## 5 Results and Discussion
### Results from Consideration Propensity Model
#### 5.1.1 Segmentation of Consideration Based on Personal Mobility Resources  
Based on the exploratory analysis findings mentioned earlier, the survey respondents were grouped into three segments based on personal mobility resource availability. These include low personal mobility (LPM) resource households (no vehicle in household or driving knowledge), medium personal mobility segment (households owning only one two-wheeler), and high personal mobility resource segment (having more than one two-wheeler or at least one car in household). A segment-wise analysis of consideration was carried out to investigate which factor influences the decision of consideration more and for which segment. Both unsegmented and segmented models were estimated and compared based on the goodness-of-fit measure (likelihood ratio index-LRI) and chi-squared test.  
Compared to the unsegmented consideration model, which has a log-likelihood of \(-423.78\), the segmented model has an improved log-likelihood of \(-414.73\) for five degrees of freedom. Thus, the Likelihood Ratio Index for the consideration model increases by \(4\%\) from \(0.24\) to \(0.25\) in the segmented model. The chi-squared test confirms that the segmented model is statistically superior to the unsegmented one at the \(1\%\) significance level (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Unsegmented Model**} & \multicolumn{2}{c}{**Segment 1:**} & \multicolumn{2}{c}{**Segment 2:**} & \multicolumn{2}{c}{**Segment 3:**} \\  & & **Low Personal** & **Medium Personal** & **High Personal** & **High Personal** \\  & & & **Mobility (LPM)** & **Mobility (MPM)** & **Mobility (HPM)** \\ \cline{2-7}  & **Coef** & **t-Stat \({}^{*}\)** & **Coef** & **t-Stat** & **Coef** & **t-Stat** & **Coef** & **t-Stat** \\ \hline Intercept & \(-3.29\) & \(-6.73\) & & \(-3.67\) & \(-6.35\) & & \\ \hline
**Vehicular fleet composition and captivity status** & & & & & & & \\ \hline Household does not own a car (\(1/0\)) & \(0.63\) & \(2.36\) & \(0.65\) & \(2.42\) & \(0.65\) & \(2.42\) & \(0.65\) & \(2.42\) \\ \hline The household does not own both & \(0.23\) & \(0.82\) & \(-0.02\) & \(-0.02\) & & & \\ two-wheeler and car (\(1/0\)) & & & & & & \\ \hline Own only one two-wheeler and is either & & & & & & \\ semi-captive or does not have driving & & & & & & \\ knowledge (LPM = \(1/0\)) & & & & & & \\ \hline Own only one two-wheeler and vehicle is & & & & & & \\ available all the time (MPM = \(1/0\)) (Base) & & & & & & \\ \hline Own a car or multiple two-wheelers & & & & & & \\ (HPM = \(1/0\)) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results from the consideration model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models/Functional Forms** & **Final Log-Likelihood** & \(\rho\)**2** \\ \hline Proposed logit model & \(-414.7313\) & \(0.2549\) \\ Probit & \(-414.8527\) & \(0.2547\) \\ Complementary log-log & \(-415.8329\) & \(0.2529\) \\ Cauchy & \(-416.5254\) & \(0.2517\) \\ Log–log & \(-416.1872\) & \(0.2523\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of alternate model structures for train consideration model.