Transportation In New South Wales: An Analysis

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The transportation is the New South Wales is the leading agency of the New South Wales transportation cluster. The role is transportation is to establish a more efficient, safer and integrated transportation system (Amiril et al. 2014). The transportation system majorly keeps people moving and links the communities of the centers, suburbs, regions and cities. The well-known types of transportation system of New South Wales are ‘rail’, ‘bus’, ‘light rail’ and ‘ferry’. Public and people who are equally associated to the transportation system, are equally responsible for planning, policy, regulation, strategy, allocation of funding and non-service delivery functions.

The transportation system focuses to enhance the ‘customer experience’ and links the ‘public and private operators’ for delivering customer-oriented transport services on their behalf (Ghaderi et al. 2015). The procurement of transport infrastructure and delivery through ‘project delivery industry’ are maintained by co-ordination of all people in the New South Wales.

The first data set that is provided by my organization is secondary data. The data is collected by other person and now I am using the data in this statistical documentation. Therefore, the data set is secondary to me. Although, the data set could be biased and erroneous, I am performing the analysis with that secondary data with true belief.

The variables that are involved in the data set is qualitative as well as quantitative. The first variable ‘Mode’ is the indicator of mode of transportation that is nominal (categorical) variable. It has four levels that are ‘Bus’, ‘Train’, ‘Ferry’ and ‘Light Rail’ (Clark 2013). The second variable ‘Date’ refers the date given in Date/month/year notation. It is another nominal (categorical) data. The dates are from 8^th August, 2016 to 14^th August, 2014. ‘Tap’ variable has two levels that are ‘On’ and ‘Off’. It is another nominal (categorical) data. ‘loc’ variable denotes the location of stops in New South Wales (for bus postcodes and other names of the stations). ‘count’ variable denotes the total number of tap on and tap off on the certain location and certain date. It is the quantitative variable

I have collected the data set by survey method. The target population was the common population of New South Wales who travel by transportation services. The data of only gender and transportation data is collected in this regard. The data set is primary data as I myself have collected the data set. The variables of the new collected data set are qualitative in nature. However, the number of samples of the data set is not adequately large. Also, the primary data set has only two variables; hence, it is in-sufficient and inadequate.

Table 1: Transportation used by New South Wales

In the time period of 8^th August, 2016 to 14^th August, 2016, the four types of transportation system are tabulated as per count. Bus is mostly used as transportation mode (count = 502) followed by Train (count = 460). The other two types of transportation mode are non-conventional and hence are less preferred. These are- ‘Ferry’ (count = 20) and ‘Light-rail’ (count = 18).

Figure 1: Frequency distribution of transportation mode

The frequency distribution of transportation mode shows that 50% passengers travel by bus and 46% passengers travel by train. Each of 2% of the passengers choose transportation system (ferry and light-rail) (Alexander and Walkenbach 2013).

Table 2: One-sample Z-test

In this part, it is fact to decide whether more than 50% of the public transport users of New South Wales use the specific mode of transportation or not. It is previously observed that 50.2% of the public transport users of New South Wales uses the most preferable transportation mode that is ‘bus’. Hence, the public transportation by bus is taken under consideration.

Null hypothesis (H₀): The percentage of public transport users in NSW who uses bus is equal to 50%.

Alternative hypothesis (H_A): The percentage of public transport users in NSW who uses bus is greater than 50%.

The One-sample Z-test is used to test the hypothesis in this regard. The level of significance of the hypothesis testing is assumed to be 5%. The calculated Z-statistic is found to be 0.12649. The One-tailed Z-test would be appropriate here as per the hypotheses stated (Wasserstein and Lazar 2016). The p-value of the upper critical value is found to be 0.44967. The p-value is less than the level of significance; the null hypothesis could be rejected in this regard.

It could be interpreted that the percentage of public transport users who uses bus in NSW is equal to 50%. Therefore, the research question is found invalid as no particular mode of transportation is used to travel by more than 50% passengers.

The government of New South Wales is required to decide a suburb among Parramatta, Bankstown and Gosford from where they should build an underground Railway line to central.

Table 3: Frequency table of trains as per Locations

The numerical summary of the locations of the three stations as transported by train shows that-

Most number of the passengers who travel by train travels from ‘Parramatta’ station to central (count = 4087) in the observational period. The ‘Bankstown’ station is not so much busy towards central (count = 446). ‘Gosford’ station is least busy station as only 75 passengers travelled via train from 8^th August to 14^th August.

Figure 2: Frequency distribution of trains as per Locations

Note that, the frequency of passengers is considerably higher for ‘Parramatta’ railway station rather than ‘Bankstown’ railway station and ‘Gosford’ railway station. 

Table 4: Table of two-sample t-test assuming unequal variances

The hypotheses are-

Null hypothesis (H₀): The difference between mean counts of passengers of taps on and taps off situations is unequal to 0 (Leendertz 2016).

Alternative hypothesis (H_A): The difference between mean counts of passengers of taps on and taps off situations is equal to 0.

The two-sample t-test assuming unequal variances (Student’s t-test) is executed to analyze the data. The mean count of passengers of taps off situation is 361.78 and taps on situation is 193.143. The calculated t-statistic is found to be 0.743896. Two tailed t-test is applicable as per the hypotheses declared. The two-tailed p-value is 0.469 that is greater than 5%. Hence, the null hypothesis of inequality of mean counts (difference ≠ 0) could be rejected with 95% evidence (De Winter 2013). Therefore, the mean counts of the passengers of taps on situation is significantly unequal to the mean counts of the passengers of taps off situation.

The analysis as per two variable analysis of secondary data set indicates that NSW government must construct the railway line between ‘Parramatta’ and ‘Central’. The reason is that the government would be much profitable to construct the railway track in that route. Also, the tap on condition of the railway route is much preferable towards the passengers.

Table 5: Gender wise distribution of transportation passengers according to the types of vehicles

The data of survey analysis indicates that both males and females prefer buses as a major transportation way (Male = 34 and Female = 28) followed by train (Male = 18 and Female = 21). Ferry and Light-rail are both least preferable as per both kinds of genders (Slezà et al. 2014).

Table 6: Chi-square test of association

To test the difference in preference between different gender (Male and Female) in terms of their transportation mode (Bus, Ferry, Light-rail and Train), the Chi-square test is applied.

The hypotheses are-

Null hypothesis (H₀): Two categorical factors ‘Gender’ and ‘Mode’ are independent to each other.

Alternative hypothesis (H_A): Two categorical factors ‘Gender’ and ‘Mode’ are associated to each other.

The Chi-square test indicates that the value of ‘Chi-square statistic’ = 2.50906 with degrees of freedom = 3. The p-value (0.47366) is greater than 0.05. Because of it, the testing of hypothesis has significance. The null hypothesis cannot be rejected with 95% confidence. Therefore, gender and transportation of mode are independent to each other (McHugh 2013).

Therefore, it could be interpreted that there is no statistical significant difference in preference of the mode of transportation for gender types.  

Figure 3: Grouped bar plot of frequency of passengers of various ways as per gender

The analysis of two data sets are accomplished with the help of MsExcel-2016 software. The analysis from secondary data set shows that bus is most preferred transportation mode that is utilized by 50% of the passengers. Most of the passengers uses Parramatta railway station. The passengers who travel by train in ‘taps on’ condition is more in average is greater than the passengers who travel by train in ‘taps off’ condition. The difference in preference between gender in terms of transport mode is invalid.

Further, in future, the research could be elaborated as the secondary data set would include more samples and more predictive factors to estimate the causes and their significance about preferences of transportation mode.

It could be recommended that NSW government should take necessary measures for the improvement of transportation system especially via ferry or light-rail. The new railway route is essential between Parramatta and Central. The quick and appropriate measure of the government might be beneficial for the regular travelers.

Alexander, M. and Walkenbach, J., 2013. Excel dashboards and reports (Vol. 17). John Wiley & Sons.

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Clark, G., 2013. 5 Secondary data. Methods in Human Geography, p.57.

De Winter, J.C., 2013. Using the Student's t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10).

Ghaderi, H., Namazi-Rad, M.R., Cahoon, S. and Fei, J., 2015. Improving the quality of rail freight services by managing the time-based attributes: the case of non-bulk rail network in Australia. World Review of Intermodal Transportation Research, 5(3), pp.203-220.

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McHugh, M.L., 2013. The chi-square test of independence. Biochemia medica: Biochemia medica, 23(2), pp.143-149.

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Wasserstein, R.L. and Lazar, N.A., 2016. The ASA’s statement on p-values: context, process, and purpose. The American Statistician, 70(2), pp.129-133.