Public Transport Inequality and Utilization: Exploring the Perspective of the Inequality Impact on Travel Choices

Abstract

Public transport (PT) inequality is evidenced to have adverse consequences on various social–urban–economic aspects of urban residents’ lives; however, the impact of this inequality on PT itself, particularly its utilization, is a less explored area of study. This paper examines the association between PT inequality and PT utilization patterns in Melbourne, Australia, using journey-to-work data in a multivariate regression model. By analyzing commuting and socioeconomic factors, we investigate how PT inequalities affect the travel choices of the residents. Our findings indicate that regions with lower PT inequality demonstrate higher PT usage for daily commuting, emphasizing the importance of the equitable distribution of resources. This finding is consistent across different PT modes of trains, trams, and buses, and using different inequality measures of the Gini index and the 90/10 ratio. Spatial variations and factors like income levels, education, home ownership, and age are also found to influence PT usage. The findings offer valuable insights into the potential impact of incorporating equity considerations during the planning stages of PT projects. Furthermore, they could justify targeted interventions aimed at enhancing the equity of PT services.

1. Introduction

In urban settings worldwide, providing efficient and accessible public transportation systems plays a pivotal role in shaping the socioeconomic landscape and enhancing the quality of life for residents [1]. Public transport (PT) networks offer an affordable and environmentally sustainable means of mobility, which is crucial in reducing traffic congestion, air pollution, and energy consumption [2,3]. This, in turn, leads to enhanced urban connectivity, economic productivity, and improved overall urban livability. Consequently, promoting PT as an alternative to private vehicles is an essential tenet of modern urban planning and sustainable development strategies [4,5,6,7].

Encouraging the utilization of PT provides many benefits that extend beyond an immediate reduction in traffic congestion. Adopting public transportation contributes to reducing carbon emissions and energy consumption, aligning with global efforts to combat climate change and environmental degradation [8]. Additionally, PT systems can foster a sense of community engagement by facilitating interactions among diverse social groups during commuting, thereby promoting social cohesion [9]. Furthermore, using PT often leads to increased physical activity, which, in turn, contributes to the betterment of public health [10]. As cities strive to achieve sustainability goals, the need to actively encourage PT use becomes even more apparent [11].

The decision to use PT is influenced by a set of interrelated factors, including individual, environmental, and spatial dimensions. Socioeconomic status, accessibility to transit stops, travel time, convenience, and perceived safety all play crucial roles in determining the mode of transportation individuals choose [12]. Moreover, the spatial distribution of PT services within a city significantly impacts their usage. While cities try to provide equitable access to PT for all citizens, service availability and reliability disparities can lead to PT inequality, wherein specific population segments are marginalized due to limited access or unattractive service conditions [13,14].

In recognition of the socioeconomic implications associated with PT inequality, this study aims to investigate the determinants of PT usage within the Melbourne Metropolitan Area’s journey-to-work context, with a specific focus on understanding the impact of PT inequality. By exploring the factors influencing PT utilization, we seek to contribute to a deeper understanding of how inequality, accessibility, and socio-demographic factors shape patterns of PT use. Through this investigation, we aim to provide insights that can inform policy interventions aimed at reducing PT inequality and enhancing transportation equity in urban settings.

We hypothesize that PT inequality could influence its usage patterns. This hypothesis parallels the notion that more egalitarian societies experience higher overall well-being and life satisfaction [15]. Accordingly, we intend to explore the relationship between PT equality and its utilization. By examining whether individuals living in regions with a more equitable distribution of PT services demonstrate higher utilization rates, we aim to not only contribute to understanding the relationship between transportation equity and behavior but also help in justifying more equitable PT development plans from their social benefits as well as economic benefits.

Our hypothesis about the impact of PT inequality on its utilization is grounded in the existing pieces of evidence on the interconnections between PT equality and broader socioeconomic equity, which, in turn, significantly influences PT utilization. The relationship between PT equity and utilization is not isolated; rather, it is linked to many factors that shape urban mobility. Several studies have explored the effects of PT equality on socioeconomic disparities. Ref. [16] states that a lack of equitable PT services can exacerbate inequalities in access to employment and essential services. Such disparities subsequently impact the socioeconomic mobility of marginalized communities, making PT less appealing to them. Furthermore, research by [17] highlights the increasing attention given to transport planning’s role in addressing social equity. Their work indicates that the spatial distribution of PT services significantly impacts the accessibility of opportunities for different socioeconomic groups. A more equitable distribution of PT services can reduce disparities in access to jobs, education, and healthcare, ultimately fostering socioeconomic equity. The influence of PT equity on socioeconomic equality has also been studied by [18]. They analyze PT’s role in providing access to opportunities for vulnerable populations and argue that policies addressing PT inequality can contribute to broader social equity agendas by enhancing the ability of marginalized groups to access essential services and employment opportunities. Moreover, research by [19] concludes that the equitable planning of PT services can directly impact individuals’ perceptions of accessibility to essential services. As PT services are designed to serve communities equitably, people are more likely to perceive PT as a reliable and accessible mode of transport, leading to increased utilization rates.

Given the discussed literature, the equitable distribution of PT services is evidenced as a basis for reducing socioeconomic disparities and, consequently, increasing the utilization of PT among disadvantaged groups. Policies and planning strategies prioritizing PT equity can reduce disparities in access to opportunities, education, and employment [20]. As these socioeconomic disparities diminish, public transit becomes an increasingly attractive and essential mode of transportation for individuals from diverse socioeconomic backgrounds.

To empirically investigate the hypothesis above, we analyze the travel-to-work journeys of employed individuals within the Melbourne Metropolitan Area. Melbourne, a vibrant metropolitan known for its diverse population and extensive PT network, offers an ideal context for this study. By examining the spatial distribution of accessibility to PT modes and demographic factors, we intend to shed light on the complex dynamics that shape individuals’ commuting preferences. This analysis reveals the relationship between PT inequality and citizens’ tendency to utilize PT services.

Our analysis reveals that PT inequality adversely impacts the utilization of key public transportation modes, including trains, trams, and buses, within the Melbourne Metropolitan Area. Our findings consistently show a relationship between PT inequality and its utilization for all the PT modes. The influence of inequality was consistent across different inequality measures, further strengthening the robustness of our conclusions. PT usage rates are the basis of many projects’ justification plans; our findings on the impacts of PT inequality can offer practical policy recommendations for bolstering the equity of PT projects and could also justify intervening in current PT services to foster greater equity.

These findings contribute to the literature from various theoretical and empirical perspectives. The impact of PT inequality on access to other urban infrastructure has been the subject of numerous studies (e.g., [21,22,23]); however, the rebound effect of this impact on PT utilization rates is a less explored area. Our findings shed light on this phenomenon and suggest a new area for future research. While this relationship is examined in the context of the Melbourne Metropolitan, the findings, and more importantly, the methodology can be applied to comparable cities around the world. Moreover, the findings provide practical policy implications. Translating the usage rates into monetary values could provide a basis for justifying more equitable PT plans and for intervening in the existing PT infrastructure to offer more equitable services.

The remainder of this paper is organized as follows. Section 2 describes the methodology and analysis techniques. Section 3 presents the study area and data. The results and findings of this study are presented in Section 4. Section 5 discusses the implications of the findings and their relevance for urban planning and policy. Finally, Section 6 concludes the paper with a summary of key findings and suggestions for future research.

2. Methodology

Our study adopts a detailed analytical approach, employing a regression model to delve into the relationship between PT utilization rates and a comprehensive set of explanatory variables. Among these variables, $PTusage$, $PTaccessibility$, and $PTinequality$ play a pivotal role. We gauge $PTusage$ as the share of employed individuals who use PT for their daily commute. The $PTaccessibility$ is obtained as the inverse distance to the PT infrastructure. For the $PTinequality$, which is of key importance to our study, we employ two different techniques of the $90/10$ ratio and the $Giniindex$, which yield two inequality variables, but we use them in separate models to test the robustness of our results. We elaborate more on these variables and also other relevant explanatory variables in the next section.

We utilize the flowing regression formula for each mode of PT, which uses its own set of variables. This provides us with 6 sets of results, for 3 PT modes and 2 inequality measures for each.

$$PTusage={\beta }_0+{\beta }_1\times PTaccessibility+{\beta }_2\times PTinequality+{\beta }_3\times X_3+\cdots +{\beta }_n\times X_n+ϵ$$

(1)

As discussed, in this equation, $PTusage$ represents the proportion of employed individuals who utilize PT for their daily work commutes. $PTaccessibility$ stands for the accessibility to PT, gauged by the inverse distance to the nearest station. Importantly, the focal point of our investigation, $PTinequality$, captures the disparities in PT usage, either through the $90/10$ ratio or the $Giniindex$.

Additionally, a suite of control variables, denoted as $X_3,X_4,\dots ,X_n$, is incorporated to account for other relevant factors that might influence PT usage patterns. These encompass demographic characteristics, distances to the business district, and economic indicators, among others. These inclusions acknowledge the socioeconomic dimensions influencing transportation choices.

The error term $ϵ$ signifies unobserved variables or random fluctuations that affect PT usage but are not explicitly captured by the model. By acknowledging the presence of this term, we accommodate for factors beyond the scope of our current analysis, ensuring the robustness and validity of our regression results.

Finally, we acknowledge that omitted variable bias (OVB) is a concern in regression analysis, where unobserved variables correlated with both the independent and dependent variables can lead to biased estimates. In addressing potential OVB in our model, we take several precautions to ensure the robustness of our findings. First, we include a comprehensive set of control variables that are theoretically and empirically linked to the outcome variable, $PTusage$. These controls help to capture additional variation in the dependent variable and reduce the potential bias from omitted variables. Furthermore, we conduct sensitivity analyses and robustness checks using alternative PT modes and inequality measures, to confirm the stability of our results across different model specifications.

Through this comprehensive regression framework, we aim to gain insights into the determinants of PT usage, focusing on the role of PT inequality in shaping transportation behavior. Such insights can inform policymakers, urban planners, and transportation authorities in crafting effective strategies to enhance PT utilization and promote equitable access to transportation services.

3. Study Area and Data

This study focuses on the Melbourne Metropolitan Area, a vibrant urban region well known for its diverse population and extensive public transportation network. With growing concerns about traffic congestion, environmental sustainability, and equitable urban development, studying the relationship between PT usage and inequality holds significant implications for metropolitan planning and policy decisions.

Melbourne’s PT system comprises trains, trams, and buses serving millions of commuters daily. However, despite its substantial reach, variations in usage and accessibility persist across different modes and regions. Figure 1 illustrates the distribution of PT infrastructure across the metropolitan. Our study aims to benefit urban planners, policymakers, and researchers by providing insights into the factors shaping PT usage patterns and their implications for urban mobility and social equity.

The primary data source for this study is the Australian Bureau of Statistics’ (ABS) Census 2021, which offers comprehensive demographic, socioeconomic, and commuting information. The spatial analysis is conducted at the Statistical Area Level 1 (SA1), ensuring a fine-grained exploration of geographic variations in PT usage and its determinants. SA1 refers to the smallest census geography in Australia, serving as a fundamental unit for demographic analysis. Across the Melbourne Metropolitan Area, there are a total of 11,488 SA1 regions. These areas are characterized by a median size of approximately 0.15 km².

The dependent variable, PT usage rate, quantifies the proportion of employed individuals who utilize PT for their journey to work. This metric is calculated by dividing the number of employed persons using PT by the total number of employed persons. The same procedure is applied separately for the main PT modes: trains, trams, and buses. Because people could use more than one PT mode and the available data format, the summation of users across all PT modes is higher than the total number of PT users. This measure encapsulates the practical use of public transportation among the working population, reflecting commuting behaviors and preferences.

Public transport accessibility is gauged by computing the inverse distance to the nearest PT station for each SA1 region. This variable captures the ease of access to PT services, acknowledging that proximity to transit hubs significantly influences utilization patterns.

PT inequality, a central explanatory variable, is assessed at the Local Government Area (LGA) level. Inequality can be defined as differences among people in their command over social and economic resources [24]. Accordingly, we measure PT inequality as accessibility disparities using the 90/10 ratio and the Gini index methods. The $90/10$ ratio calculates the ratio of income or other relevant indicators between the 90th percentile and the 10th percentile of the population. In the context of this study, the 90/10 ratio applied to PT inequality signifies the disparity in access between the top 10% and the bottom 10% of commuters within the PT network [25]. The Gini index, on the other hand, provides a quantitative measure of inequality within a distribution by assessing the area between the Lorenz curve (representing the actual distribution of the variable) and the line of perfect equality (representing a completely equal distribution) [26]. In the context of this study, the Gini index serves as a comprehensive measure of PT inequality by capturing the overall disparity across the entire distribution of PT accessibility rates.

The “distance to CBD” variable is a proxy for access to employment opportunities and latent urban factors. Other essential variables integrated into the model include median age, gender ratio, tertiary education attainment, household income, and home ownership. These covariates capture key socio-demographic dimensions contributing to PT usage pattern variations.

By employing these inequality measures alongside other control variables, our regression models offer a robust framework to explore the relationship between PT usage rates and inequality while accounting for potential confounding factors.

4. Results

4.1. Descriptive Statistics

The study variables include diverse factors influencing PT usage patterns within the Melbourne Metropolitan Area.

Table 1. Summary Statistics of Study Variables.

	N	Mean	Std. Dev.	Q1	Median	Q3
Train usage (%)	11,185	$2.609$	$3.027$	$0.000$	$1.948$	$3.887$
Train accessibility	11,185	$0.915$	$1.171$	$0.290$	$0.567$	$1.111$
Train inequality—90/10	11,185	$7.638$	$5.462$	$5.129$	$5.991$	$7.131$
Train inequality—Gini	11,185	$0.417$	$0.085$	$0.386$	$0.418$	$0.443$
Tram usage (%)	11,185	$0.908$	$2.546$	$0.000$	$0.000$	$0.000$
Tram accessibility	11,185	$1.160$	$2.806$	$0.053$	$0.134$	$0.642$
Tram inequality—90/10	11,185	$5.688$	$5.133$	$1.828$	$3.133$	$6.845$
Tram inequality—Gini	11,185	$0.298$	$0.164$	$0.133$	$0.256$	$0.410$
Bus usage (%)	11,185	$0.919$	$1.628$	$0.000$	$0.000$	$1.523$
Bus accessibility	11,185	$5.250$	$6.354$	$2.740$	$4.410$	$6.177$
Bus inequality—90/10	11,185	$6.365$	$8.568$	$3.625$	$4.509$	$5.647$
Bus inequality—Gini	11,185	$0.365$	$0.068$	$0.332$	$0.364$	$0.396$
Distance to CBD (km)	11,185	$22.906$	$14.465$	$11.894$	$20.213$	$31.073$
Median age	11,185	$37.758$	$7.020$	$34.109$	$37.494$	$40.908$
Gender ratio (male to female)	11,185	$0.986$	$0.277$	$0.867$	$0.971$	$1.083$
Household income (*1000 AUD per week)	11,185	$0.990$	$0.218$	$0.861$	$1.010$	$1.140$
Home ownership (%)	11,185	$0.625$	$0.188$	$0.515$	$0.655$	$0.761$
Tertiary educated (%)	11,185	$0.304$	$0.157$	$0.184$	$0.275$	$0.402$

summarizes the usage rates of three main PT modes and other included variables. Across the 11,185 regions (SA1s) included in this study, the mean train usage rate is around 2.6%, reflecting the proportion of employed individuals who opt for train transportation for their work commutes. The variability in train usage is evident with a standard deviation of 3, indicating the range of responses across different regions. Similar patterns can be observed for tram and bus usage rates, where the mean for both is around 0.9%, suggesting lower overall utilization for trams and buses than trains.

Public transport accessibility, a fundamental determinant of usage, is gauged by the inverse distance to the nearest PT station. The mean accessibility values vary across modes, with trains showing the lowest mean (0.9) or the worst accessibility and buses exhibiting the highest mean (5.2) or the best accessibility. These statistics highlight the disparities in access to different modes of PT and their implications for utilization patterns.

Additionally, this study addresses the crucial aspect of PT inequality, measured through the 90/10 ratio and the Gini index. The average train inequality (7.638) implies variations in access and utilization across Local Government Areas (LGAs). The Gini index for train inequality averages around 0.417, indicating the extent of inequality within train usage distribution. Similar trends are evident for trams and buses, where inequality metrics provide insights into the extent of disparities in PT utilization within the metropolitan area. To better understand this variability across the metropolitan, the main study variables, including PT utilization and PT inequality, are presented on a map in Figure 2.

Considering other confounding factors, the average distance to CBD in the metropolitan area is around 23 km. We are studying a working population with a median age of around 38 years, with fewer males than females, a ratio of 0.986, a household income of around AUD 1000 per week, home ownership of 62%, and a tertiary educated ratio of 30%.

These summary statistics indicate the complex nature of urban mobility infrastructure and the need for a comprehensive understanding of the factors that drive PT usage. The variables reflect a mix of access, equity, socioeconomic factors, and latent urban characteristics, offering a holistic perspective for analyzing and addressing the complexities of PT behavior in the Melbourne Metropolitan Area.

4.2. Modeling

The regression results presented in

Table 2. Regression results: modeling PT usage.

Inequality Mode:	90/10				Gini
PT Mode:	Train	Tram	Bus		Train	Tram	Bus
Intercept	9.08 ***	3.77 ***	4.62 ***		9.61 ***	3.98 ***	5.35 ***
	(0.23)	(0.16)	(0.14)		(0.24)	(0.16)	(0.15)
PT accessibility	1.00 ***	0.50 ***	0.02 ***		1.00 ***	0.50 ***	0.02 ***
	(0.02)	(0.01)	(0.00)		(0.02)	(0.01)	(0.00)
Distance to CBD	−0.03 ***	−0.02 ***	−0.01 ***		−0.03 ***	−0.02 ***	−0.01 ***
	(0.00)	(0.00)	(0.00)		(0.00)	(0.00)	(0.00)
PT inequality	−0.03 ***	−0.02 ***	−0.01 ***		−1.19 ***	−0.91 ***	−2.41 ***
	(0.01)	(0.00)	(0.00)		(0.29)	(0.13)	(0.22)
Median age	−0.09 ***	−0.03 ***	−0.03 ***		−0.09 ***	−0.03 ***	−0.03 ***
	(0.00)	(0.00)	(0.00)		(0.00)	(0.00)	(0.00)
Gender ratio	0.05	0.05	0.18 ***		0.05	0.05	0.18 ***
	(0.08)	(0.06)	(0.05)		(0.08)	(0.06)	(0.05)
Tertiary educated	−1.55 ***	1.11 ***	−0.60 ***		−1.69 ***	1.10 ***	−0.57 ***
	(0.22)	(0.15)	(0.13)		(0.22)	(0.15)	(0.13)
Household income	−0.87 ***	−0.41 ***	−1.20 ***		−0.88 ***	−0.46 ***	−1.21 ***
	(0.16)	(0.11)	(0.10)		(0.16)	(0.11)	(0.10)
Home ownership	−3.12 ***	−2.81 ***	−1.67 ***		−3.11 ***	−2.73 ***	−1.66 ***
	(0.19)	(0.13)	(0.11)		(0.19)	(0.13)	(0.11)
R2	0.34	0.57	0.14		0.34	0.57	0.15
Adjusted R2	0.34	0.57	0.14		0.34	0.57	0.15
Number of observations	11,185	11,185	11,185		11,185	11,185	11,185

Notes: *** denote statistical significance at the 1% level. The plausible results of multicollinearity test on our data are presented in the online Appendix A, Table A1.

provide some insights into the complex relationship between various factors and PT usage, considering different inequality measures (90/10 and Gini) and PT modes (train, tram, and bus).

PT accessibility emerges as a highly significant positive predictor across all modes and inequality measures ($p<0.001$). This finding states the importance of accessibility in driving higher PT usage rates. The coefficient value of $1.0$ for train indicates that a one-unit increase in PT accessibility corresponds to an approximately one-unit increase in usage rate. This implies that improved accessibility to PT stations significantly encourages more people to choose public transportation. Putting this into practice, doubling the train accessibility index improves train utilization by around 35%. The same coefficient for the bus is $0.02$, implying that, although statistically significant, there is a negligible impact of improving bus accessibility on its utilization. This difference could be attributed to the routes that each transport mode services and the concentration of train services around employment centers compared to bus services around suburban residential areas.

Distance to CBD, as expected, exhibits a significant negative relationship with PT usage across all modes and inequality measures. This implies that as the distance to the central business district increases, PT usage for commuting purposes declines. The negative coefficients, between $-0.03$ and $-0.01$ for different modes, highlight the role of proximity to employment centers in influencing transportation choices [27].

PT inequality showcases varying impacts depending on the PT mode and inequality measure. Interestingly, in the 90/10 inequality measure, the coefficient values are negative and highly significant for all modes. This suggests that higher levels of PT inequality negatively influence usage rates. Focusing on the train, a one-standard-deviation improvement (decrease) in the inequality measure increases the train utilization by around 0.16%. The same value is around 0.1% and 0.09% for trams and buses, respectively. Similarly, in the Gini inequality measure, the coefficients remain negative and significant for all the transport modes of train, tram, and bus. Gauging the extent of this impact in practice yields values similar to the former inequality measure.

Furthermore, socio-demographic variables show an impact on PT usage. Similar to [28] and others’ findings, median age displays a negative relationship across all modes and inequality measures, indicating that older populations tend to use PT less. The gender ratio positively impacts only the bus mode, suggesting that a higher proportion of males positively influences bus usage. We could not find any impact of gender on train and tram utilization.

Household income and home ownership variables demonstrate consistent impacts across different modes and inequality measures. Higher household income levels are associated with reduced PT usage [29]; similarly, higher home ownership rates lead to decreased usage rates. However, the results for tertiary education are not deterministic. While we found a negative impact from this on train and bus usage, the coefficient is positive for trams. The observed pattern is similar across both inequality measures.

The regression results unveil the intricate web of factors shaping PT usage rates in the Melbourne Metropolitan Area. The impact of accessibility, inequality, distance to CBD, and socio-demographic characteristics varies but is mostly similar across PT modes and inequality measures. The findings shed light on the multifaceted nature of PT utilization patterns and provide insights into how to plan for targeted interventions to encourage PT usage and address disparities.

5. Discussion

The findings of this study offer a refined understanding of the relationship between various factors and PT usage within the dynamic context of the Melbourne Metropolitan Area. These insights hold significant implications for policymakers crafting urban planning strategies that foster equitable transportation systems.

Our analysis considers different modes of public transport—trains, trams, and buses—while also incorporating two measures of inequality, the 90/10 and Gini indices. Accessibility, as presented by our research, emerges as a pivotal driver of PT usage rates. This highlights the importance of prioritizing investments aimed at enhancing transit access. Expanding the PT network, bolstering connectivity between modes, and optimizing the overall convenience of transit options can stimulate increased usage [2]. Such initiatives align with the objectives of sustainable urban planning by offering residents viable alternatives to private vehicle usage, reducing traffic congestion, and mitigating environmental impacts [1].

In addition to improving accessibility, addressing distance decay effects is paramount in crafting sustainable urban transportation policies. The negative relationship between distance to the central business district (CBD) and PT usage indicates the need to reduce commuting distances [10]. Transit-oriented development (TOD) emerges as a valuable strategy, promoting mixed-use neighborhoods with residential and employment centers near PT hubs [30,31]. Policies incentivizing developers to build near transit stations can contribute to this urban transformation, aligning with sustainability goals by reducing reliance on private automobiles and promoting walkability [32].

Our study also confirms the findings of [33,34,35] and many others that socio-demographic variables significantly influence PT usage patterns. Median age emerges as an influencing factor, showing a consistent negative impact across all modes and inequality measures [28]. This suggests that older populations use PT less frequently. Policymakers should consider tailoring PT services and make efforts to meet older commuters’ specific needs and preferences, potentially by offering senior-friendly services, accessible infrastructure, and travel education programs [36].

Furthermore, the gender ratio positively impacts bus usage, highlighting that a higher proportion of females increases bus utilization rates. This observation underscores the significance of gender-inclusive planning and safety measures within the PT network [2]. Policymakers should prioritize initiatives that enhance the safety and comfort of female passengers, including well-lit stations, gender-sensitive design, and security measures [37,38].

Socioeconomic variables such as tertiary education, household income, and home ownership consistently exhibit negative impacts across modes and inequality measures, except for tertiary education and trams. Policymakers should consider these findings when designing PT services, considering measures such as service quality improvements to serve diverse socioeconomic backgrounds within the community.

As is outlined, this research focuses on PT inequality and its impact on usage rates. Measured through the 90/10 and Gini indices, PT inequality has pronounced implications for usage patterns. The inequality measures yield negative impacts across all modes, emphasizing the detrimental influence of higher inequality levels on PT usage. This important finding is justified by myriad studies highlighting the impacts of PT equality on socioeconomic equity and overall well-being [18,39,40]. Distributing PT services equitably results in more accessible and convenient transit options for all community members, regardless of socioeconomic status. This accessibility, in turn, translates into increased opportunities for individuals to access education and employment [41,42]. Improved access to education and employment reduces socioeconomic inequalities and makes PT an attractive mobility mode for everyone. It also stops being just a means of mobility and becomes a powerful tool for social inclusion and upward mobility [43,44,45]. This relation could also be related to societal well-being and life satisfaction. Prioritizing PT equity in policies and planning strategies could reduce inequalities in access to opportunities. It helps create more inclusive societies where public transportation helps provide social cohesion [9]. This is also in line with numerous studies consistently showing that societies with greater equality experience higher overall well-being and life satisfaction [46,47,48,49]. In egalitarian societies, where access to education and job opportunities are more equally distributed, individuals from diverse socioeconomic backgrounds can pursue their goals and aspirations. In these societies, public transportation is a factor in equal access to opportunities and a catalyst for improving the quality of life for all citizens.

The evidenced synergistic relationship between PT equity and utilization has significant implications for both theoretical discussions and practical interventions in transportation planning. Firstly, this reinforces the transformative potential of equitable urban transportation policies by demonstrating that integrating equity considerations during PT project planning stages yields tangible benefits. Recognizing the link between equity and utilization compels policymakers and urban planners to adopt a holistic approach that prioritizes the diverse needs of stakeholders [50]. This necessitates a paradigm shift from conventional, top-down planning towards inclusive, participatory frameworks that actively engage underserved communities in decision-making processes. Gauging the extent to which more equitable PT plans could contribute to PT utilization directly and decrease other socioeconomic inequalities indirectly could help urban planners and policymakers to effectively plan and justify more equitable PT projects. This paves the way for the development of more inclusive and sustainable urban transportation systems.

Furthermore, this strengthens the case for proactive policy action to address existing disparities in access to PT. Underserved communities have historically faced the brunt of these inequities, experiencing limited access to essential services, economic opportunities, and social amenities. By leveraging research on equity and PT utilization, policymakers can design and validate targeted interventions to mitigate these disparities and promote more equitable access for all. These interventions may encompass optimizing route designs, rerouting existing services, and infrastructure investments—all aimed at improving the accessibility and reliability of PT services for underserved communities.

These findings not only emphasize the importance of addressing PT inequality in urban planning and transportation policies but also could be used to support and justify more equitable PT projects. PT inequality hinders disadvantaged communities’ access to education, employment, and essential services and exacerbates urban mobility challenges, including traffic congestion and air pollution [15]. Accordingly, targeted investments in overlooked areas and efforts to improve service frequency in areas impacted by transportation inequities are opportunities for action. Furthermore, policymakers should prioritize mode-specific strategies that address the unique challenges facing tram and bus services, including route optimization and possibly fare restructuring and community engagement efforts to bridge accessibility gaps [51,52].

This study provides new insights into the interrelated factors influencing PT usage within the Melbourne Metropolitan Area. Policymakers can leverage these findings to develop sustainable urban planning strategies that prioritize accessibility, address distance decay effects, reduce inequality, and address citizens’ diverse needs. This then helps in creating more inclusive, efficient, and sustainable transportation systems that benefit residents and the environment. Finally, it is advised to utilize data analytics and advanced modeling techniques to continuously monitor and assess PT usage patterns, allowing policymakers to adapt their strategies to changing commuter behaviors and preferences [1].

Finally, we acknowledge that this study has not been without limitations. We recognize that our model may be susceptible to omitted variable bias due to factors not included in our analysis. Future research could explore additional variables or employ different methodologies to further address this issue. This includes using instrumental variables which requires identifying valid instruments that satisfy the necessary conditions, such as relevance and exogeneity. In future research, exploring the applicability of instrumental variables or other advanced econometric techniques could further enhance the ability to handle omitted variable bias effectively, thereby strengthening the robustness of the findings.

6. Conclusions

This study investigated the factors influencing PT usage within the dynamic urban landscape of the Melbourne Metropolitan, focusing on the relationship between PT inequality and its utilization. Our study aimed to unpack the drivers of PT usage, including accessibility, distance decay, inequality, and socio-demographic characteristics. It provides significant implications for policymakers and urban planners for creating sustainable, efficient, and equitable transportation systems.

In our analysis, we examined three major PT modes—trains, trams, and buses—and employed two measures of inequality, the 90/10 and Gini indices. The findings confirmed the pivotal role of accessibility in encouraging PT usage. Improved access to PT stations emerged as a highly significant positive predictor. Furthermore, our research emphasized the need to address distance decay effects. As the distance to the central business district (CBD) increased, PT usage declined significantly. Socio-demographic factors also played a significant role. Older populations tend to use PT less, underlining the need for age-sensitive transportation solutions. Gender ratios influenced bus usage, emphasizing the importance of gender-inclusive planning and safety measures [2].

As a primary focus of this study, PT inequality, whether measured by the 90/10 or Gini indices, showed a significant impact on PT usage patterns. Higher levels of inequality were associated with lower PT usage rates. In light of these findings, we offer policy recommendations that align with the requirements of sustainable urban planning. These include using the main findings of this research, the impacts of PT inequality on its utilization, to plan for and justify more equitable PT projects. Moreover, the findings serve as a roadmap for urban planners and policymakers, guiding them in developing intervention plans aimed at enhancing existing PT infrastructure to ensure more equitable services. By understanding and addressing the different ways that PT inequality affects its utilization, urban planners and policymakers can create more sustainable, equitable, and efficient transportation systems that meet the needs of all people while also supporting broader goals of urban sustainability.

Additionally, we acknowledge that this study has not been without limitations. We recognize that our model may be susceptible to omitted variable bias due to factors not included in our analysis. Future research could explore additional variables or employ different methodologies to further address this issue. This includes using instrumental variables, which requires identifying valid instruments that satisfy the necessary conditions, such as relevance and exogeneity. In future research, exploring the applicability of instrumental variables or other advanced econometric techniques could further enhance the ability to handle omitted variable bias effectively, thereby strengthening the robustness of the findings.

Finally, the findings offer a new roadmap for future research in the field of public transport (PT) inequalities. Further studies aimed at quantifying the financial and economic impacts of PT inequality could further justify the need for more equitable PT projects. Moreover, understanding the indirect mechanisms through which PT inequality may affect the utilization of other urban infrastructure, beyond its direct accessibility impact, is of paramount importance.