Next Article in Journal
Chance-Constrained Dispatching of Integrated Energy Systems Considering Source–Load Uncertainty and Photovoltaic Absorption
Previous Article in Journal
Digital Competence Development in Public Administration Higher Education
Previous Article in Special Issue
Entropy Model of Dynamic Bus Dispatching Based on the Prediction of Back-Station Time
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 16
10.3390/su151612453
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analyzing the Impact of Decreasing Out-of-Vehicle Time of Public Transportation Travel on Accessibility to Tertiary Hospitals

by
Zhenbao Wang
1,*,
Dong Liu
1,
Shihao Li
1,
Shuyue Liu
1,
Huiqing Li
1 and
Ning Chen
2
1
School of Architecture and Art, Hebei University of Engineering, Handan 056038, China
2
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(16), 12453; https://doi.org/10.3390/su151612453
Submission received: 7 July 2023 / Revised: 2 August 2023 / Accepted: 15 August 2023 / Published: 16 August 2023
(This article belongs to the Special Issue Achieving Sustainable Public Transportation System: Travel Behaviors, New Technologies and Their Equity, Accessibility and Planning Implications)
Browse Figures
Versions Notes

Abstract

:
Unequal distribution of healthcare resources can lead to many fundamental problems, including the accessibility and equity of care in different regions. Existing studies often focus on administrative divisions, street zones, and conducting analyses of healthcare accessibility, but there is a lack of research on healthcare accessibility analysis specifically targeting apartment complexes. Furthermore, there is insufficient consideration of the impact of out-of-vehicle time on healthcare accessibility through public transportation. Taking Beijing’s 5th Ring Road area as an example, we used multiple data sources to construct a framework for the accessibility of medical care in apartment complexes using public transportation. We assumed two scenarios of 1/2 and 1/3 reduction in out-of-vehicle time. We compared and analyzed the changes in accessibility and equity under the two scenarios to investigate the impact of out-of-vehicle time on accessibility and equity of medical care in apartment complexes. The results show that (1) reducing out-of-vehicle time does not guarantee increased accessibility to all apartment complexes. (2) Under both scenarios, the accessibility of most apartment complexes within the fourth and fifth rings increased, and the accessibility of most apartment complexes within the Daxing District increased; otherwise, the accessibility of most apartment complexes in other areas decreased, and the decrease in accessibility was more significant for the scenario with a 1/2 reduction in out-of-vehicle time than for the scenario with a 1/3 reduction in out-of-vehicle time. (3) In both scenarios, the Gini coefficients of residential accessibility were calculated separately for inter-ring and administrative divisions, and the equity of residential accessibility increased in each division; the equity of accessibility increased more with a 1/2 out-of-vehicle time reduction than with a 1/3 out-of-vehicle time reduction. The framework proposed in this paper allows us to analyze the impact of out-of-vehicle time of public transportation on accessibility to medical care for apartment complexes.

1. Introduction

Globally, achieving sustainable urban development [1,2] requires providing timely, high-quality, and non-discriminatory healthcare services for urban residents, and equitable access to healthcare services has been recognized as an issue [3].
Accessibility of medical facilities refers to the ease of access to healthcare services and facilities for residents of a given area. Although there are many access studies from a regional perspective [4,5], there are fewer calculations related to accessibility and equity from a neighborhood perspective. At the same time, recent research on accessibility has focused on uncovering regional differences in public facilities from a socioeconomic perspective. In turn, an efficient public transportation network can help improve access to public facilities and promote social equity [6], especially for cities with a high population density and limited development of public transportation.
The world has shown a positive attitude towards public transportation. In developed countries, “creating conditions for public transport to become a high-quality alternative to personal transport” has been proposed [7], while the development of public transport systems has been promoted in some countries [8]. Different policies have been introduced in developing countries for the development of public transport [9,10]. Among them, the Chinese government has developed strategies to ensure the sustainable operation of public transport systems [11]. Although domestic and international policies have been advocating the development of public transportation and encouraging the priority of public transit, there is still a lack of strategies to explore the current problems of public transportation system operation from the perspective of the actual process of public transportation operation. In other words, it is essential to focus on the impact of out-of-vehicle time on overall travel in public transportation.
As a megacity, Beijing currently has a public transportation share of 52.9% of motorized trips and a total rail line network size of 1172 km. However, the intensity of minibus trips has decreased by 2.7%. Furthermore, traffic problems still exist. Poor and inefficient public transport travel is still an essential factor affecting residents’ life and social functioning in many large cities. Additionally, how to promote the conversion of residents’ trips using private cars to trips using public transportation has become an important topic of existing research [12].
In the discussion about the operation process of buses [7,13,14,15,16,17,18,19,20,21,22,23], scholars have employed various methods such as adopting eight quantitative indicators [24], proposing the Transit Opportunity Index [25], and providing strategies for quantifying connectivity [26] to analyze the operation process of public transportation. Even in cases where the origin and destination of public transport are relatively dispersed [27], most of the related research has focused only on the increased complexity of travel processes as a barrier to the use of public transportation [28]. However, the transfer component, as an essential factor causing complexity and additional effort in bus travel [29], has been primarily discussed in existing research regarding its impact on attractiveness and satisfaction [30], without paying much attention to the effects of out-of-vehicle time during transfers on accessibility and equity.
Therefore, this study has two important objectives: (1) Consider the time cost of each stage of public transportation trips using multiple sources of data. We propose an analysis method that can analyze the impact of public transport out-of-vehicle time on accessibility from apartment complexes to tertiary hospitals. (2) Use the Gini coefficient and Lorenz curve to observe the changes in healthcare equity under different scenarios of reduced out-of-vehicle time proportions. The contributions of this study are: (1) constructing an analytical framework for the tertiary hospital accessibility in public transportation that reflects the impact of out-of-vehicle time; (2) using apartment complexes as the demand point scale for calculating tertiary hospital accessibility and equity; (3) analyzing the impact of changes in out-of-vehicle time on healthcare accessibility and equity in apartment complexes.

2. Literature Review

Factors affecting access and equity are a subject of interest to scholars in urban planning and transportation [2]. To our knowledge, no studies have explored the effect of out-of-vehicle time on accessibility and its equity. This literature review focuses on three aspects of research: out-of-vehicle time during travel by public transportation modes, the selection of the accessibility demand capacity scale for healthcare facilities, and the selection of the supply capacity and equity measures.
Existing studies have analyzed how to improve satisfaction with public transportation and explored relevant factors that influence satisfaction and preference for public transportation [31,32]. The out-of-vehicle time during travel by public transport mode is a crucial factor affecting the satisfaction of public transport users [33], and it is often more demanding than the in-vehicle time when users are making transfers [34]. Recently, more attention has been paid to how changes in out-of-vehicle time affect the psychology of transit users [33]. In previous studies, the impact of walk-to-stop time, wait time, and transfer time on accessibility has not been further explored, and the travel process has been viewed as a closed whole rather than a process that can be broken down into parts. To fill this knowledge gap, we simulated the process of public transport travel for medical care, pointed out the concept of out-of-vehicle time during public transport travel, and varied this time to observe the effect of out-of-vehicle time on the accessibility and equity of medical facilities in different scenarios.
As a critical indicator of equal distribution of public services and social inclusion, research related to accessibility is also evolving. Scholars have successively proposed many methods for calculating spatial accessibility. Hansen [35] introduced the concept of accessibility in 1959, a measure of potential physical accessibility [36], and as a result, the proportional method [37], the two-step floating catchment area method (2SFCA) [38], and the buffer zone method [39] have emerged as the most commonly used accessibility measures in recent years of research. These approaches attempt to formulate distance-dependent or time-dependent interactions between health services and population demand while representing competition among populations for limited resources. Therefore, the calculation of accessibility helps to identify underserved areas and suggests a rational allocation of health resources, contributing to further improving regional equity of access [40,41]. Among them, 2SFCA is widely used in calculating medical facilities due to its intuitive, simple operation and easy access to data [42]. Because of its neglect of the actual resistance, an extended form of 2SFCA is often required for calculations in subsequent studies. For example, Enhanced 2SFCA (E2SFCA) [43], Gravity 2SFCA (G2SFCA) [43], Kernel Density 2SFCA (KD2SFCA) [43], and Gaussian 2SFCA (Ga2SFCA) [43]. The difference between different extension forms mainly lies in the other attenuation trends of the distance attenuation function, e.g., the E2SFCA was designed to address the deficiency in the original form of the 2SFCA that did not differentiate between differences in accessibility within the search radius and the E2SFCA distance attenuation function for segmental jump-type attenuation; G2SFCA uses the distance decay function of the gravity model as the distance decay function within the 2SFCA search radius; KD2SFCA incorporates a distance decay function in the form of a kernel density function within the 2SFCA search radius, including continuous decay of Gravity 2SFCA and Kernel Density 2SFCA in power functional form for convex and concave forms, respectively; the Gaussian 2SFCA is an “S” type attenuation. The rate of reachability decay with distance is slower in the nearer and more distant phases and faster in the middle part [43]. Therefore, the Gaussian function is more realistic in modeling the distance decay effect than other decay functions [44]. It is also more often used in the calculation of accessibility to public facilities, especially healthcare facilities. In this study, Ga2SFCA will be used for the calculation of tertiary hospital accessibility.
Regarding the selection of demand points for healthcare facility accessibility, more access studies have been calculated from the regional and community scales (see Table 1). However, the accessibility calculation from this scale still has a significant error for different apartment complexes in the region. The choice of apartment complexes for the accessibility analysis in this study will provide a more detailed and accurate picture of the convenience and accessibility of public transportation to health care. Tertiary hospitals are hospitals that provide medical and health services across regions, provinces, cities, and nationwide and are medical and preventive technology centers with comprehensive medical, teaching, and research capabilities. Because tertiary hospitals treat patients with difficult and critical illnesses, teach and conduct research work [45], and the coverage area includes the whole city, research on accessibility to tertiary hospitals is particularly important. In the accessibility calculation, this study uses tertiary hospitals as supply points and apartment complexes as demand points for accessibility and equity calculations. Additionally, in this calculation process, as the available data contain two data sets on the number of beds in tertiary hospitals [46] and the number of medical and nursing staff, the two data sets have not been compared as supplied in previous studies. In the survey, accessibility was calculated using two types of data as the supply capacity in 2SFCA, and the accessibility was calculated by regressing the two accessibility results and observing the Lorenz curve to determine whether the number of beds or the number of medical and nursing staff was used as the supply capacity.
Social equity is considered one of the prominent issues related to social sustainability [47], as improving accessibility to healthcare facilities contributes to better social equity [48]. Many studies have evaluated the equity of public service facilities based on accessibility results [49,50]. Equity in healthcare facilities means that every resident has the same opportunity to access healthcare facilities [51,52]. There are three methods of calculating social equity. The first method assesses the distribution of healthcare resources through a holistic approach based on relevant indicators, including the concentration index method [53,54], the Gini coefficient, and the Lorenz curve. The second approach is to construct an indicator evaluation system, such as an accessibility-based evaluation indicator system [55]. The third one is the assessment of healthcare facilities through parity [56,57]. The Lorenz curve and Gini coefficient are commonly used in economics to describe wealth equity. As equity research continues to grow, it is found that the Lorenz curve and Gini coefficient analysis are equally applicable to the fairness measurement of urban public resource allocation. The study uses the first method, the Gini coefficient and the Lorenz curve, for access equity statistics.
Based on these limited considerations, this study aims to analyze the accessibility of tertiary hospitals as well as the equity of accessibility. Firstly, we use residential communities as the demand capacity, and by judging the equity of different supply capacities, we select one of the appropriate numbers of beds and the number of medical and nursing staff as the supply capacity to be used for subsequent calculations. Then, we split the whole process of using public transportation to travel to the medical chain, and with the help of the new data acquisition method, we obtain the latest time of the whole travel chain of public transportation. Finally, we obtain and analyze the specific impact of out-of-vehicle time on accessibility as well as equity by calculating the impact of out-of-vehicle time on accessibility in different contexts. The framework of this paper has important implications for cities experimenting with public transportation priority policies.
Table 1. Accessibility literature summary.
Table 1. Accessibility literature summary.
AuthorCalculation of AccessibilityThe Demand Point ScaleTravel ModeDemand CapacitySupply CapacityTime Threshold/Distance ThresholdThe Size of the Time ThresholdStudy AreaMain Conclusions
Xia, Y. T. et al. [3]Ga2SFCA and Weighted Average Travel TimeStreet zoneNot mentionedPopulationNumber of BedsTime Threshold30 minWuhan, ChinaTraffic conditions
change the accessibility by extending travel time and reducing the likelihood of obtaining healthcare services
during peak hours, especially for suburban residents.
Luo, W. et al. [58]E2SFCA1 km gridPrivate car mode/public transport modePopulationNumber of doctorsTime Threshold30 minIllinois, USAReveals more intuitive patterns of spatial accessibility and depicts clearer areas of shortage of spatial health professionals.
Gina, P. et al. [59]Ga2SFCAEvery census tract’s centroidWalking modePopulationNumber of vaccinationsDistance Threshold800 mSão Paulo, BrazilThe eastern part of the city has higher accessibility values compared to the peripheral and central areas.
Dai, D. J. et al. [60]Ga2SFCAZIP code tabulation areaPrivate car mode/public transport modePopulationNumber of doctorsTime Threshold30 minMichigan, USAThe results showed that living in areas with higher levels of black segregation and poorer access to mammograms was associated with a significantly increased risk of late breast cancer diagnosis.
Kang, J. Y. et al. [46]P-E2SFCAAggregated 5 km hexagon gridsPrivate car mode/public transport modePopulationNumber of BedsTime Threshold30 minIllinois, USAThis study identifies vulnerable populations living in areas of Chicago with low spatial accessibility.
Jumadi, J. et al. [61]2SFCACensus tract (administrative boundary)Not mentionedPopulationNumber of BedsTime Threshold30 minJakarta, IndonesiaThe accessibility shows a distance decay pattern, with higher accessibility in the central city and lower accessibility in the suburbs.
Liu, M. H. et al. [62]2SFCAaggregated 1 km hexagon gridspublic transport modetax value, number of elderly, number of immigrants, and mortalityNumber of BedsDistance Threshold1.5 kmShenyang, ChinaThe results of the study indicate that the metro has limited potential to solve the problem of poor access.
Khashoggi, B. F. et al. [4]2SFCAcensus tract (administrative boundary)Private car modePopulationNumber of medical and nursing staffTime Threshold30 minJeddah, Saudi ArabiaThe results of the study could help local health planners prioritize underserved areas when allocating future healthcare centers in Jeddah City, thereby improving spatial equity in access to healthcare centers
Naylor, K. B. et al. [63]Variable-distance E2SFCAZIP code tabulation areaPrivate car modePopulationNumber of doctorsTime Threshold60 minUSAThe Variable-distance Enhanced 2-step Floating Catchment Area method is a viable approach to measure spatial accessibility at the national scale
Kim, Y. et al. [64]SE2SFCApopulation gridPrivate car mode/public transport modePopulationNumber of doctorsTime Threshold120 minSeoul, KoreaThe proposed SE2SFCA method is realistic and effective in identifying weakly accessible areas.

3. Study Area and Data Sources

3.1. Study Area

As the capital city of China and its political and economic center, Beijing is an important city for public transportation research because of its high population density and the high traffic load it generates. The scope map of this study is within the 5th Ring Road of Beijing (Figure 1). This area includes seven administrative districts: Dongcheng District, Xicheng District, Fengtai District, Daxing District, Chaoyang District, Haidian District, and Shijingshan District. Among them, there are 9679 valid apartment complex data points and 59 tertiary hospital data points. Most of the residential communities and tertiary hospitals are concentrated within the 4th Ring Road, and the number of apartment complexes and tertiary hospitals in the city center is more densely populated than in the periphery of the city.

3.2. Data Sources

The detailed data of the Beijing neighborhoods utilized in the study were obtained from the Anjuke network platform (https://sjz.anjuke.com/?from=AJK_Web_City&from=AJK_Web_City accessed on 9 June 2022), with a total of 14,255 items and a total of 13,267 valid data after data cleaning. Data on the number of beds in urban tertiary hospitals and the number of medical and nursing staff were obtained from the publicly available hospital profile data under the official website of each hospital. The urban road traffic network data were obtained from OSM Open Street Map (https://www.openstreetmap.org/ accessed on 9 June 2022); the real-time data of public transportation travel (see Table 2) were obtained from the Gaode Map Open Platform (https://lbs.amap.com/ accessed on 9 June 2022).

4. Research Methodology

4.1. Accessibility and Equity of Tertiary Hospitals

4.1.1. Accessibility of Tertiary Hospitals

In the first step, for a tertiary hospital j within the 5th ring road of Beijing, the travel time threshold t 0 for public transportation during weekday morning peak hours is obtained. The Gaussian equation is used to assign weights to the population in each apartment complex i within the scope, and these weighted populations are summed to obtain the number of potential users for each tertiary hospital j . The bed supply is then used as a measure of tertiary hospital service capacity and divided by the number of all potential users to obtain the supply-to-demand ratio for tertiary hospitals. The supply–demand ratio of tertiary hospitals is calculated using Equation (1); the Gaussian equation weights are calculated in Equation (2).
R j = S j ∑ i ∈ { t i j ≤ t 0 } G ( t i j , t 0 ) P i ,
G ( t i j , t 0 ) = { e − ( 1 2 ) ( t i j t 0 ) 2 − e − ( 1 2 ) 1 − e − ( 1 2 ) , if   t i j ≤ t 0 0 , if   t i j ≤ t 0
where R j denotes the ratio of supply to demand in the scope of tertiary hospitals j ; S j indicates the number of beds in tertiary hospitals j ; i indicates a apartment complex within the scope of a tertiary hospital; P i refers to the number of people in the apartment complex i ; actual time t i j spent on public transportation during the morning rush hour from the apartment complex i to the tertiary hospital j ; t 0 denotes the public transport travel time consumption threshold; G ( t i j , t 0 ) denotes the weights obtained from the Gaussian equation calculation. The calculation is shown in Equation (2).
In the process of calculating the accessibility of tertiary hospitals, the time thresholds for different levels of hospitals often have different criteria, and the appropriate time thresholds for different levels of hospitals also vary from city to city. While in the study of hospital accessibility in Beijing, Rui [65] and Lu [66] concluded that the appropriate time threshold for accessibility of tertiary hospitals in Beijing is 5400 s. Travel times to all tertiary hospitals using apartment complexes are plotted in Figure 2: 5400 s corresponds to 97.59% of the cumulative travel frequency. Therefore, t 0 = 5400   s is chosen as the public transportation travel time threshold.
In the second step, a public transport travel time threshold t 0 is given for the apartment complex i during the weekday morning peak hours so that the scope of each apartment complex is formed. The supply–demand ratio R j of tertiary hospitals falling within the size of the cell is summed up using the Gaussian equation with weights to obtain the accessibility A i of tertiary hospitals j in the apartment complex. The magnitude of its value indicates the number of beds as the per capita access to tertiary hospital service capacity. The accessibility of tertiary hospitals in apartment complexes is calculated in Equation (3).
A i = ∑ i ∈ ( t i j ≤ t 0 ) G ( t i j , t 0 ) R j ,
where G ( t i j , t 0 ) denotes the weight obtained from the Gaussian equation calculation.

4.1.2. Equity in the Accessibility of Tertiary Hospitals

The Gini coefficient is used to measure the equity of the supply of accessibility to tertiary hospitals, and when the supply capacity is the number of beds, the Gini coefficient is calculated as Equation (4).
G 1 = 1 − ∑ i = 0 n − 1 ( P i + 1 − P i ) ( S i + 1 A 1 + S i A 1 ) ,
where G 1 is the Gini coefficient for calculating the accessibility of apartment complexes to public services using the number of beds as the supply; P i is the proportion of the cumulative number of apartment complexes from 1 to i ; P i + 1 is the proportion of the cumulative number of apartment complexes from 1 to i + 1 ; S i A 1 is the ratio of the cumulative accessibility value in terms of the number of beds as a supply from 1 to i apartment complexes to the total accessibility; S i + 1 A 1 is the ratio of the cumulative accessibility value in terms of the number of beds as a supply from 1 to i + 1 apartment complexes to the total accessibility.
When the supply capacity is the number of medical and nursing staff, the Gini coefficient of access to care is Equation (5).
G 2 = 1 − ∑ i = 0 n − 1 ( P i + 1 − P i ) ( S i + 1 A 2 + S i A 2 ) ,
where G 2 is the Gini coefficient of accessibility to public services using the number of medical and nursing staff as the supply apartment complex; S i A 2 is the ratio of the cumulative accessibility value of the number of medical and nursing staff as a supply from 1 to i settlements to the total accessibility; S i + 1 A 2 is the ratio of the cumulative accessibility value of the number of medical and nursing staff as a supply from 1 to i + 1 settlements to the total accessibility;

4.2. Reducing the Impact of Out-of-Vehicle Time for Transit Travel on the Accessibility of Tertiary Hospitals

4.2.1. Out-of-Vehicle Time for Public Transport Travel

The time composition of each part of the public transportation travel chain from home to the hospital generally includes from home to the bus stop, on the bus, the bus stop to the subway station, on the subway, the subway station to the bus stop, on the bus, and from the bus stop to the tertiary hospital to seven processes, as shown in Figure 3. The out-of-vehicle time from the apartment complex to the tertiary hospital consists of four components: walking and waiting time T 1 from the apartment complex to the bus stop, walking and transfer time T 2 from the bus stop to the subway station, walking and waiting time T 3 from the subway station to the bus stop, and walking time T 4 to the tertiary hospital. Out-of-vehicle time data are obtained with the help of open-source data provided by the Gaode Map open platform. The platform captures the entire process of public transportation travel, including walking from apartment complexes to bus stops, riding between bus stops, walking from bus stops to subway stops, riding between subway stops, walking from subway stops to bus stops, riding between bus stops, and walking from bus stops to tertiary hospitals. The impact on public transportation efficiency is observed by analyzing out-of-vehicle time and comparing the magnitude of the non-riding process with the total travel time during transit operation.
Out-of-vehicle time was counted, and the average out-of-vehicle time as a percentage of total travel time was calculated for all tertiary hospitals in the residential area (Figure 4). Most out-of-vehicle time in the 4th to 5th ring area is less than 25%, and the out-of-vehicle time within the 4th ring is higher. The average out-of-vehicle time is lower in the west and south of the study area between the 4th and 5th Ring Roads. Additionally, the result shows a phenomenon that the closer to the city center, the higher the average out-of-vehicle time share.

4.2.2. Rate of Change in Accessibility of Tertiary Hospitals

Assuming that the scenario reduces the out-of-vehicle time by one-half, the accessibility of the apartment complex to the tertiary hospital after the change is calculated by Equation (6).
t i j 1 = t i j − 1 2 t o ,
where t i j 1 is the actual time spent on public transport to reduce the out-of-vehicle time by one-half; t o is the out-of-vehicle time during the travel.
Assuming that the scenario reduces the out-of-vehicle time by one-third, the public transportation accessibility from the changed apartment complex to the tertiary hospital is calculated by Equation (7).
t i j 2 = t i j − 1 3 t o ,
where t i j 2 is the actual time spent on public transport to reduce the time outside the vehicle by one-third; t o is the time spent outside the vehicle during travel.
The accessibility of tertiary hospitals under the two scenarios was counted, and the change rate of accessibility under different scenarios was calculated based on the realistic accessibility, which is shown in Equations (8) and (9).
C 1 = A i 1 − A i j A i j ,
C 2 = A i 2 − A i j A i j ,
where C 1 is the rate of change in accessibility for the reduced one-half out-of-vehicle time scenario; C 2 is the rate of change in accessibility for the reduced one-third out-of-vehicle time scenario; A i 1 is the accessibility for the reduced one-half out-of-vehicle time scenario; A i 2 is the accessibility for the reduced one-third out-of-vehicle time scenario; A i j is the accessibility for the status quo scenario.

5. Results and Discussion

5.1. Results

5.1.1. Accessibility of Apartment Complexes to Tertiary Hospitals

The accessibility of apartment complexes to tertiary hospitals was calculated using the G2SFCA method using the number of medical and nursing staff (Figure 5) and the number of beds in tertiary hospitals (Figure 6) as supply capacity, respectively. All showed higher accessibility in the middle of the study area and lower accessibility at the edges. There is a large difference between accessibility between the 4th ring and the 5th ring and accessibility within the 4th ring, with higher accessibility in the central and eastern parts of the 4th ring and overall lower accessibility in the west and south.
Based on the results of the two accessibility analyses (Figure 7), it can be seen that the results of accessibility in terms of the number of beds and accessibility in terms of medical and nursing staff are highly correlated. The coefficient of determination R2 = 0.979. Therefore, the number of beds was chosen to represent the supply capacity of tertiary hospitals for subsequent calculations.
The Lorenz curve in this study is based on the number of beds as the cumulative percentage of supply accessibility, the number of medical and nursing staff as the cumulative percentage of supply accessibility, and the cumulative percentage of the population in the community to form a specific curve (Figure 8). Observing the degree of curvature of their Lorenz curves, they all differ in the degree of curvature, and the greater the degree of curvature, the greater the inequity in the distribution of tertiary hospitals. The differences in the Lorenz curves generated by the number of beds and the number of medical and nursing staff are minor, with Gini coefficients of 0.250 and 0.257, respectively. This indicates that the results obtained from both calculations are more equitable.

5.1.2. Variation in Accessibility in Two Scenarios

From Figure 9, it can be seen that when in the reduced one-half out-of-vehicle time scenario, the overall rate of change in accessibility between the fifth and fourth rings increases, the accessibility of the central part between the fourth and second rings decreases, the accessibility of the northwest and southeast corners increases, and the overall accessibility performance decreases after the change within the second ring. Figure 10 shows that in the one-third out-of-vehicle time reduction scenario, the change in accessibility is the same as in the one-half out-of-vehicle time reduction scenario. Figure 11 shows that the rate of change in affected apartment complexes is primarily concentrated in “decrease by more than 4%” and “increase by more than 4%”, in which the proportion of apartment complexes with a 4% decrease in the accessibility of tertiary hospitals is 40.27%, while the proportion of apartment complex with a 4% increase in the accessibility of tertiary hospitals is 30.72%. The proportion of apartment complexes with a 4% decrease in the accessibility of tertiary hospitals is 40.27%, while the proportion of apartment complexes with a 4% increase in the accessibility of tertiary hospitals is 30.72%; but in the scenario that reduces out-of-vehicle time by one-third the percentage of “decrease by more than 4%” is 33.65%, which is lower than the one-half out-of-vehicle time reduction scenario. The percentage of “more than 4% improvement” is 27.63%, which is also lower than the one-half reduction in the out-of-vehicle time scenario, and the remaining “reduce by 4% to 2%”, “reduce by 2% to 0”, “ increase by 0 to 2%”, and “increase by 2 to 4%” are all higher than the reduction by one-half of the outside time scenario. The number of apartment complexes with increased accessibility change rates in both scenarios is greater than those with decreased accessibility change rates. In Figure 12, it can be seen that the overall accessibility decreases as the distance from the center increases. Before 9000 m, both scenarios result in lower accessibility than the existing level, and a reduction of one-half of the out-of-vehicle time decreases accessibility more compared to a reduction of one-third of the scenario; after 9000 m, both scenarios result in higher accessibility than the existing level and a reduction of one-half of the out-of-vehicle time increases accessibility more than a reduction of one-third of the scenario. From the results, it can be seen that while reducing transit travel time (including reducing out-of-vehicle time) can improve the convenience of transit travel and the attractiveness of transit modes, simply reducing out-of-vehicle time has different effects on the accessibility of apartment complexes.
For the different rings (Figure 13), the accessibility of tertiary hospitals decreases for most apartment complexes within the 4th ring. The accessibility of tertiary hospitals decreases more for the one-half out-of-vehicle scenario than for the one-third out-of-hours scenario, while the accessibility of tertiary hospitals increases between the 4th and 5th rings, and the accessibility of tertiary hospitals increases more for the one-half out-of-vehicle scenario than for the one-third out-of-vehicle scenario. In terms of administrative divisions, the accessibility of tertiary hospitals in Dongcheng District, Fengtai District, Haidian District, and Xicheng District decreased under both out-of-vehicle time reduction scenarios. In contrast, the accessibility of tertiary hospitals in Daxing District, Chaoyang District, and Shijingshan District increased under both out-of-vehicle time reduction scenarios. The proportional change in accessibility was greater under the one-half out-of-town time reduction scenario than the one-third out-of-vehicle time reduction scenario.

5.1.3. Variation in Equity in Two Scenarios

The Gini coefficient for the two reduction scenarios (Figure 14) is 0.188 for the one-half reduction scenario and 0.205 for the one-third reduction scenario. The accessibility Gini coefficients for both scenarios are lower than the status quo accessibility Gini coefficient of 0.188, i.e., more equitable than the status quo. Among the inter-ring Gini coefficients (Figure 15), the Gini coefficients for rings three to four are at a lower level and are more equitable compared to other rings; among the administrative districts, the Gini coefficients for Daxing District are at a lower level and are more equitable compared to other districts. At the same time, the reduction in out-of-vehicle time will decrease the Gini coefficient, and the Gini coefficients of accessibility within the study area will all decrease further with the degree of reduction in out-of-vehicle time.

5.2. Discussion

To prove the research value of our hypothesis, the calculation of the accessibility of tertiary hospitals was first performed for the current situation. Additionally, to exclude the effect of using supply capacity on the study, we calculated the accessibility of apartment complexes to tertiary hospitals using the number of beds and the number of medical and nursing staff as supply capacity, respectively. Additionally, we saw that the results for both reachabilities are roughly the same as previous studies [67]. Regression results and Gini coefficients were analyzed for two types of tertiary hospital accessibility. The differences in accessibility and equity between the scenarios and the status quo were then observed and compared by assuming two scenarios of reducing the out-of-vehicle time by one-half and reducing the out-of-vehicle time by one-third. The results suggest that a reduction in out-of-vehicle time may lead to an increase in tertiary hospital accessibility, or it may lead to a decrease in tertiary hospital accessibility. The main reason is that Ga2SFCA calculates accessibility considering both the supply capacity of tertiary hospitals and the demand of residents in apartment complexes. A reduction in out-of-vehicle time will decrease transit travel time, reducing the supply-to-demand ratio for all tertiary hospitals. For some apartment complexes, the sum of the accessibility to all tertiary hospitals may increase or decrease when calculating the accessibility based on Gaussian functions, which results in both positive and negative rates of change in accessibility. Therefore, simply reducing public transportation travel times does not improve the accessibility of tertiary hospitals in all apartment complexes.
In contrast, both scenarios of reducing out-of-vehicle time increased the equity of accessibility from apartment complexes to tertiary care hospitals, as evidenced by varying degrees of reduction in the Gini coefficient. The Gini coefficient of accessibility from apartment complexes to tertiary hospitals decreases more for the scenario with a one-half reduction in out-of-vehicle time than the status quo, i.e., the reduction in out-of-vehicle time does improve the equity of accessibility of apartment complexes. However, this does not prove that a single decrease in out-of-vehicle time will lead to a sustained increase in equity.
The most significant difference between this study and previous studies is that it does not only test the equity of the two supply capacities but also analyzes the effect of out-of-vehicle time on accessibility in the transit travel chain and assumes two scenarios of changes in out-of-vehicle time.
In addition, as a new perspective, the study of public transport interchange time is not limited to out-of-vehicle time. Because of the lack of attention in previous studies, the survey of out-of-vehicle time and its interchange component can help urban planning policymakers formulate policies to improve the equity of service capacity of existing tertiary hospitals and provide new ideas to enhance the equity of other types of public facilities.
For the objectives of future studies, we provide the following recommendations: the supply points of the study population can be extended from tertiary hospitals to all levels of hospitals, i.e., tertiary, secondary, primary, and community hospitals. The mode of travel can be extended from the mode of public transportation travel to multi-modal transportation, i.e., the mode of cabs and public transport. While exploring the process of public transport interchange, the impact of walking time from the apartment complex to the waiting point and waiting time at the waiting point on the accessibility of medical treatment can be analyzed in the process of using cabs for medical treatment. The mode of travel can be extended from the mode of public transportation travel to multi-modal transportation, i.e., the mode of cabs and public transport. While exploring the process of public transport interchange, the impact of walking time from the apartment complex to the waiting point and the waiting time at the waiting point on the accessibility of medical treatment can be analyzed in the process of using cabs for medical treatment. At the same time, the selection of hospitals by different populations in the process of seeking medical treatment was not mentioned in this study, and subsequent studies should collect data on the corresponding populations and the superior departments of the relevant hospitals for different populations in order to improve the accuracy of accessibility in the process of seeking medical treatment. Finally, the algorithm is expected to calculate the appropriate out-of-vehicle time reduction scenarios for different ring zones, administrative divisions, and apartment complexes. Further studies should develop a dynamic out-of-vehicle time reduction scenario at the apartment complex scale and construct a decision model that provides highly accurate, real-time updates of optimal solutions for accessibility and equity of medical facilities.
We assumed changes in apartment complex accessibility for the same percentage reduction in out-of-vehicle time for all transit trip scenarios, which is inconsistent with the results of the actual transit travel time improvement scenarios. However, using our proposed framework, it is possible to analyze the impact of the reduction in transit outbound time for some specific neighborhoods on the accessibility of all apartment complexes to medical care.

6. Conclusions

Using the Ga2SFCA approach, this study proposes the hypothesis of observing changes in accessibility and equity of tertiary hospitals under different scenarios by reducing the number of different out-of-vehicle time scenarios. This hypothesis allows for a more realistic analysis of tertiary hospital accessibility than previous discussions of tertiary hospital accessibility. The strength of this hypothesis lies in its analysis of the role of out-of-vehicle time in the public transport access process, considering the specific impact of this time on the accessibility and equity of tertiary hospitals. The study shows that reducing out-of-vehicle time may result in elevated accessibility or reduced accessibility, but the equity of accessibility is improved in both scenarios.
At the same time, the study still has some limitations, which the research team hopes to improve. For the objectives of the study, the current study is only an analysis of the impact of out-of-vehicle time on accessibility and equity to tertiary hospitals in apartment complexes, does not include secondary hospitals as well as primary and community level hospitals, and does not consider the choice of different hospitals by different types of patients, which can lead to partisan bias in the accessibility results. For the research method, there is still a gap between the Ga2SFCA calculations for reachability and the actual results. A more realistic accessibility result should take into account a combination of preferences, competition among hospitals, the strength of the hospital’s internal departments, and the attractiveness of renowned physicians to their patients. For the study results, this study does not provide a conclusion on how much reduction in out-of-vehicle time leads to a minimum value of equity in access to care. Still, it only calculates that out-of-vehicle time impacts access to care and its equity in the apartment complexes to tertiary hospitals in the study area.

Author Contributions

Conceptualization, Z.W. and N.C.; Data curation, D.L., S.L. (Shihao Li), S.L. (Shuyue Liu) and H.L.; Formal analysis, D.L.; Methodology, Z.W., D.L., S.L. (Shihao Li) and N.C.; Software, S.L. (Shuyue Liu); Supervision, Z.W.; Visualization, S.L. (Shihao Li) and S.L. (Shuyue Liu); Writing—original draft, D.L. and H.L.; Writing—review and editing, Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Natural Science Foundation of Hebei Province, China (grant No. E2023402085) and the Hebei Social Science Development Research Project, China (grant No. 20210201407).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, Z.; Tang, K. Combating COVID-19: Health equity matters. Nat. Med. 2020, 26, 458. [Google Scholar] [CrossRef] [PubMed]
  2. Tseng, M.H.; Wu, H.C. Accessibility Assessment of Community Care Resources Using Maximum-Equity Optimization of Supply Capacity Allocation. Int. J. Environ. Res. Public Health 2021, 18, 1153. [Google Scholar] [CrossRef] [PubMed]
  3. Xia, Y.; Chen, H.; Zuo, C.; Zhang, N. The impact of traffic on equality of urban healthcare service accessibility: A case study in Wuhan, China. Sustain. Cities Soc. 2022, 86, 104130. [Google Scholar] [CrossRef]
  4. Khashoggi, B.F.; Murad, A. Use of 2SFCA Method to Identify and Analyze Spatial Access Disparities to Healthcare in Jeddah, Saudi Arabia. Appl. Sci. 2021, 11, 9537. [Google Scholar] [CrossRef]
  5. Le, K.H.; La, T.X.P.; Tykkylainen, M. Service quality and accessibility of healthcare facilities: Digital healthcare potential in Ho Chi Minh City. BMC Health Serv. Res. 2022, 22, 1374. [Google Scholar] [CrossRef]
  6. Adhvaryu, B.; Mudhol, S.S. Visualising public transport accessibility to inform urban planning policy in Hubli-Dharwad, India. GeoJournal 2021, 87, 485–509. [Google Scholar] [CrossRef]
  7. Bernard, J. Public transport accessibility: Simulation of the usability of public transport in everyday situations. Geografie 2022, 127, 145–168. [Google Scholar] [CrossRef]
  8. Pons Rotger, G.A.; Nielsen, T.A.S. Effects of Job Accessibility Improved by Public Transport System: Natural Experimental Evidence from the Copenhagen Metro. Eur. J. Transp. Infrastruct. Res. 2015, 15, 419–441. [Google Scholar] [CrossRef]
  9. Jamari, J.; Suthanaya, P.A.; Handogo, R.; Suryani, E. Accessibility to Public Transport Services (Case Study of Tabanan Region, Bali-Indonesia). MATEC Web Conf. 2016, 58, 02002. [Google Scholar] [CrossRef]
  10. Risimati, B.; Gumbo, T. Exploring the Applicability of Location-Based Services to Delineate the State Public Transport Routes Integratedness within the City of Johannesburg. Infrastructures 2018, 3, 28. [Google Scholar] [CrossRef]
  11. Mao, B.H. Public Transport Capability is an Important Indicator of National Strength in Transport. J. Beijing Jiaotong Univ. (Soc. Sci. Ed.) 2018, 17, 1–8. [Google Scholar] [CrossRef]
  12. Javid, M.A.; Okamura, T.; Nakamura, F.; Tanaka, S.; Wang, R. People’s behavioral intentions towards public transport in Lahore: Role of situational constraints, mobility restrictions and incentives. KSCE J. Civ. Eng. 2015, 20, 401–410. [Google Scholar] [CrossRef]
  13. Liang, H.; Zhang, Q. Assessing the public transport service to urban parks on the basis of spatial accessibility for citizens in the compact megacity of Shanghai, China. Urban Stud. 2017, 55, 1983–1999. [Google Scholar] [CrossRef]
  14. Saghapour, T.; Moridpour, S.; Thompson, R.G. Public transport accessibility in metropolitan areas: A new approach incorporating population density. J. Transp. Geogr. 2016, 54, 273–285. [Google Scholar] [CrossRef]
  15. Qi, Z.; Lim, S.; Hossein Rashidi, T. Assessment of transport equity to Central Business District (CBD) in Sydney, Australia. Transp. Lett. 2019, 12, 246–256. [Google Scholar] [CrossRef]
  16. Saghapour, T.; Moridpour, S.; Thompson, R.G. Retracted: Modeling access to public transport in urban areas. J. Adv. Transp. 2016, 50, 1785–1801. [Google Scholar] [CrossRef]
  17. Tiran, J.; Mladenovič, L.; Koblar, S. Accessibility to public transport using the PTAL method: The case of Ljubljana. Geod. Vestn. 2015, 59, 723–735. [Google Scholar] [CrossRef]
  18. Moreno-Monroy, A.I.; Lovelace, R.; Ramos, F.R. Public transport and school location impacts on educational inequalities: Insights from São Paulo. J. Transp. Geogr. 2018, 67, 110–118. [Google Scholar] [CrossRef]
  19. Tao, Z.; Zhou, J.; Lin, X.; Chao, H.; Li, G. Investigating the impacts of public transport on job accessibility in Shenzhen, China: A multi-modal approach. Land Use Policy 2020, 99, 105025. [Google Scholar] [CrossRef]
  20. Guzman, L.; Oviedo, D.; Cardona, R. Accessibility Changes: Analysis of the Integrated Public Transport System of Bogotá. Sustainability 2018, 10, 3958. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Li, W.; Deng, H.; Li, Y. Evaluation of Public Transport-Based Accessibility to Health Facilities considering Spatial Heterogeneity. J. Adv. Transp. 2020, 2020, 7645153. [Google Scholar] [CrossRef]
  22. Fransen, K.; Neutens, T.; Farber, S.; De Maeyer, P.; Deruyter, G.; Witlox, F. Identifying public transport gaps using time-dependent accessibility levels. J. Transp. Geogr. 2015, 48, 176–187. [Google Scholar] [CrossRef]
  23. Niedzielski, M.A.; Kucharski, R. Impact of commuting, time budgets, and activity durations on modal disparity in accessibility to supermarkets. Transp. Res. Part D Transp. Environ. 2019, 75, 106–120. [Google Scholar] [CrossRef]
  24. Barta, M. GIS Based Methodology to Analyse the Public Transport Supply-Hungarian Case Studies. Geogr. Pannonica 2022, 26, 92–101. [Google Scholar] [CrossRef]
  25. Mamun, S.A.; Lownes, N.E.; Osleeb, J.P.; Bertolaccini, K. A method to define public transit opportunity space. J. Transp. Geogr. 2013, 28, 144–154. [Google Scholar] [CrossRef]
  26. Ceder, A.; Le Net, Y.; Coriat, C. Measuring Public Transport Connectivity Performance Applied in Auckland, New Zealand. Transp. Res. Rec. J. Transp. Res. Board 2009, 2111, 139–147. [Google Scholar] [CrossRef]
  27. Muller, T.H.J.; Furth, P.G. Transfer Scheduling and Control to Reduce Passenger Waiting Time. Transp. Res. Rec. J. Transp. Res. Board 2009, 2112, 111–118. [Google Scholar] [CrossRef]
  28. Ampt, E. Understanding Voluntary Travel Behaviour Change. Transp. Eng. Aust. 2004, 9, 53–66. [Google Scholar]
  29. Hadas, Y.; Ranjitkar, P. Modeling public-transit connectivity with spatial quality-of-transfer measurements. J. Transp. Geogr. 2012, 22, 137–147. [Google Scholar] [CrossRef]
  30. Guo, Z.; Wilson, N.H.M. Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground. Transp. Res. Part A Policy Pract. 2011, 45, 91–104. [Google Scholar] [CrossRef]
  31. Abdullah, M.; Ali, N.; Shah, S.A.H.; Javid, M.A.; Campisi, T. Service Quality Assessment of App-Based Demand-Responsive Public Transit Services in Lahore, Pakistan. Appl. Sci. 2021, 11, 1911. [Google Scholar] [CrossRef]
  32. Javid, M.A.; Okamura, T.; Nakamura, F.; Wang, R. Comparison of Commuters’ Satisfaction and Preferences with Public Transport: A Case of Wagon Service in Lahore. Jordan J. Civ. Eng. 2013, 7, 461–472. [Google Scholar]
  33. Iseki, H.; Taylor, B.D. Not All Transfers Are Created Equal: Towards a Framework Relating Transfer Connectivity to Travel Behaviour. Transp. Rev. 2009, 29, 777–800. [Google Scholar] [CrossRef]
  34. Ceder, A.; Chowdhury, S.; Taghipouran, N.; Olsen, J. Modelling public-transport users’ behaviour at connection point. Transp. Policy 2013, 27, 112–122. [Google Scholar] [CrossRef]
  35. Hansen, W.G. How Accessibility Shapes Land Use. J. Am. Inst. Plan. 1959, 25, 73–76. [Google Scholar] [CrossRef]
  36. Joseph, A.E.; Bantock, P.B. Measuring potential physical accessibility to general practitioners in rural areas: A method and case study. Soc. Sci. Med. 1982, 16, 85–90. [Google Scholar]
  37. Perry, B.; Gesler, W. Physical access to primary health care in Andean Bolivia. Soc. Sci. Med. 2000, 50, 1177–1188. [Google Scholar] [CrossRef]
  38. Luo, W.; Wang, F. Measures of Spatial Accessibility to Healthcare in a GIS Environment: Synthesis and a Case Study in Chicago Region. Environ. Plann. B Plann. Des. 2003, 30, 865–884. [Google Scholar] [CrossRef]
  39. Alam, B.M.; Thompson, G.L.; Brown, J.R. Estimating Transit Accessibility with an Alternative Method: Evidence from Broward County, Florida. Transp. Res. Rec. J. Transp. Res. Board 2010, 2144, 62–71. [Google Scholar] [CrossRef]
  40. Mao, L.; Nekorchuk, D. Measuring spatial accessibility to healthcare for populations with multiple transportation modes. Health Place 2013, 24, 115–122. [Google Scholar] [CrossRef]
  41. Rosero-Bixby, L. Spatial access to health care in Costa Rica and its equity: A GIS-based study. Soc. Sci. Med. 2004, 58, 1271–1284. [Google Scholar] [CrossRef]
  42. Radke, J.; Mu, L. Spatial Decompositions, Modeling and Mapping Service Regions to Predict Access to Social Programs. Ann. GIS 2000, 6, 105–112. [Google Scholar] [CrossRef]
  43. Tao, Z.; Cheng, Y. Research progress of the two-step floating catchment area method and extensions. Prog. Geogr. 2016, 35, 589–599. [Google Scholar] [CrossRef]
  44. Cheng, M.; Huang, W.W. Measuring the Accessibility of Residential Care Facilities in Shanghai Baesd on Gaussian Two-Step Floating Catchment Area Method. J. Fudan Univ. Nat. Sci. 2020, 59, 129–136. [Google Scholar] [CrossRef]
  45. Chen, Y.; Wu, J. The Effect of the Referral System on the Accessibility of Healthcare Services: A Case Study of the Wuhan Metropolitan Development Zone. Int. J. Environ. Res. Public Health 2022, 19, 10441. [Google Scholar] [CrossRef]
  46. Kang, J.Y.; Michels, A.; Lyu, F.; Wang, S.; Agbodo, N.; Freeman, V.L.; Wang, S. Rapidly measuring spatial accessibility of COVID-19 healthcare resources: A case study of Illinois, USA. Int. J. Health Geogr. 2020, 19, 36. [Google Scholar] [CrossRef] [PubMed]
  47. Tahmasbi, B.; Mansourianfar, M.H.; Haghshenas, H.; Kim, I. Multimodal accessibility-based equity assessment of urban public facilities distribution. Sustain. Cities Soc. 2019, 49, 101633. [Google Scholar] [CrossRef]
  48. Kompil, M.; Jacobs-Crisioni, C.; Dijkstra, L.; Lavalle, C. Mapping accessibility to generic services in Europe: A market-potential based approach. Sustain. Cities Soc. 2019, 47, 101372. [Google Scholar] [CrossRef]
  49. Gong, S.; Gao, Y.; Zhang, F.; Mu, L.; Kang, C.; Liu, Y. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Trans. GIS 2021, 25, 1504–1521. [Google Scholar] [CrossRef]
  50. Lara-Hernandez, J.A.; Melis, A. Understanding the temporary appropriation in relationship to social sustainability. Sustain. Cities Soc. 2018, 39, 366–374. [Google Scholar] [CrossRef]
  51. Arranz-López, A.; Soria-Lara, J.A.; Pueyo-Campos, Á. Social and spatial equity effects of non-motorised accessibility to retail. Cities 2019, 86, 71–82. [Google Scholar] [CrossRef]
  52. Dadashpoor, H.; Rostami, F.; Alizadeh, B. Is inequality in the distribution of urban facilities inequitable? Exploring a method for identifying spatial inequity in an Iranian city. Cities 2016, 52, 159–172. [Google Scholar] [CrossRef]
  53. Vijayaraghavan, M.; Martin, R.M.; Sangrujee, N.; Kimani, G.N.; Oyombe, S.; Kalu, A.; Runyago, A.; Wanjau, G.; Cairns, L.; Muchiri, S.N. Measles supplemental immunization activities improve measles vaccine coverage and equity: Evidence from Kenya, 2002. Health Policy 2007, 83, 27–36. [Google Scholar] [CrossRef]
  54. Ruiz Gómez, F.; Zapata Jaramillo, T.; Garavito Beltrán, L. Colombian health care system: Results on equity for five health dimensions, 2003–2008. Rev. Panam. Salud Publica 2013, 33, 107–115. [Google Scholar] [CrossRef] [PubMed]
  55. Tsou, K.-W.; Hung, Y.-T.; Chang, Y.-L. An accessibility-based integrated measure of relative spatial equity in urban public facilities. Cities 2005, 22, 424–435. [Google Scholar] [CrossRef]
  56. Boyne, G.; Powell, M.; Ashworth, R. SPATIAL EQUITY AND PUBLIC SERVICES: An empirical analysis of local government finance in England. Public Manag. Rev. 2001, 3, 19–34. [Google Scholar] [CrossRef]
  57. Denhardt, R.B.; Edward, T.; Jennings, J. Image and Integrity in the Public Service. Public Adminnistration Rev. 1989, 49, 753–755. [Google Scholar] [CrossRef]
  58. Luo, W.; Qi, Y. An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health Place 2009, 15, 1100–1107. [Google Scholar] [CrossRef]
  59. Polo, G.; Acosta, C.M.; Dias, R.A. Spatial accessibility to vaccination sites in a campaign against rabies in São Paulo city, Brazil. Prev. Vet. Med. 2013, 111, 10–16. [Google Scholar] [CrossRef]
  60. Dai, D. Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health Place 2010, 16, 1038–1052. [Google Scholar] [CrossRef]
  61. Jumadi, J.; Fikriyah, V.N.; Hadibasyir, H.Z.; Sunariya, M.I.T.; Priyono, K.D.; Setiyadi, N.A.; Carver, S.J.; Norman, P.D.; Malleson, N.S.; Rohman, A.; et al. Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia. Sustainability 2022, 14, 14478. [Google Scholar] [CrossRef]
  62. Liu, M.; Luo, S.; Du, X. Exploring Equity in Healthcare Services: Spatial Accessibility Changes during Subway Expansion. ISPRS Int. J. Geo-Inf. 2021, 10, 439. [Google Scholar] [CrossRef]
  63. Naylor, K.B.; Tootoo, J.; Yakusheva, O.; Shipman, S.A.; Bynum, J.P.W.; Davis, M.A. Geographic variation in spatial accessibility of U.S. healthcare providers. PLoS ONE 2019, 14, e0215016. [Google Scholar] [CrossRef]
  64. Kim, Y.; Byon, Y.J.; Yeo, H. Enhancing healthcare accessibility measurements using GIS: A case study in Seoul, Korea. PLoS ONE 2018, 13, e0193013. [Google Scholar] [CrossRef] [PubMed]
  65. Zhong, S.Y.; Xin, Y.; Rui, C. The accessibility measurement of hierarchy public service facilities based on multi-mode network dataset and the two-step 2SFCA: A case study of Beijing’s medical facilities. Geogr. Res. 2016, 35, 731–744. [Google Scholar]
  66. Lu, C.; Zhang, Z.; Lan, X. Impact of China’s referral reform on the equity and spatial accessibility of healthcare resources: A case study of Beijing. Soc. Sci. Med. 2019, 235, 112386. [Google Scholar] [CrossRef]
  67. Su, Y.W.; Ma, Y.; Chang, J.; Liu, J.; Long, Y. Research on Accessibility and Equity of Emergency Medical Care: A Case Study of Acute Myocardial Infarction. J. Hum. Settl. West China 2023, 38, 1–7. [Google Scholar] [CrossRef]
Sustainability 15 12453 g001
Figure 1. Schematic distribution of apartment complexes and tertiary hospitals in the study area.
Figure 1. Schematic distribution of apartment complexes and tertiary hospitals in the study area.
Sustainability 15 12453 g001
Sustainability 15 12453 g002
Figure 2. Cumulative distribution histogram of travel time.
Figure 2. Cumulative distribution histogram of travel time.
Sustainability 15 12453 g002
Sustainability 15 12453 g003
Figure 3. Schematic diagram of the whole process of public transport travel.
Figure 3. Schematic diagram of the whole process of public transport travel.
Sustainability 15 12453 g003
Sustainability 15 12453 g004
Figure 4. Proportion of average out-of-vehicle time in total travel time from an apartment complex to a tertiary hospital.
Figure 4. Proportion of average out-of-vehicle time in total travel time from an apartment complex to a tertiary hospital.
Sustainability 15 12453 g004
Sustainability 15 12453 g005
Figure 5. Number of medical and nursing staff as a result of accessibility calculation of supply volume.
Figure 5. Number of medical and nursing staff as a result of accessibility calculation of supply volume.
Sustainability 15 12453 g005
Sustainability 15 12453 g006
Figure 6. Number of beds in tertiary hospitals as a result of accessibility calculation of supply volume.
Figure 6. Number of beds in tertiary hospitals as a result of accessibility calculation of supply volume.
Sustainability 15 12453 g006
Sustainability 15 12453 g007
Figure 7. Regression results of two accessibility analyses.
Figure 7. Regression results of two accessibility analyses.
Sustainability 15 12453 g007
Sustainability 15 12453 g008
Figure 8. Lorentz curve of service level equilibrium in tertiary hospitals.
Figure 8. Lorentz curve of service level equilibrium in tertiary hospitals.
Sustainability 15 12453 g008
Sustainability 15 12453 g009
Figure 9. Change rate of hospital accessibility reduced by half of the out-of-car time.
Figure 9. Change rate of hospital accessibility reduced by half of the out-of-car time.
Sustainability 15 12453 g009
Sustainability 15 12453 g010
Figure 10. Change rate of hospital accessibility reduced by one-third of out-of-car time.
Figure 10. Change rate of hospital accessibility reduced by one-third of out-of-car time.
Sustainability 15 12453 g010
Sustainability 15 12453 g011
Figure 11. Changes in accessibility under different scenarios: (a) percentage of apartment complexes under different rate of change intervals; (b) increase or decrease in accessibility.
Figure 11. Changes in accessibility under different scenarios: (a) percentage of apartment complexes under different rate of change intervals; (b) increase or decrease in accessibility.
Sustainability 15 12453 g011
Sustainability 15 12453 g012
Figure 12. Variation in accessibility with distance to the city center.
Figure 12. Variation in accessibility with distance to the city center.
Sustainability 15 12453 g012
Sustainability 15 12453 g013
Figure 13. Accessibility rate of change in two scenarios: (a) area defined by the ring road; (b) administrative district.
Figure 13. Accessibility rate of change in two scenarios: (a) area defined by the ring road; (b) administrative district.
Sustainability 15 12453 g013
Sustainability 15 12453 g014
Figure 14. Results of the Lorenz curve and Gini coefficient for the status quo and two scenarios.
Figure 14. Results of the Lorenz curve and Gini coefficient for the status quo and two scenarios.
Sustainability 15 12453 g014
Sustainability 15 12453 g015
Figure 15. Change in Gini coefficient under different scenarios: (a) area between different ring roads; (b) administrative district.
Figure 15. Change in Gini coefficient under different scenarios: (a) area between different ring roads; (b) administrative district.
Sustainability 15 12453 g015
Table 2. Data display of Amap Open Platform (part).
Table 2. Data display of Amap Open Platform (part).
od-idShortest Path Distance (m)Bus Expense (Chinese Yuan)Total Walking Distance (m)Actual Total Distance (m)Actual Total Time Spent (s)Interchange Walking Time (s)Interchange Walking Distance (s)Public Transportation Time Consumption (s)Public Transportation Distance (m)Taking the Number of Public Transport Lines
014,098568420,8525469585684488320,1681
113,4625113720,32356389741137466419,1861
221,5135224423,788437519222244245221,5442
325,6226217325,909438018612173251823,7361
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Z.; Liu, D.; Li, S.; Liu, S.; Li, H.; Chen, N. Analyzing the Impact of Decreasing Out-of-Vehicle Time of Public Transportation Travel on Accessibility to Tertiary Hospitals. Sustainability 2023, 15, 12453. https://doi.org/10.3390/su151612453

AMA Style

Wang Z, Liu D, Li S, Liu S, Li H, Chen N. Analyzing the Impact of Decreasing Out-of-Vehicle Time of Public Transportation Travel on Accessibility to Tertiary Hospitals. Sustainability. 2023; 15(16):12453. https://doi.org/10.3390/su151612453

Chicago/Turabian Style

Wang, Zhenbao, Dong Liu, Shihao Li, Shuyue Liu, Huiqing Li, and Ning Chen. 2023. "Analyzing the Impact of Decreasing Out-of-Vehicle Time of Public Transportation Travel on Accessibility to Tertiary Hospitals" Sustainability 15, no. 16: 12453. https://doi.org/10.3390/su151612453

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop