Statistical Modelling for Public Transport Infrastructure

STATISTICAL MODELLING
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Section 1: Introduction
a) Due to growth in city size and underlying population, the public transport infrastructure
requires maintenance and capital investments so as to provide mobility options to the
people that are efficient yet affordable. One of the ways in which the efficiency factor
coupled with assessability is improved is through alternation of timings and overhauling of
routes so as to ensure wider coverage to a larger number of people and ensuring that travel
time is minimised for a larger segment of population. These exercises are carried out after
relevant market research is done with regards to the patterns of usage of travellers and
there are specialised agencies that are involved in the same which provide key input in this
regards (Meyers, 2017). These then form the basis of introduced changes which may not
be useful to everyone but aims to maximise the efficiency and utility of the transport
network while providing higher coverage especially to remote locations. The given report
tends to analyse some datasets through statistical tools in order to understand behaviour
and preferences of travellers and offer advice for future surveys.
b) For the given dataset, it needs to be determined if the dataset is primary or secondary. In
order for the dataset to the primary, it is imperative that the underlying data must be
collected by the researcher directly from the subjects or respondents. Clearly, this is not
happening in the given case as the data has not been collected by the university and it has
merely sourced the data and provided us the same. As a result, the given dataset would be
labelled as secondary only and not primary (Eriksson and Kovalainen, 2015). There are
essentially six variables in the given dataset with a sample size of 1000 observations. A
brief description of the given variables is indicated as follows.
 Mode – It indicates the public transport means that a given trip uses and is
essentially a categorical variable. Considering that no automatic arrangement of the
responses is possible, hence the given variable is represented using nominal scale.
 Date – It indicates the date of travel for the given trip. Considering that the given
responses can be arranged in chronological order, hence the appropriate
measurement scale would be ordinal.
 Tap – Tap essentially represents two states namely tap on and tap off for the given
trip and is essentially a categorical variable. Considering that no automatic

arrangement of the responses is possible, hence the given variable is represented
using nominal scale.
 Time represents the time aspect related to the trip and would be considered a
quantitative variable. The measurement scale used for this would be interval scale.
 Count highlights the requisite frequency and is essentially a numerical or
quantitative variable. The concerned measurement scale is ratio as absolute zero is
defined.
 Location highlights the underlying station in the trip where tap on or tap off is
happening at the particular time. The underlying variable is categorical.
Considering that no automatic arrangement of the responses is possible, hence the
given variable is represented using nominal scale
The key cases may be derived considering the differences in the above variables which
have been defined above and would correspond to the behaviour and different preferences
of the travellers.
c) For collection of dataset 2, 30 respondents have been selected and the relevant information
collected from these. The focus of this data is only on two variables namely the gender of the
respondent along with public transport mode. The dataset 2 would be termed as primary data
considering the fact that it was not been taken from some other primary or secondary source
but has been collected myself using survey as mechanism (Hair et. al., 2015). Even though
this dataset is primary, if does not imply that this dataset would be more accurate that the
dataset 1. Two reasons are responsible for the same. One is the use of non-probability based
sampling technique. The other is the low sample size of 30 which is insufficient for an
accurate representation of the population. The underlying sampling is convenience sampling
which also does not aid in faithful representation of the population and hence the results
obtained from the analysis of this data may lack in reliability (Eriksson and Kovalainen,
2015). With regards to data type and the corresponding measurement scale, gender would be
categorical variable with the use of a nominal measurement scale since no automatic
arrangement. The same is applicable with regards to mode of transport (Hillier, 2016).
Section 2: Single variable Analysis – Dataset 1
a) The public transport usage summary of the Dataset 1 has been provided as follows.