Accessibility is the ease of reaching opportunities that have become a critical indicator and common goal in many transportation plans. Improving job accessibility can increase the probability of reducing unemployment and commutes of private vehicles. Therefore, this study aims to measure the job accessibility of Ohio's four largest cities (Columbus, Cleveland, Cincinnati, and Toledo) within 30- and 45-min time thresholds and identifies the job accessibility condition among the vulnerable census tracts as they are the deprival groups who need good access to avail their jobs. Therefore, it also evaluates the vertical equity among the four cities. The results found that public transportation provides uneven distribution of job accessibility among four cities. All four cities can only meet around 27% of the population's job accessibility demand using public transport. If the travel time increases, job accessibility conditions improve using public transportation. However, most census tracts with high employment accessibility are located near the downtown areas of the four cities. Though Columbus is the largest city in Ohio, 47% of the population does not get good job accessibility using public transport and also cannot meet the demand of vulnerable census tracts. Furthermore, the equity gap is also high for Columbus. The study found inequality among the cities, and most of the city's Gini values are close to 1. It indicates that access to services (jobs) using public transport is not distributed properly, and disadvantaged people are not benefited. The study suggests that the improvement of public transit facilities is essential in the cities. Hence, the transportation authorities and other relevant organizations should focus on providing an equitable distribution of public transit facilities for availing services.
Research Article
Assessing Job Accessibility and Equity by Public Transport in the Four Largest Cities of Ohio
https://doi.org/10.21203/rs.3.rs-3803404/v1
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Job Accessibility
Gini Index
Lorenz curve
Vulnerable census tract
Public transport
In recent decades, accessibility has emerged as a crucial metric for evaluating transportation plans (Bocarejo & Oviedo, 2012; Fan, Guthrie, & Levinson, 2011). It refers to the convenience of socioeconomic activities/services/opportunities (jobs, education, health care, etc.) available to all socioeconomic classes. It is the indicator of efficacy and equity of transportation and land use planning. This accessibility measure is widely accepted as 'the potential of opportunities for interaction (Hansen, 1959; Hu, 2017). Therefore, integrating land use planning with transportation has been widely accepted worldwide as it helps achieve sustainable development to ensure a healthy environment and a prosperous economy (Halden, 2002). The transportation sector aims to provide access to jobs, shops, groceries, recreational centers, and many other activities (Karner, 2016). The ease of access to this service depends on the transportation system and land use patterns (Boisjoly and El-Geneidy, 2017). Therefore, accessibility measures help to define the degree of options available to anyone, and people choose their travel options based on the existence and available transport options (Boisjoly and El-Geneidy, 2017). Due to the fact that transportation is a derived demand, accessibility considers both the availability of transportation and the available opportunities (Halden, 2002). In order to put integrated land use and transport planning into action, this metric is crucial, as stated by Yigitcanlar et al. (2010). Despite its critical function in policymaking, it is often misunderstood, inadequately defined, and underqualified (Geurs and van Wee, 2004).
Accessibility has various measurements and application methods based on multiple issues and spatial dimensions. In urban geography, planning, and transportation studies, accessibility to employment has become an established measure (Cheng and Bertolini, 2013). The concept under consideration pertains to the quantification of job opportunities accessible to an individual under a predefined temporal constraint, sometimes referred to as cumulative opportunity measures (Foth et al., 2013). This measure can compare different socioeconomic groups, identify the unserved and well-served groups, and predict the people who will benefit from new transport (Foth et al., 2013; Boisjoly and El-Geneidy, 2017). The spatial distribution of opportunities throughout the cities has a significant role in the recurrence of access discrepancies between various transport users in different places (Karner, 2016). Urban populations must have access to dependable transportation systems, including public transit, to take advantage of significant social and economic opportunities. Public transit is essential for underserved communities, low-income households, and unemployed people to access different services (Mihyeon & Amekudzi, 2005). If the transportation decision is not clearly defined, public transport cannot serve these disadvantaged people (Karner, 2016). It will lead to social exclusion, so transport and land use policies focus on accessibility by enabling people to reach their destination at reasonable costs and times (Hawas, Hassan, and Abulibdeh, 2016). Therefore, improving and providing public transport has become one of the main goals of policymakers and planners in metropolitan areas (Saghapour et al., 2016).
In the United States, 13.4% of people in Ohio live below the poverty line, which is higher (11.6%) than the national poverty line. According to the American Community Survey (2021), the median income is around $61,938, which is lower ($70,784) than the national income (Semega and Kollar, 2022; U.S. Census Bureau, 2022). After the pandemic, employment opportunities have increased in the state. It is also ranked 38th in the nation's state employment-to-population ratio (Lee, 2022). According to the Ohio Public Transit report, about 80% of residents can access public transit. The report also described that it does not mean it provides high service, quality, and frequency. It only provides demand response services for 30% of the population (Ohio Department of Health, 2019). Therefore, it creates a demand to measure the job accessibility of Ohio's cities considering public transport. Though many job accessibility-related papers have been published, they mainly focus on the specific cities of the state. This paper aims to consider multiple cities (the four largest cities) of Ohio to evaluate their job accessibility condition using public transport. It also evaluates the vertical equity to identify the service gap among four cities.
This study contributes to assessing the accessibility situation through public transport for the four largest cities of Ohio. It provides evidence of the current situation of accessibility for the people of Ohio and the situation for the disadvantaged people who are using public transport to access jobs using public transport. A comparative understanding of accessibility and equity is evaluated among the four cities in the study, which has not been analyzed before, which helps to compare the situation and gives a clear view of the accessibility situation through public transport.
There are five sections in this study. An overview of the literature on concerns of equity and accessibility in transportation is given in the first section. The data and process used to create and evaluate the accessibility measures with respect to vertical equity are then described. For the equity and accessibility measures, the results are spatially evaluated. Lastly, a discussion of the findings is followed by the presentation of the study's conclusions.
2.1 The Concept of Accessibility
Hansen (1959) proposed the concept of accessibility as "the potential of interaction," while Weibull (1980) defined it as "a property of the configuration of opportunities for spatial interaction." A precise definition has been proposed by Geurs and van Wee (2004). According to them, it is the extent to which land use and transport systems enable individuals to reach activities or destinations utilizing transport modes. It is the ease of reaching destinations or activities called opportunity (Litman, 2002; El-Geneidy and Levinson, 2006). According to Ross (2000), "The ease of reaching some destination may include real or perceived costs in terms of time or money, distance traveled, level of comfort, availability, and reliability of public transport or any combination of these."
Accessibility is not only considered mobility (physical movement) but also considers other factors that affect the access to opportunities, location of the opportunities, patterns of land use, the quality and affordability of transport options, and people's ability to use those transport options (Levinson, 2020). It has other meanings in different fields, such as in geography and urban economics; it refers to the relative ease of reaching a particular location or area. In pedestrian planning and facility design, accessible design (also called universal design) refers to facilities designed to accommodate people with disabilities (e.g., a pathway designed to accommodate people in wheelchairs may be called accessible). In the social planning field, it refers to people's ability to use services and opportunities. So, it can be said that accessibility is the ultimate goal of most transport, which generally refers to physical access to goods, services, and destinations (Ahsan, 2013).
2.2 The Concept of Job Accessibility
Job accessibility is the "ease of reaching workplaces" or the "potential of job opportunities for interaction" (Hansen, 1959). It comprises three sub-systems: transport, worker's residence, and jobs, further categorized into spatial and non-spatial distribution. The spatial distribution ensures a connection between the worker's residence and job location. Besides, the non-spatial distribution contributes to the variation of mobility provision through transport sub-systems (service schedules, traffic management, and planning policies) (Cheng and Bertolini, 2013).
2.3 Accessibility Measurement
Accessibility comprises four distinct components: land use, transportation, time, and the individual (Geurs and van Wee, 2004). Therefore, accessibility measurement should be sensitive to changes in either or both components. It is commonly divided into place-based and person-based accessibility measures. Place-based accessibility measures at a specific location in space often include only land use and transportation elements. Besides, person-based measures focus on individuals incorporating time budgets and socioeconomic data (Geurs and van Wee, 2004). This study is considered a place-based accessibility measure.
Place-based measures are divided into three categories: Cumulative opportunity measure, gravity-based measure, and Utility-based measure (Deboosere and El-Geneidy, 2008). The cumulative opportunity measure quantifies the number of reachable destinations from a designated spatial place within a predetermined time frame, employing a specific transportation mode. (Ingram, 1971; Morris et al., 1979). It quantifies the number of opportunities using a particular mode of transport considering a specific time (often 30 or 45 minutes). Numerous extant research examines the concept of job prospects, whereas many other chances encompass various domains such as healthcare, clinics, educational institutions, retail establishments, and more (Geurs and van Wee, 2004). The metric under consideration considers the principle of equal opportunity while disregarding values beyond a certain threshold and does not include passengers' subjective impression of time (Ben-Akiva and Lerman, 1979). It can identify the most significant accessibility areas and ascertain equity disparities across different groups (Tomer, Kneebone, Puentes, & Berube, 2011; Ross & Svajlenka, 2012; Guthrie, Fan, & Das, 2016).
Gravity-based measures also consider the distance between the destination or opportunities. If the opportunity is a larger distance from the origin, it will be considered as less accessible. This measure does not consider a certain time threshold. The statement adheres to Tobler's first law of geography, which posits that the opportunities in proximity to the point of origin hold greater value (Hansen, 1959; Owen and Levinson, 2014). The gravity measure dampens opportunities as time or distance from the origin increases. It is used to measure job accessibility by a specific mode of transit from a particular place (Levinson, 1998; Di Paolo, Matas, and Raymond, 2014). It can also combine with a multinomial logit model to decide mode selection and accessibility individually (Cervero, Sandoval & Landis, 2002). However, it has some criticism, including generating meaningless values and overestimating the accessibility score.
Finally, the Utility-based accessibility measure assigns a given utility to each destination and calculates the log sum of all destinations within a probable option set. Thus, utility-based accessibility can be estimated using the multinomial logit model as the denominator (Handy and Niemeier, 1997). It emphasizes the benefits that individuals derive access to spatially dispersed activities.
2.4 Consideration of Equity in Transport Literature
According to Litman (2017), equity (justice or fairness) is referred to as the appropriate and fair distribution of impacts (benefits and costs) among all. Equity has two main approaches: horizontal equity and vertical equity (Litman, 2002). Horizontal equity is known as the equal distribution of benefits among all social classes. Vertical equity in public transportation requires the distribution of benefits according to the needs of each social class for those services (Murray and Davis 2001).
According to Kahn et al. (2008), low-income families tend to congregate in areas close to central business districts because of the convenience of public transportation. According to research conducted by Ong and Miller (2005), the "transport mismatch theory" posits that there is a disconnect between the supply and demand for citizens to use various forms of transport to get to and from work. The social significance of a product was used as a measure of fairness in its distribution by Martins et al. (2012). People's interpretations of the social significance of public transport systems might vary widely. For captive users (those who rely on a single motorized mode to complete their intra-urban trips due to a lack of car ownership or access or age-related travel conditions), public transportation is a necessity, while for choice users (those who rely on at least two motorized modes to complete their intra-urban trips and can choose a transit or paratransit mode), it is merely an option.
Bertolaccini (2013) conducted her research in eight metropolitan cities in the USA to check whether the distribution of transit services is equitable across these cities. The researchers also check how the equity level changes with the spatial dimension change. In that case, the level of equity is measured for both the Metropolitan Statistical Area (MSA) and the Transit Service Area (TSA) using the general transit feed specification (GTFS) database. The supply index was derived using the frequency intensity method, and the demand index was calculated considering the total population of MSA and TSA. Finally, the disparity between the supply and demand index was measured, and the Gini-coefficient value was generated.
2.5 Relationship between Accessibility and Equity
In order to perform better urban transportation research, it is crucial first to understand the relevance of transportation planning and how it affects accessibility. The subsequent phase may involve determining the level of accessibility for various places and socioeconomic categories (Lucas et al., 2016). Traditional transportation and land-use strategies frequently overlook the influence of distributional implications on the travel capacity of urban populations with various social identities, economic levels, and skill and aptitude levels. This is a critical matter. One of the main aims of transportation policy is to provide equitable mobility for all city and metropolitan residents, especially the poor and socially disadvantaged. Therefore, accessibility equity analysis is essential. However, to inform land-use regulations and policies about how they could affect different populations' access to and utilization of social and economic opportunities, it is critical to understand the distribution of accessibility from both a transportation and an effect of land-use on travel behavior standpoint (van Wee and Handy, 2016). The connection between equity and the ease of using public facilities has been the subject of much debate (Grengs, 2015). Public facilities and services provided by nonprofits have a redistributive impact because they lessen the impact of the wage system on the most vulnerable individuals in society (Harvey, 2010).
Multiple instances highlight the significance of equity in the formulation and assessment of public policy in the global literature on the subject of accessibility and equity (Delbosc and Currie, 2011a; Lucas et al., 2016; van Wee and Handy, 2016; Wu and Hine, 2003). According to Lucas et al. (2016), traditional methods of evaluating policies, including cost-benefit or multi-criteria analysis, run the danger of overestimating the positive impacts on certain groups while ignoring others. A number of studies in the field of transportation have used scale-independent metrics like Lorenz curves and Gini indices to examine accessibility and vertical equity comprehensively (Delbosc and Currie, 2011a; Grengs, 2015; Karlström and Franklin, 2009; Lucas et al., 2016). Such indicators are supposedly easy for policymakers and decision-makers to understand and work with. Delbosc and Currie (2011a) investigated equity in Melbourne's transport system, whereas equity in Stockholm's congestion pricing scheme was investigated by Karlström and Franklin (2009). In addition, Wu and Hine (2003) and Grengs (2015) conducted calculations to determine accessibility for different social groups, with a particular focus on equity, even if Lorenz curves were not present.
2.6 Past Research on Accessibility and Equity
There is a lot of literature on the topic of transportation-related social exclusion. A major claim made by this research is that insufficient or nonexistent mobility options lead to social marginalization (Preston and Rajé, 2007). Lack of access to opportunities is both a cause and a consequence of social exclusion, according to Kain (1968), Kenyon et al. (2002), Manaugh and El-Geneidy (2012), Sanchez et al. (2004), and Wee and Geurs (2011), who emphasized the mobility and accessibility perspectives on the links between transportation disadvantages and social exclusions. Using the UK's Department of Transport's accessibility planning initiatives as a case study, Preston and Rajé (2007) investigated the reliability of accessibility as an indicator of social exclusion. There has been less effort to quantify accessibility for low-income people despite equity being a prominent theme in accessibility studies. Since there are frequently non-spatial hurdles to obtaining high-wage work, these accessibility indicators offer a more accurate picture of the accessibility situation for marginalized groups (Legrain et al., 2016). Legrain et al. (2016) note that most studies that measure accessibility to jobs do not specifically measure access to low-income jobs, meaning that they do not assess more valuable destinations for socially vulnerable populations. However, most studies do find that vulnerable groups experience higher accessibility levels overall (e.g., Foth et al., 2013).
It is surprising that measuring accessibility to vulnerable people has received so little focus, given the abundance of equity-focused accessibility studies. Because there are sometimes non-spatial barriers to attaining high-wage occupations, these measures better represent the state of access for disadvantaged groups (Legrain et al., 2016). Contrarily, most research that looks at job accessibility also reveals that vulnerable persons have a greater level of accessibility (Foth et al., 2013). Research on vulnerable groups of people's job access gets little attention in the USA, especially compared to the cities within a state. This study adds to the existing literature on job accessibility by comparing accessibility measures and examining the resulting equity of the interaction between transportation and land use in four major cities in Ohio. The aim is to offer a more comprehensive perspective on the fairness of public transportation services in Ohio.
3.1 Study Area Profile
This research takes into account the four most populous cities in Ohio. All four cities' median household income is below the Ohio median ($61,938). The highest average commute time is found in the city of Cincinnati. In addition, Columbus has the highest number of employed people in the city.
The table of demographic features is given below:
Table 1: Demographic profile of the study area
Source: US Census Bureau, 2023
3.2 Data Collection
Table 2: Source of required data
3.3 Job Accessibility Calculation Process
Accessibility-related literature gives a vast idea of measurement. This study selects the cumulative measure, considering both attractions (gains) and repulsion factors (travel cost). The cumulative opportunity measure generates an accessibility score that increases proportionately to gains and decreases proportionately to trip costs. The following formula has been used to determine the accessibility of the census tracts (Casas et al., 2009; Moniruzzaman and Páez, 2012; Deboosere, 2018; Ben-Elia and Benenson, 2019):
$${A}_{i}=\sum _{i}{O}_{j}f\left({t}_{ij}\right) and f\left({t}_{ij}\right)=\left\{\begin{array}{c}1 if {t}_{ij}\le {t}_{threshold}\\ 0 if {t}_{ij}>{t}_{threshold}\end{array}\right.$$
Where, Ai=Accessibility in census tract i
Oj=Number of jobs in census tract j
tij= Travel time between census tracts i and j
tthreshold= Average duration of a commute by public transport in the region
3.4 Determinants of Travel Time Threshold
After calculating the travel time, 30 and 45-minute interval threshold limits are selected for a more meaningful comparison of accessibility measures (Guers and Eck, 2001; Cerda, 2009; Albacete, Olaru, Paul & Biermann, 2015). The average commute time of the public transport users of the four largest cities of Ohio is between 37 and 45 minutes (See Table 1).
3.5 The Method of Generating Network Dataset
General Transit Feed Specification (GTFS)-formatted public transportation schedules for each city's transportation agencies have been collected. This GTFS data is imported into ArcGIS Pro along with street centreline data. In the centreline data, pedestrians are restricted for highways, freeways, and ramp roads. The public transit data model is then generated using GTFS data in order to transform it into the appropriate feature classes and tables for the network dataset. The computation of travel time for public transportation includes the time required for accessing the transportation, waiting for it, transferring between different modes of transportation, being inside the vehicle, and exiting the transportation.
In addition, the public transit data model is connected to streets to ensure the transit stops, transit lines, and streets are all well connected. This connection is essential for ensuring that pedestrians or transport use the street and transit system. Lastly, the network dataset is built using a network analysis tool.
3.6 Calculation of Origin-Destination (O.D.) Matrix
The O.D. matrix measures the network's least-cost path from multiple origins to multiple destinations. As the origin-destination point is required for calculating the O.D. matrix, the census tract's centroid points (latitude and longitude) are computed using ArcGIS Pro and appended to the map. Then, the O.D. matrix is generated using a network analysis tool. This study employs travel cut-off times of 30 minutes and 45 minutes during a typical Tuesday morning peak at 9 a.m. for a specific mode of transportation (public transportation). In this study, all census tracts serve as origin-destination points that are used to construct an O.D. matrix from a build network dataset and identify the accessible route within 30- and 45-minute time constraints by public transport. As the cut-off point is specified, the origin-destination point will count the locations of census tracts that satisfy the required time threshold. The network dataset defines the public transit line and its connecting route or stops, which is used to locate or identify opportunity points (census tracts). For example (Fig. 2), census tract i can count two census tracts (j and m) within 30 min time thresholds. As the transit stops and lines are generated, and the travel cut-off point is specified, the origin point will identify the destination point using the transit line and cut-off points. Thus, census tract i count two destination points within 30 min and four census tracts within 45 min thresholds.
Then, the output geometry is found in a straight-line layer that is exported to calculate the accessibility score. The job accessibility score is the sum of the destination point's number of jobs. Finally, the score file is added to ArcGIS Pro, and prepare the maps. The map of accessibility score is classified using the Natural Break Jenks classification method that groups the data based on natural groups in the data distribution. The entire process is conducted using ArcGIS Pro.
3.7 Vulnerable Census Tract
Public transportation is required for disadvantaged people such as low-income, disabled, unemployed, below-poverty people, etc. So, it is important to measure the vulnerable census tract job accessibility to observe their accessible condition. The needy people require more accessible public transport facilities for their jobs. Therefore, this study considers the poverty rate (≥ 13%), unemployment rate (≥ 5%), and median household income (≤$30,000) for identifying vulnerable census tracts (Liu & Kwan, 2020; Foth et al., 2013; Boisjoly et al., 2020; U.S. Census Bureau, 2022). The identification of vulnerable census tracts is performed using the ArcGIS Pro Querry tool.
3.8 Equity Calculation
This research calculates the Lorenz curve and Gini Coefficient value to identify the equity gap. Lorenz curve is the graphical representation of equality. This curve can be applied not just to income but to any quantity that can be cumulated across a population. This curve has been applied in various disciplines, from biodiversity studies to business modeling and transport. But, these curves do not imply that perfect equality is possible or desirable. For developing the curve, the parameters' cumulative percentage is plotted on the X-axis, and the cumulative percentage of accessibility indices is plotted on the Y-axis (Gini, 1912).
On the other hand, the Gini coefficient is a simple mathematical metric representing the overall degree of inequality. It is a ratio of the area between the line of equality and the Lorenz curve divided by the total area under the line of equality (Gini, 1912). In this way, the distribution of two different Lorenz curves can be mathematically compared. The more the Gini coefficient value means closer to 1, the more inequality is seen in the distribution (Gini, 1912).
For every city, three Lorenz curves would be generated:
-
Accessibility index vs. Median household income
-
Accessibility index vs. Poverty rate
-
Accessibility index vs. Unemployment rate
4.1 Accessibility conditions considering census tracts
The population of Columbus is the highest among the other cities. Typically, it has more employment and job opportunities. However, accessibility distribution is not enough to serve the people's opportunities (job). It is seen that cities 8% (30 min) and 19% (45 min) census tracts (see Table 3) get high job accessibility that serves only 6% (30 min) and 15% (45 min) population. The city does not provide accessibility for 45% of the population (see Table 7). Moreover, The center part of the city, basically near the downtown area, gets high job accessibility, and the distant census tracts, like the suburban, get low accessibility.
Table 3: Census tract wise job accessibility (%) of Columbus
|
Job Accessibility |
30 minutes |
45 minutes |
|---|---|---|
|
Low |
41% |
39% |
|
High |
8% |
19% |
Source: US Census Bureau, 2023; Author, 2023
Cleveland is the second largest city in Ohio. The map shows that some census tracts near downtown have fallen on low job accessibility (30 min) that is improved for 45 min travel time. The pattern of accessible census tracts is not clustered, which may be for an inefficient route facility. The city only provides access to jobs for 10% (30 min) and 19% of census tracts, which covers around 11% and 20% of the population of the census tracts (see Table 4 and Table 7).
Table 4: Census tract wise job accessibility (%) of Cleveland
|
Job Accessibility |
30 minutes |
45 minutes |
|---|---|---|
|
Low |
19% |
11% |
|
High |
10% |
19% |
Source: US Census Bureau, 2023; Author, 2023
Cincinnati has the highest average commute time of other cities. The city provides 15% high accessibility to jobs within 30-minute time thresholds; still, 32% of census tracts are under low accessibility using public transport (see Table 5). It improves their service facility within 45 min thresholds, providing job accessibility to 30% of census tracts. However, 10% of census tracts do not get proper accessibility (see Table 5). It is clear from Figure 5 that downtown and its surrounding area get most of the services. Additionally, job accessibility has improved for the city's southwest side within 45-minute time thresholds.
Table 5: Census tract wise job accessibility (%) of Cincinnati
|
Job Accessibility |
30 minutes |
45 minutes |
|---|---|---|
|
Low |
32% |
10% |
|
High |
15% |
30% |
Source: US Census Bureau, 2023; Author, 2023
Toledo has the lowest average commute time among other cities and a higher median income than Cleveland. As the commute time is lower, it should serve better than other cities. However, the city does not provide access to jobs around 28% and 25% of census tracts within 30 and 45-minute time thresholds. Table 6 provides the overview of accessibility that accessibility condition has not improved from 30 minutes to 45 minutes, and only 2% (low accessibility) of census tracts get job accessibility. High job accessibility covers only 12 to 19% of census tracts that serve around 10 to 17% of the population within 30 to 45-minute thresholds (see Table 6 and Table 7).
Table 6: Census tract wise job accessibility (%) of Toledo
|
Job Accessibility |
30 minutes |
45 minutes |
|---|---|---|
|
Low |
28% |
25% |
|
High |
12% |
19% |
Source: US Census Bureau, 2023; Author, 2023
Though accessibility conditions are not good compared to the four cities, Cincinnati has a better position than others. It has increased its services in the census tract by 15% (30 min to 45 min). Still, it provides higher levels of lower job accessibility (30 min) after Columbus (see Tables 3 and 5). It should be improved to make it equitable for all.
Table 7: Population coverage (%) through job accessibility using public transport
|
City |
Low job accessibility (30 minutes) |
High job accessibility (30 minutes) |
Low job accessibility (45 minutes) |
High job accessibility (45 minutes) |
|---|---|---|---|---|
|
Columbus |
47% |
6% |
45% |
15% |
|
Cleveland |
20% |
11% |
27% |
20% |
|
Cincinnati |
35% |
13% |
9% |
23% |
|
Toledo |
33% |
10% |
29% |
17% |
Source: US Census Bureau, 2023; Author, 2023
Table 8: Comparison based on high job accessibility
|
City |
High job accessibility (30 minutes) |
High job accessibility (45 minutes) |
|---|---|---|
|
Columbus |
8% |
19% |
|
Cleveland |
10% |
19% |
|
Cincinnati |
15% |
30% |
|
Toledo |
12% |
19% |
Source: US Census Bureau, 2023; Author, 2023
4.2 Vulnerable Census Tract Condition in Terms of Service Provision
Figure 7 shows that Cincinnati provides better service provision using public transport in the vulnerable census tracts, around 33% for 30-minute thresholds and 43% for 45-minute thresholds (see Fig. 8 and Fig. 9 for vulnerable census tract maps). Toledo has the worst scenario among the other cities, and it can only provide access below 30% of census tracts, aggregating 30 and 45-minute thresholds. Comparatively, Columbus, Cleveland, and Toledo can provide job access only for 20% of the census tract within 30 min. When the commute time is increased, Columbus and Cincinnati improve their facilities. However, the condition of Cleveland and Toledo has not improved significantly.
4.3 Equity condition in the provision of job accessibility using public transport
Table 9: Gini Coefficient values considering 30 minutes job accessibility
|
City |
Median Household Income |
Poverty Rate |
Unemployment Rate |
|---|---|---|---|
|
Columbus |
0.65 |
0.52 |
0.58 |
|
Cleveland |
0.41 |
0.41 |
0.41 |
|
Cincinnati |
0.54 |
0.54 |
0.53 |
|
Toledo |
0.41 |
0.49 |
0.52 |
| Source: U.S. Census Bureau, 2023; Author, 2023 | |||
Table 10: Gini Coefficient values considering 45 minutes of job accessibility
|
City |
Median Household Income |
Poverty Rate |
Unemployment Rate |
|---|---|---|---|
|
Columbus |
0.60 |
0.47 |
0.52 |
|
Cleveland |
0.37 |
0.38 |
0.39 |
|
Cincinnati |
0.48 |
0.49 |
0.48 |
|
Toledo |
0.42 |
0.47 |
0.52 |
The Gini coefficient value represents the overall degree of inequality. If the value is closer to 1, it means more inequality in the distribution. This study measures three Gini coefficient values considering three variables with job accessibility for each city. Table 9 and Table 10 represent the Gini values where the equity condition is unsatisfactory. In comparison to other cities, Columbus has a greater disparity between work accessibility and several socioeconomic indicators, such as median household income, poverty rate, and unemployment rate, provided that public transportation adequately facilitates job accessibility within a 30-minute timeframe.
If public transport provides job accessibility within the 45-minute threshold, the inequality changes are not insignificant as well. It can illustrate that the time threshold does not ensure better service provision through public transport to make jobs available. The census tracts with higher unemployment, poverty, and lower median household income can not get proper service provision using public transport to avail job opportunities.
Though the four cities' equity gap is close to 1, Cleveland's inequality is less than in other cities. Job accessibility distribution among the census tract level has not provided satisfactory service to disadvantaged people. However, its Gini value is close to 0, meaning it has inequality in distribution but is not high. Though Cincinnati provides better job accessibility compared to the four cities, it has a significant equity gap in the job accessibility distribution using public transportation. This study shows the Columbus and Cleveland Lorenz curve for understanding the distribution of the high equity (see Figures 10 and 11) and low equity (see Figures 12 and 13) gap in the Lorenz curve.
This study measures job accessibility as well as vertical equity using public transport among the four largest cities of Ohio. The accessibility score is generated by calculating the number of jobs that can be reached within 30 minutes and 45 minutes of travel using public transport from one census tract to another. The result shows that accessibility to jobs using public transportation is unevenly distributed among the four cities. Most accessible census tracts are concentrated in the core of the city area, while periphery areas exhibit a relatively low level of job accessibility using public transport. Columbus City's peripheral area is more deprived of job accessibility compared to the four cities. Even the job accessibility condition of Toledo city is not satisfactory compared to other cities. 20% of the census tract gets high job accessibility using public transport in Toledo, which covered only 17% of the population. The rest of the people are bound to use the automobile to access their job location. The most job-accessible places are located near the downtown area, which does not provide affordable housing to all. So, low-income people have to reside in suburban or peripheral regions. Therefore, improving public transportation accessibility from peripheral or suburban locations to other job locations is significant. Instead of inefficient job accessibility using public transport found among four cities, Cincinnati has provided better job access by public transport.
Furthermore, this study has also identified vulnerable census tracts that do not get proper job accessibility. It only provides high services, around 20 to 30% of vulnerable census tracts. The planning intervention should start from these tracts as they are the vulnerable ones that depend on affordable transport systems. Transit authorities should improve their transit service facility by improving the route system or adding other transit facilities to these vulnerable census tract people and ensuring more access to jobs. Less job accessibility by public transport facilities will cause an increase in the unemployment rate. If public transport can provide efficient services from any location, it will reduce the unemployment rate.
The study possesses certain limitations. Initially, the measurement of job accessibility was determined at the census tract level through the computation of travel times utilizing the centroid. Hence, it is possible that journey durations could be inaccurately assessed, either underestimating or overestimating them, especially in the case of expansive census tracts located primarily on the outskirts of the region. In the majority of instances, the duration of the journey has minimal impact on the computations of accessibility. The results of this study indicate the patterns of access observed in cities. In order to enhance peripheral accessibility, it is recommended that more investigations be undertaken with an increased spatial resolution. The study conducted does not include an analysis of the reliability of trip time. The GTFS data provides transit schedules that take into consideration the impact of traffic congestion, although it should be noted that daily traffic conditions may exhibit variability. Future measures should consider these distinctions. This study lacks the inclusion of additional pertinent factors and methodologies that could enhance its conciseness. This study employs a cumulative opportunity measure to quantify the number of employment opportunities falling within a 30-minute and 45-minute radius. However, the quantification of employment opportunities can be influenced by additional variables, like occupation alignment, occupation category, transportation timetables, etc. Residents who reside in close proximity to the transport center or within a reasonable walking distance but fail to align their schedules with the public transit system will be unable to avail themselves of employment opportunities. Future research should aim to expand the scope by incorporating more criteria and including a broader range of cities. These elements may include the vulnerability of the population, median income levels, educational attainment, and other relevant variables. Conducting these types of studies in diverse cities would be advantageous in order to ascertain whether distinct contextual factors contribute to disparate outcomes.
Author Contribution
Tabassum, N. J. developed the idea, performed the computations, verified the analytical method, investigated the data, wrote the manuscript, data analysis and supervised the findings and conclusion of this work.Alam, M. S. is working on finding the literature, data analysis and writing and editing the manuscript.
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No competing interests reported.