In cities, a crucial challenge for planners is to ensure that the transport infrastructure must be robust to cater to the ever increasing population while ensuring efficiency and also affordability that is associated with public transport system. For enabling the same, the relevant authority tends to make changes in route of timetable and stoppages so that the available infrastructure can be utilised more efficiently and serve greater number of people. In this regards, some of the people may be negatively impacted but the larger good is considered more important. As a result, the decision to change timetable and stoppages must be taken after due research from specialised agencies who understand the preferences and the issues faced by the travellers (Meyers, 2017).
In order to determine whether the given dataset is primary or not, it needs to be seen if the underlying data has been collected by the entity conducting the research. It is apparent that the given data in dataset 1 has neither been collected by myself nor has been collected by the university. This data was computed by an external agency and hence the given dataset 1 would be termed as secondary data (Eriksson and Kovalainen, 2015). The given dataset has an underlying sample size of 1000 observations and the given information is represented in the form of six variables. For the variables such as location, tap, mode the underlying data type is categorical and since automatic arrangements of the respective values is not possible; hence the applicable measurement scale is nominal. On the other hand, date is also a categorical variable but the underlying measurement type of ordinal since arrangement in an orderly manner without any additional information is possible. Count and time are both quantitative variables, however the relevant measurement scale for the former would be ratio while for the latter would be interval (Flick, 2015). The cases in the dataset would correspond to a tap on or tap off at a given location at a given time through a defined mode on a particular date. The frequency of each of these cases is represented using the count variable.
The dataset 2 is a primary data since it has been obtained from any source but rather has been collected through the use of survey (Hair et. al., 2015).. The focus of the survey was only on recording two variables namely the preferred mode of public transport coupled with the underlying gender of the respondent. Even though dataset 2 is primary data unlike dataset 1, but this would not automatically imply that the former is more accurate than the latter. For the reliability of data obtained from primary source, the sample needs to representative of the underlying population. This is clearly not the case because of the following two reasons (Eriksson and Kovalainen, 2015).
The sample size is only 30 which is very small compared the population and the key attributes driving the preferences.
In the given case, the two variables i.e. transport mode and gender are variables of categorical form with a nominal measurement scale (Hillier, 2016).
Section 2: Single variable Analysis – Dataset 1
The graphical illustration of the above information is as given below.
As per the given summary table and graph regarding the transport mode, it is apparent that the mode which is most frequently used is train as is clear from the sample data where it has the maximum frequency. However, bus mode is also quite close and trails by only a minimal insignificant difference. But the contribution of other means of transport besides bus and train is only 5% thereby indicating a high degree of reliance on bus and train in the public transport system. As a result, going forward it is desired that relevant measures must be undertaken to strengthen the bus and train infrastructure so that it can handle higher number of passengers. Alternatively, the other means of public transport should be explored so as to ease the pressure and underlying traffic on bus and train.
The level of significance for the test is defined as 0.05.
Further, the relevant test statistics would be Z and the underlying test would be a one tail test. The excel output pertaining to the hypothesis is illustrated as follows.
For hypothesis testing, the focus would be on the p value based methodology. The p value obtained is 0.93 and hence exceeds the significance level of 0.05. This clearly implies the insufficiency of available evidence for null hypothesis rejection (Flick, 2015). Therefore the alternative hypothesis cannot be accepted. This hints that train does not have a share of over 50% in the public transport in NSW.
Section 3: Analysis of Two Variables – Dataset 1
The graphical illustration of the above information is as given below.
The maximum count from all the given three stations is witnessed for Parramatta with the difference from other stations being significant.
(b) The first step in the hypothesis test is to define the relevant hypotheses which is carried out below.
The level of significance for the test is defined as 0.05.
Further, the relevant test statistics would be F and the underlying test would be ANOVA. The statkey output pertaining to the hypothesis is illustrated as follows.
For hypothesis testing, the focus would be on the p value based methodology. The p value obtained from the given F statistic exceeds the significance level of 0.05. This clearly implies the insufficiency of available evidence for null hypothesis rejection. Therefore the alternative hypothesis cannot be accepted (Eriksson and Kovalainen, 2015). Hence, it may be concluded that the trends in tap off and tap on do not show any significant difference.
(c) With regards to the analysis performed in part (a) and part(b), it may be concluded that the proposed train line should have a connection with Parramatta station. This would ensure that the given station can function as a hub which can be used for passengers to commute from one place to another effectively and thereby ensuring lower traffic related congestion at other stations.
The numerical summary of the primary data computed through survey is shown below.
The graphical illustration of the above information is as given below.
In accordance with the summary of the data derived,. It can be seen that with regards to light rail, there is no particular gender difference. However, the difference between the genders is visible in case of other modes of public transport. In particular, bus and train are two modes of public transport which are preferred by males and hence the males ridership seems to be higher in comparison of females. However, in context of females, ferry seems to be preferred with about 50% of the females preferring to travel using this particular mode of public transport. However, the conclusion drawn above are not conclusive considering the fact that the underlying sample which has been used is most likely biased since the sample size is very small and also the sampling technique is not suitable.
Section 5: Discussion & Conclusion
A key observation is that train is the most frequently used public transport mode in the given sample. Also, bus mode of transport is also quite popular with a market share quite close to that of train assuming that the population preferences would be mirrored by the sample preferences. The share of train and bus is quite large and hence only a very limited share is occupied by the other modes of transport. Despite the dominance of train and bus, no transport mode exceeds 50% share in terms of travellers. In relation to the train line that is to be constructed underground, the suitable choice of linking seems to be Parramatta railway station owing to high traffic. The dataset 2 highlights the differencing gender preferences for mode of transport, Females have a preference for ferry and males for bus and train. However, further research ought to be conducted for conclusive response as the given sample is not an accurate representation of population leading to low reliability.
It is quite possible that the trends and preferences of travellers may be influenced by some extraneous factor during the given period. Hence, in order to draw more reliable conclusions, data should be taken on more dates of different months so as to ensure that the preferences are clearer. This is required considering the high degree of capital investment that is required in enabling infrastructure for bus and train.
References
Eriksson, P. and Kovalainen, A. (2015) Quantitative methods in business research 3rd ed. London: Sage Publications.
Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project. 4th ed. New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., and Page, M. J. (2015) Essentials of business research methods. 2nd ed. New York: Routledge.
Hillier, F. (2016) Introduction to Operations Research 6th ed. New York: McGraw Hill Publications.
Mayers, L. (2017) Greater Sydney and NSW public transport undergo state’s ‘largest’ timetable overhaul ever, [online] Available at https://www.abc.net.au/news/2017-11-26/new-sydney-and-nsw-public-transport-timetable-launched/9194538 (Assessed September 19, 2018)
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