Research Statement
In the world of globalization, countries have become increasingly interconnected. In building a strong intercountry relationship transport and communication system play an important role. Now a day, different modes of transportation is being used. People travel to their desired destination by road, water or air (Neirotti et al., 2014). Both private and public sector have now come forward to invest in transportation. Means of transportation being a part of infrastructural development has a vital role in economic growth of a nation (Meyer & Shaheen, 2017). With growing population, traffic congestion has become a major problem worldwide (Mayer & Trevien, 2017).
The problem of traffic congestion in Australia is going worst day by day. People in popular capital cities in Australia especially Sydney and Melbourne face trouble of congestion in rush hours (Boyac?, Zografos, & Geroliminis, 2015). During peak hours, it takes more than double time for commuters to reach office, schools and to other important places. New South Wales is one of the most populated State of Australia. Around one third of Australia’s total population live in New South Wales (Chaturvedi & Kim, 2015). The estimated population of New South Wales during 20152016 is 7.7 million (Bugheanu, A. M., & Colesca, S. E. (2016). With growing population, the state is likely to face severe traffic congestion. The research paper aims at finding the impact of population growth on transportation system of New South Wales.
Literature Review
In connection to growing concern for impact of population on transportation system several scholarly research have already been undertaken in this field. In a paper formulated by Kroes et al., 2013 focus is given on finding the perceived value of overcrowding in public transport vehicles in France. The paper has made a qualitative research analysis to derive perceived value in terms of their physical comfort (Kroes et al., 2013). Passengers are classified into 4 groups Type 1 contains passengers who fear closeness to other passengers, Type 2; this group contains people who enjoy traveling a time of their own, Type 3; those who wait for saving time and finally Type 4; those acting as individualist. The result of the paper reveals a clear distinction of preferences to avoid crowding out among the four groups. Other papers as well (Lam, Leung & Chu, 2016) support the results.
Another study developed by Haywood et al., 2017 concentrates on crowding out of public transport vehicles. The paper has twin objectives. First is to analyze the discomfort in public transport with varying density level and across heterogeneous groups. Second is to find the reason for this effect of discomfort. The level of discomfort is analyzed using three different perspectives dissatisfaction arising from not getting seat and for long standing hours, discomfort due to having less opportunity to utilize time at times of journey and finally uneasiness from being physically close in the crowded vehicles (Haywood, Koning & Monchambert, 2017). The paper concerns with interaction between in vehicle density (IVD) and crowding discomfort (CDD). These seem to be significant and negative for discomfort measured from standing, Wasted time and over closeness (Clifton & Mulley, 2016). Passengers having high level of dissatisfaction in any of these three discomfort have greater disutility from crowing effect (Lam, Taghia & Katupitiya, 2016). The results show Standing and Time Wasted from it are the most important aspect affecting satisfaction (Geertman et al., 2017). This is followed by the problem of over closeness. It is found that people have high disutility in times of high density followed from long standing during their journey (Pearce, 2017).). The disutility arises from the not getting scope for utilizing time while traveling and physical closeness to other passengers.
Prior Research on Overcrowding in Public Transport Vehicles
A similar research is conducted by Tiranchi, Hensher and Rose in their paper in 2016. The effect of passengers’ density is different at bus stops, rail stations and inside trains and buses. The single paper researches on multiple dimension relating to long waiting time, reliability of travel time, wellbeing of passengers, importance of time saving in vehicles, choice of travelling route, size of vehicles and its fare (Tirachini, Hensher & Rose, 2013). The externalities from crowding in vehicles are for bus and rai service in Sydney are estimated to capture the impact of crowding on prediction of demand and invehicle time saving. A regression estimation is made to find out how crowding impacted passengers in times of alighting and boarding. The high level of occupancy raises the probability of vehicle circulation (Philip et al., 2014). This causes people to wait for a long time in stations and bus stops increasing the waiting time and variability of travel time (Hendrigan & Newman, 2017). While coming to impacts of health and wellbeing the paper documented an increase in anxiety, exhaustion, stress level with perceived risk for personal safety and interruption of privacy. These factors contribute to a negative valuation for the resulting from high occupancy of passengers at stations and stops (Chen, Gong & Paaswell, 2012). The passengers’ choice is likely to be affected from competing routes and unbalanced loads of vehicles (Gharehbaghi & Georgy, 2015). The externality arises from crowding increases marginal cost for travelling. This external effect should be taken into consideration for designing the system of public transport. The frequency for vehicles availability, its size and fare are subjected to the impact of population growth on vehicles (Glazier et al., 2014).
The research paper developed by Li et al. in 2016, studies traveler’s mode of choice in the presence of a multimodal network. The paper conducts a comprehensive study on perceived behavioral differences based on a travel time reliability (TTR) model with considering crowding out for different transport modes with a multimodal network. A survey of passengers’ is conducted at two stage including a combined mode of travel with park and ride in reference to four major factors ( Li, Gao & Tu, 2016). In the first stage, the perceived travel time reliability in obtained with respect to delays at different transportation mode (Li, Burke & Dodson, 2017). After obtaining perceived TTR, in the next stage the paper designs an experimental analysis using balance theory of utility for the stated preference survey to examine mode of choice behavior and the difference in passengers’ perception in TTR and crowding (Chen, Gong & Paaswell, 2012). The results obtained from the first stage survey revealed that the travelers have differential tolerance level regarding the span of accepted delays for different modes (Saberi et al., 2017). The perceived TTR of travelers measured in relation to a certain delay differs by transportation mode (Ricciardi, Xia & Currie, 2015). The survey results from the second stage further confirms the result of perceived difference obtained from the first stage survey (Grinin & Korotayev, 2018). TTR based on different modes greatly influences choice behavior and hence should be taken for consideration in times of forecasting models used for analyzing mode in a multimodal network (Blanco Ramírez, 2016).
Effect of Passenger Density on Operations and User Perception
There are studies that found some contrasting results on effect of population growth on mode of transportation and satisfaction. According to Olsen, Macdonald and Ellaway, 2017 the effect of population growth on trasportation is not uniform. That is it is not true that population growth always have detremental effect on satisfaction and transportation (Olsen, Macdonald & Ellaway, 2017). Studies aim at finding how overtime satisfaction changes with mode of trasportation. It is found that satisfaction level of two third of the population increases with their mode of transporatation (Boyac?, Zografos & Geroliminis, 2015). The unsatisfied people are those who has poor health condition. Access to car was related with satisfaction level of transportation (Yao & Yang, 2012). The deprivation or dissatisfaction offsets when adjusted for car access to household. When population from 1997 to 2010 are considered, the results shows most of the retired individuals travelled by public transport with satisfaction (Bugheanu & Colesca, 2016). However, those using private car are relatively less satisfied following increased consumption level.
Based on the theoretical backgroud and research objectives following hypotheses are framed
Hypothesis 1
H01: There is no significant relation between railway trasporation and population growth
HA1: Significant relation exist between railway transpportation and population growth.
Hypothesis 2
H02: There is no significant relation between bus transportation and growth of population.
HA2: Population growth significantly influences bus transportation
Hypothesis 3
H03: There is no significant relation between aircraft travel and growth of population.
HA3: Population growth significantly influences aircraft travel and population growth
Hypothesis 4
H04: There is no significant relation between private car transportation and growth of population.
HA4: A significant relation exists car transportation and population growth
Data on population and different mode of transportation are collected along with length of road and safety measures. The timeframe considered are varied from 2009 to 2016. All the relevant data are collected for New South Wales, one if the major capital city of Australia. The data on population is obtained from “Australian bureau of statistics’. The data related to transportation system are collected from ‘NSW and Sydney Transportation Facts’.
The paper has conducted a quantitative research analysis. The obtained data are analyzed with statistical software namely SPSS. The trend in population growth is considered by constructing a line graph with respect to time. The description statistics is estimated to have an overall idea about the mean, variability, range of the data set for the chosen timeframe (Golub & Martens, 2014). As far as mode of transportation is concerned, transportation via Rail and Bus routes are considered. The length of roads are considered for different time to find whether population growth significantly influence length of roads. Finally, data on road, rail and aviation accidents are considered to analyze the safety status.
Contrasting Results on the Effect of Population Growth on Transportation System
Chart 1: History of congestion level in NSW
The congestion history in New South Wales is presented in the above figure. In 2008, the congestion level is below 20%. From 2009 onwards, the congestion level started to increase and remain at the level 20% until 2015. Between 2015 and 2016, the congestion level grows up slightly (Parolin & Rostami, 2017).
Char 2: Evening Peak Hours in NSW
The evening peak rate for different days of the week are considered. The peak rate for Monday and Tuesday is almost same (Pettit et al., 2017) . Evening is high in Wednesday and Thursday. Evening peak in Friday is less as compared to that is Thursday.
Chart 3: Morning Peak in NSW
The morning peak is greater as compared to the evening peak. Because of rush hours for schools and offices morning peak is higher. The peak rate is maximum for Wednesday. For Tuesday and Thursday, peak rate is around that in Wednesday. For Monday and Friday, peak rate is lower as compared to three other days but still higher than evening peak.
Table 1: Descriptive Statistics of Population
Population 

N 
Valid 
8 
Missing 
0 

Mean 
7419350.00 

Median 
7378750.00 

Std. Deviation 
205955.719 

Variance 
42417758392.857 

Skewness 
.337 

Std. Error of Skewness 
.752 

Kurtosis 
.807 

Std. Error of Kurtosis 
1.481 

Range 
613175 

Minimum 
7133275 

Maximum 
7746450 

Percentiles 
25 
7261393.75 
50 
7378750.00 

75 
7607387.50 
The average population for the chosen time is 7419350. The population has reached to its maximum level in 2016 with population being 7746450.The population in New South Wales has constituted an upward trend overtime.
Table 2: Descriptive Statistics of mode of transportation
Statistics 

Rail 
Bus 
Aircraft 
Cars 

N 
Valid 
8 
8 
8 
8 
Missing 
0 
0 
0 
0 

Mean 
3293.88 
1184.00 
924.25 
46645.50 

Std. Error of Mean 
96.499 
42.399 
44.574 
974.573 

Median 
3320.00 
1187.50 
896.25 
46743.50 

Mode 
2803^{a} 
998^{a} 
770^{a} 
41806^{a} 

Std. Deviation 
272.941 
119.923 
126.074 
2756.510 

Variance 
74496.982 
14381.429 
15894.723 
7598346.286 

Skewness 
.606 
.419 
.647 
.400 

Std. Error of Skewness 
.752 
.752 
.752 
.752 

Kurtosis 
.166 
.714 
.308 
.163 

Std. Error of Kurtosis 
1.481 
1.481 
1.481 
1.481 

Range 
859 
335 
379 
8828 

Minimum 
2803 
998 
770 
41806 

Maximum 
3662 
1333 
1149 
50634 

Sum 
26351 
9472 
7394 
373164 

Percentiles 
25 
3102.50 
1069.00 
828.98 
44827.25 
50 
3320.00 
1187.50 
896.25 
46743.50 

75 
3494.50 
1299.50 
1008.35 
48681.00 

a. Multiple modes exist. The smallest value is shown 
The average number of visitors in rail transportation in the last 8 years is 3294. The same for bus, aircraft and cars transportation are 1184, 924 and 46645 respectively. The standard deviation in rail transportation is 272.941, for bus it is 119.923 and for aircraft and bus standard deviation is 126.074 and 2756.510 respectively. The standard deviation are lower than mean implying less variability in mode of transportation.
Table 3: Registered vehicles
Year 
Registered vehicle 
Annual growth 
2011 
5.6 
2.30% 
2015 
6.2 
2.50% 
The number of registered vehicles has increased in between 2011 and 2015. During 2011, the number of registered vehicles were 5.6 million which increases to 6.2 million in 2015. The annual growth rate for registered vehicles has increased from 2.3% to 2.5%.
Table 4: Correlation between population and different mode of transportation
Correlations 

Population 
Rail 
Bus 
Aircraft 
Cars 

Population 
Pearson Correlation 
1 
.654 
.737^{*} 
.977^{**} 
.842^{**} 
Sig. (2tailed) 
.079 
.037 
.000 
.009 

N 
8 
8 
8 
8 
8 

Rail 
Pearson Correlation 
.654 
1 
.621 
.610 
.762^{*} 
Sig. (2tailed) 
.079 
.100 
.108 
.028 

N 
8 
8 
8 
8 
8 

Bus 
Pearson Correlation 
.737^{*} 
.621 
1 
.643 
.535 
Sig. (2tailed) 
.037 
.100 
.085 
.172 

N 
8 
8 
8 
8 
8 

Aircraft 
Pearson Correlation 
.977^{**} 
.610 
.643 
1 
.806^{*} 
Sig. (2tailed) 
.000 
.108 
.085 
.016 

N 
8 
8 
8 
8 
8 

Cars 
Pearson Correlation 
.842^{**} 
.762^{*} 
.535 
.806^{*} 
1 
Sig. (2tailed) 
.009 
.028 
.172 
.016 

N 
8 
8 
8 
8 
8 

*. Correlation is significant at the 0.05 level (2tailed). 

**. Correlation is significant at the 0.01 level (2tailed). 
The correlation between population and rail transportation is 0.654. This means with growth of population, the use of rail transportation has increased. For bus, the correlation is negative having a correlation coefficient of 0.757. That means with growth of population use of bus transportation reduces because of high congestion on roads. For aircraft and cars, there is high correlation between population and these transportation means.
Hypotheses
The relation obtained from correlation analysis further analyzed using linear regression
Relation between population and rail transport
Table 5: Regression model between rail and population
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.654^{a} 
.427 
.332 
223.077 
a. Predictors: (Constant), Population 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
3134.429 
3038.397 
1.032 
.342 

Population 
.001 
.000 
.654 
2.116 
.079 

a. Dependent Variable: Rail 
The R square value of the model is 0.427 implying a positive relation between rail transport and population. However, the coefficient is not statistically significant. Therefore, the null hypothesis of no significant relation between rail transport and population growth is accepted.
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.737^{a} 
.543 
.467 
87.520 
a. Predictors: (Constant), Population 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
4368.793 
1192.059 
3.665 
.011 

Population 
.0004 
.000 
.737 
2.673 
.037 

a. Dependent Variable: Bus 
The regression results shows a statistically significant relation between bus transportation and population growth. An inverse relation exists between the two variables. Therefore, the null hypothesis of no significant relation between bus transportation and population growth is rejected.
Relation between population and aircraft transport
Table 7: Regression model between aircraft and population
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.977^{a} 
.955 
.948 
28.884 
a. Predictors: (Constant), Population 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
3514.111 
393.409 
8.932 
.000 

Population 
.001 
.000 
.977 
11.286 
.000 

a. Dependent Variable: Aircraft 
The estimated coefficient of population is 0.001. Therefore, population growth has a negligible impact on aircraft transportation. The coefficient is statistically significant as obtained from the significant p value. The positive and significant coefficient implies population growth positively influences aircraft transportation.
Table 8: Regression model between cars and population
Model Summary 

Model 
R 
R Square 
Adjusted R Square 
Std. Error of the Estimate 
1 
.842^{a} 
.710 
.661 
1604.402 
a. Predictors: (Constant), Population 
Coefficients^{a} 

Model 
Unstandardized Coefficients 
Standardized Coefficients 
t 
Sig. 

B 
Std. Error 
Beta 

1 
(Constant) 
37004.462 
21852.569 
1.693 
.141 

Population 
.011 
.003 
.842 
3.829 
.009 

a. Dependent Variable: Cars 
For car transport, a positive coefficient is obtained from the regression result. The p value for the coefficient is 0.009, which is less than significance level. Therefore, the null hypothesis of no significant relation between population and car transportation is rejected.
Table 9: Population and road length
Length of roads 

Year 
Population 
Length 
2009 
7133275 
184,761 
2016 
7746450 
185,000 
With an increase in population overtime, the length of roads has increased. The length of roads in 2009 was 184,761 km, which increases to 185,000 km in 2016.
The analysis has shown, population in New South Wales is growing continuously. This is now considered as one of the most populated regions of Australia. With increase in population, there is an increasing pressure on transportation system. The number of registered vehicles have increased significantly with level of population. This is in line with results of previous research papers (Stevenson et al., 2016).
With growth of population, the use of different mode of transportation increases. The used mode of transportation are rail, bus, aircraft and cars. A positive relation exists between rail transport and population. The rail patronages though have increased for city rail and metropolitan areas, in regional and rural areas patronages has accounted a decline as compared to previous years. This means more people are now shifted to the metropolitan areas (Wanke, Barros & Figueiredo, 2016). Popualtion growth has a negative signifiant relation with bus transportation. However, with increasing population crowd in public and private transportation is likely to increases. To avoid heavy crowd, people prefer to use their own cars. A statistically signifcant positive relation exists between car use and popualtion growth. To avoid traffic congestion people increasingly uses the means of airtransport. Popualtion growth has a positve significant influence on aircraft users.
Methodology
For easing the congestion level, new roads are constructed. The congestion level in New South Wales is increasing at a comparatively slow rate.
Conclusion
The first research question of this paper is how population growth impacts transportation system of New South Wales. Overtime the population in New South Wales have increased. With this, the number of vehicles on roads have also increased.
Secondly, the paper tries to find relation between mode of transportation and population growth. Passengers travelling by rail records a significant increase. However, most of the increase in transportation have recorded in metropolitan areas. For bus transportation population growth negatively influnces bus trasportation leading to a decrase in number of people using bus transportation. The use of cars and aircrafts have recorded a considerable incrase.
Third objective is to find whether length of road increases along with population. The length of roads in NSW has increased to control the traffic congestion.
The main limitation of the paper is only a limited data can be obtained and analyzed. The timeframe taken for comparison is relatively small. Another limitation is that the study focuses only on New South Wales. However, major capital cities like Sydney and Melbourne also faces problem of congestion because of overpopulation. The scenario in other cities has not considered in the research.
The limitation of present research opens up scope for further research in this field. The paper makes only quantitative analysis on transportation and population growth. Research can be made to find how population growth affect comfort of people. For this, a qualitative research needs to be conducted on the comfort and satisfaction level of people along with population.
The paper finds that with growing population the preference for rail transportation has increased. Therefore, government should focus on a better development of rail transportation in the city.
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