 0.597 & \\ travelling on public transport. & 0.556 & \\ I feel a moral obligation to avoid travelling on public transport. & & \\ I respond if I have COVID-19 symptoms. & & \\ I feel an equal responsibility to use sanitizers for the safety of all passengers. & & 0.879 & \\ I feel an equal responsibility to keep a social distance & & 0.728 & \\ from passengers while travelling on public transport. & & 0.862 \\ It is difficult to use hand sanitizers all the time while & & 0.768 \\ travelling on public transport. & & \\ \hline \% of variance explained & 260.397 & 190.189 & 90.928 & 80.924 \\ Cronbach’s alpha & 0.888 & 0.871 & 0.859 & 0.818 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Exploratory factor analysis of the questionnaire items (Varimax rotation). checked before entering the explanatory variables. Income was removed from the explanatory variables since it had a strong correlation with age. Item 1 of Section 2 of the questionnaire (How often would you use public transport considering the COVID-19 pandemic) was entered as the dependent variable. Since the distribution of responses was skewed towards \"never\" as compared to \"always\", the five levels (never to always) were collapsed into three levels as \"never--rarely\", \"sometimes\", and \"often--always\". The chi-square statistic was significant indicating that the model is a significant improvement over the intercept-only model (Table 3). However, the goodness-of-fit measures i.e., Pearson statistic and Deviance statistic were significant which could be attributed to the fact that there were zero frequencies in a considerable number of cells especially because of the continuous predictors and these statistics may not provide stable estimates of goodness-of-fit under such conditions [52]. The good-of-fit measures i.e., Cox and Snell, Nagelkerke, and McFadden pseudo R square were 0.203, 0.246 and 0.130, respectively. The non-significant chi-square statistic (\(\chi^{2}=4.946\), df = 19, \(p=0.999\)) indicated that the test of parallel lines was satisfied. Parameter estimates for the ordinal regression model are shown in Table 4. The sign of the coefficients indicates direct or indirect relationships between continuous explanatory variables and the ordinal outcome variable.