Examining the Connectivity between Urban Rail Transport and Regular Bus Transport

Abstract

According to the principle of urban transport integration and sustainable development, in this work, we study the level of connection between urban rail transit and regular bus transport, construct an evaluation indicator system according to the characteristics of the connection system, use the entropy weighting method (EWM) to calculate the weights of the indicators to determine the influence of each indicator on the level of connection, and construct a TOPSIS comprehensive evaluation model, which can make an overall evaluation of objects subject to multiple factors to analyze the level of connection between rail transit stations. Finally, the system of evaluation indicators and the analysis of the level of connection are applied to an example of a rail transit station in operation in Wuxi city, and the problems of connection and interchange in the case station are analyzed. We find that 57.5% of rail stations in Wuxi have low connectivity and that interchange information service and average transfer time are the most influential factors. This study defines and quantifies eight key indicators that influence the level of rail-transit connectivity to quantify and grade the connectivity of different stations, and selects the city of Wuxi as a case study for validation. Our research provides theoretical support and practical guidance for improving rail transit interchange capacity and the sustainable development of public transport.

1. Research Background

With rapid urbanization, traditional industries are transforming around sustainable development [1], where transport systems need to be upgraded to meet the growing travel demand in urban areas [2], and where today’s travelers tend to use different modes of transport depending on the specific situation [3]. There is an urgent need for public transport in China to take advantage of the different modes of transport and to promote a comprehensive transport network with sustainable transport development. Rail transit represents a modern, high-capacity, efficient, punctual, and green mode of transport. As of 31 December, 2022, a total of 53 cities in 31 Chinese provinces had opened 290 urban rail transit lines with 9584 km of operation [4], accounting for more than 50% of the share of public transport in first-tier cities [5,6]. This situation indicates that urban transport systems with railways as the backbone have become a mainstream trend, and ideally other modes of transport should be highly connected and complementary to railways [7,8]; however, there are many factors that influence the distribution of railways stations, such as early urban road network planning and environmental protection zones, which result in some stations being poorly connected to other modes of transport, and their level of connectivity also affects the choice of rail transport for travelers.

Based on the concept of the sustainable urban mobility plan (SUMP), urban rail transport needs to be connected to other modes of transport to ensure a travel mix [9]. This concept was validated by scholars such as Deschaintres [10], who found that transport modes, such as bike sharing and buses, could complement rail travel by examining the interactions between different modes of public transport. Yang et al. [11] state that the encouraging car-based travelers to use public transport requires the development of attractive and accessible public transport systems. According to Yue et al. [12], the use of a single mode of transport by travelers leads to an imbalance in the overall transport network, and the attractiveness of public transport needs to be improved by optimizing the public transport network (reducing walking and waiting times). This corresponds to the findings of Huang et al. [13], who showed that higher public transport accessibility is associated with higher travel demand and patronage in the area through a Bayesian network learning approach. However, with the informatization and automatization of transport facilities, we also need to take into account the information services of public transport. Friedrich et al. [14] propose a six-level evaluation scheme for the service quality of trips. Rail transit is the core of public transport in China, and it is effective in improving the spatial connectivity of cities. Song et al. [15] used the city of Wuhan as a case study, and found that rail transit increased urban connectivity by 25%, while Gan et al. [16] propose that commuting by bus is currently one of the main alternatives to rail travel, and that the service radius of rail stations can be extended to about 6 km. Bus transport, with its large number, wide distribution and relative flexibility, remains one of the best interchange with rail transport, and, according to Yue et al. [17], the rational planning and adjustment of buses facilitates the integration of fragmented urban spaces. The above studies illustrate that improving the capacity of public transport can effectively increase the share of public transport passengers and that a combination of rail and regular buses can effectively improve urban connectivity and accessibility. From the perspective of the travelers’ transfer experience, Feng et al. [18] proposed a non-linear integer planning model that substantially reduces the cost of waiting time for rail travelers to switch to buses. By optimizing rail and bus schedules, Kang et al. [19] showed that improving schedule synchronization can effectively improve traveler service. Cao et al. [20] studied travelers’ comfort and satisfaction in terms of both waiting time and seat availability. Mishra et al. [21] proposed some indicators for the connectivity of multi-transit networks to quantify and assess the service capacity of public transport.

Improving railway connectivity is seen as one of the ways of alleviating the current high intensity of urban travel flows and enhancing sustainable mobility, and the level of connectivity is highly dependent on the efficiency of rail connections with other modes of transport in all aspects. Most previous studies have focused on single transport networks, such as the efficiency of rail operations and bus timetables. In contrast to single transport networks, problems of interchanges between different transport networks need to be considered and need to be more relevant to the actual situation and the needs of the traveler. The scope of this paper is to examine the level of connectivity between urban rail stations and regular public transport. By analyzing the characteristics that affect the connectivity between rail transit and regular buses, and by combining the reality of the situation with the availability of relevant data, a connectivity evaluation indicator system is constructed and the level of connectivity between urban rail transit stations and regular buses is analyzed using an entropy weighting method and a TOPSIS comprehensive evaluation model. With this paper, we hope to enhance the connectivity between rail transit networks and regular buses through an in-depth study of the connection levels of rail transit stations, thereby improving the service capacity of urban public transport and the sustainable development of urban transport planning.

2. Research Methodology

2.1. Establishing Evaluation Indicators

The complete evaluation of a transport connection system is composed of five aspects: technical evaluation, economic evaluation, social evaluation, environmental evaluation, and system construction evaluation [22]. Since the purpose of the evaluation is to study how the existing regular bus network and rail network can be developed in a coordinated manner and eventually achieve integrated operation, only the technical and social evaluations are considered. Based on the system conditions of the connection between the two modes of transport, eight quantitative indicators are proposed to measure the state of the connection: interchange demand, average transfer time, capacity matching, interchange information service, per capita interchange area, reasonability of regular bus installation, regional accessibility, and interchange comfort.

2.1.1. Technical Evaluation

• Average transfer time

Transfer time refers to the time spent by passengers in the interchange (including walking, waiting for buses, etc.). Transfer time is an indicator describing the operational efficiency of the interchange system and refers to the service time of the connecting interchange facilities occupied by passengers during the entire process of transferring between rail and regular bus transport.

• 2.. Interchange requirements

Interchange demand refers to the demand for interchanges between rail stations and other modes of transport (regular bus transport) and is the average passenger flow per unit time for interchanges at rail stations. Rail transit stations are important nodes of the urban transport system. In the initial planning of rail transit construction, stations are mainly built with reference to the geographical location of commercial areas, residential areas, and other major transport hubs in the city. The demand for rail transit stations is also a reflection of the rail transit’s ability to access the city’s integrated transportation system.

• 3.. Per capita interchange area

The per capita interchange area is used to measure the capacity of interchange facilities to accommodate passengers within an interchange system and can reflect the level of congestion and comfort of an interchange. The per capita interchange area is a quantitative indicator used to evaluate the adaptability of interchange passenger facilities.

The area of interchange facilities per capita is

(1)$S=\frac{\beta {{\displaystyle \sum }}_{a=1}^b\left(E_a\right)}{\mathrm{P}\ast \mathrm{T}}$

where P is the actual number of passengers exchanged between rail stations and regular buses during the peak hour of passenger traffic, $\beta$ is the ratio of interchange passenger flow to passenger flow at the rail transit station [23,24], $E_a$ is the area used for the $a$-th interchange at the rail station, b is the total number of interchanges at the rail station, and T is the transfer time.

• 4.. Interchange information service

Passenger information service corresponds to whether a full range of interchange information and related services is provided to passengers, and it is evaluated on the basis of whether stations include clearly marked and located interchange routes, notifications of normal traffic conditions and accidents (announcements), electronic information boards, and instant information screen displays (videos), passenger service centers, accessible lifts and escalators, police services, first aid facilities, public toilets, and shops [25].

• 5.. Reasonability of regular bus setting

The reasonability of regular bus setting is a measure of how well regular buses within the interchange can accommodate different interchange needs, reflecting the degree of reasonability of regular bus settings. When the density of bus stops and the number of bus routes around a rail transit station are combined, the resilience and service capacity of the interchange system is high; when the density of bus stops and the number of routes is low, however, the resilience and service capacity of the connection system is weak.

• 6.. Capacity matching

Capacity matching is a measure of how well regular bus transport capacity matches rail transport capacity, and if it can be used to determine the adaptability of passenger transport equipment. This indicator can be expressed as the ratio of the average number of passengers transferring to rail stations during peak travel hours to the capacity of regular bus transport.

The capacity matching ratio is

(2)$H=\frac{\mathrm{A}}{\mathrm{P}}$

where A is the capacity of regular buses within the service radius of the rail station. The ideal match is as follows: when $H>1$, the connection is good; when $H\le 1$, the capacity of regular buses does not meet the needs of rail passenger traffic [23]. In this case, additional bus routes and frequencies are needed to meet passenger demand.

2.1.2. Social Evaluation

• Regional accessibility

The implementation of the interchange system combines various modes of transport, such as urban road traffic and metro transport, into a large integrated transport system. Together with the integrated development of community and commercial facilities, these modes of transport complete this system and create a TOD model (public-transport-oriented development) [26], making the system more convenient and increasing the speed at which people can travel. This system also makes the regions more connected and accessible and can significantly enhance inter-regional accessibility. At the same time, the TOD model integrates public transport with urban life, not only providing people the convenience of choosing their mode of travel at will but also bringing urban transport services into an optimal balance and promoting the optimization of travel flow between metro and urban facilities, thereby improving the operational efficiency of the entire urban public transport system [27].

• 2.. Interchange comfort

Interchange comfort refers to the level of comfort and satisfaction passengers obtain from interchanges, reflecting the level of intelligence of station facilities, the adaptability of interchange facilities, such as the environment of interchange lanes, and the level of overall travel services provided to passengers.

This section aims to establish an evaluation indicator system for the coordination of rail transit and regular bus interchanges based on the characteristics of existing transport interchanges. This section also defines and quantifies the comprehensive evaluation indicator system and further selects a comprehensive evaluation method to calculate the evaluation indicators, thereby deriving the basis for grading the degree of coordination of rail transit and regular bus interchanges, as well as conducting an in-depth analysis of the stations.

2.2. Connection System Evaluation Methods

2.2.1. Rating of Assessment Indicators

The assessment level is divided into five levels from high to low: A, B, C, D, and E. Due to the ambiguity in the delineation of the boundaries of some indicator levels, it is necessary to quantitatively assess the indicator levels. As the construction and operation of rail transport varies from city to city, some of the indicators cannot be graded according to a fixed value, so related data will be collected, normalized, and graded on a scale of 1 to 10. The preliminary delineation of the boundaries of each indicator level is shown in Table 1.

2.2.2. Calculation of Indicator Weights

Commonly used methods for calculating indicator weights include the entropy weighting method (EWM) and the analytic hierarchical process (AHP). EWM is an objective weighting method [28,29,30,31,32,33], while AHP is a subjective weighting method [34,35,36,37,38], both of which can determine the weight of each indicator in the sample, but AHP requires industry experts to perform qualitative analysis for the indicators and requires a larger amount of statistical data when there are adjustments to the indicators, which is not conducive to the flexibility of the evaluation model. In contrast, EWM determines objective weights by the variability in the indicators, which is simpler, more practical, and more reliable. Therefore, this paper uses the entropy weighting method to determine the weights.

Determine the weights W_j

Standardization of positive indicators

(3)$d_{ij}=\frac{f_{ij}-f_j^{\min }}{f_j^{\max }-f_j^{\min }}+1 ,$

Standardization of inverse indicators

(4)$d_{ij}=\frac{f_j^{\max }-f_{ij}}{f_j^{\max }-f_j^{\min }}+1$

where $d_{ij}$ is the standardized value of the indicator, $f_{ij}$ is the value of the $j$ assessment indicator for the $i$ object, ${{\displaystyle f}}_j^{\max }$ is the maximum value of the assessment indicator interval, and ${{\displaystyle f}}_j^{\min }$ is the minimum value of the assessment indicator interval.

(5)$p_{ij}=\frac{d_{ij}}{{\displaystyle \sum _{i=1}^n{d}_{ij}}}$

(6)$H_j=-\frac{1}{\ln n}{\displaystyle \sum _{i=1}^n{p}_{ij}}\ln (p_{ij})$

(7)$G_j=1-H_j$

(8)$W_j=\frac{G_j}{n-{\displaystyle \sum _{j=1}^n{H}_j}}$

where $p_{ij}$ is the share of the $j$ assessment indicator of $i$ in the overall assessment system; $H_j$ is the entropy value of the assessment indicator $j$; $G_j$ is the information utility value of the assessment indicator $j$, and $W_j$ is the weight of each assessment indicator.

2.3. TOPSIS Comprehensive Evaluation Model

The TOPSIS method, also known as the technique for order preference by similarity to an ideal solution, is a common comprehensive evaluation model [39,40,41,42,43], which can make full use of the information in the original data, and its results can accurately reflect the gap between each evaluation object. Compared to other comprehensive evaluation methods [44,45,46,47,48], it has the following two main advantages: 1. It can avoid the subjectivity of the data and can portray the comprehensive impact of different indicators well; 2. There are no strict restrictions on the data distribution and the number of samples and indicators, and it is suitable for both small samples and large systems with multiple indicators [49]. For this study, we hope that the model can be applied to different cities; the scale and construction of rail transport varies from city to city, so the data should be kept objective, the overly complicated calculations in actual use should be avoided, and the TOPSIS comprehensive evaluation model should remain flexible and practical. Therefore, the TOPSIS model is chosen to evaluate the interchange capacity of rail transport and regular bus commute.

2.3.1. Virtual Ideal Scenario

An ideal scenario, u, is first simulated [50], where the higher the benefit-based indicator the better, and the lower the cost-based indicator the better

(9)$u=\left(u_1,u_2,\cdots ,u_{\mathrm{n}}\right),$

from which

(10)$u_i=\left\{\begin{array}{cc}{\max }_j\left\{a_{ij}\right\},& a_{ij} \mathrm{is} \mathrm{indicator} \mathrm{of} \mathrm{efficiency} \\ {\min }_j\left\{a_{ij}\right\},& a_{ij} \mathrm{is} \mathrm{indicator} \mathrm{of} \cos \mathrm{t} \end{array}\right.,$

2.3.2. Building a Matrix of Relative Deviations

Next, the deviation matrix R is built for the set of interchange indicators from u

(11)$R=\left(\begin{array}{cccc}{r}_{11}& r_{12}& \cdots & r_{1n}\\ r_{21}& r_{22}& \cdots & r_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ r_{m1}& r_{m2}& \cdots & r_{mn}\end{array}\right),$

from which

(12)$r_{ij}=\frac{\left|a_{ij}-u_i\right|}{\underset{j}{\max }\left\{a_{ij}\right\}-\underset{j}{\min }\left\{a_{ij}\right\}}$

where m represents the number of subsets in the evaluation set; m = 1, 2, 3, …; and n represents the number of indicators, n = 1, 2, 3, ….

2.3.3. Build a Weighted Average of Deviations for Each Rail Transit Station

The combined distance F between the station and u is obtained by weighting the deviation matrix R with weights W

(13)$F_i={{\displaystyle \sum }}_{j=1}^n{W}_j\ast r_{ij}(\mathrm{i}=1, 2, 3..., \mathrm{m}).$

2.3.4. Integrated Evaluation

The t-th evaluated rail transit station is set to Ft, and the s-th evaluated rail transit station is set to Fs. If Ft < Fs, then the $\mathrm{t}$-th rail transit station is rated higher than the $\mathrm{s}$-th rail transit station.

3. Experiment

There are currently 53 cities in China with rail transit. Through a comparative analysis and comprehensive study of each city with rail transit; Wuxi city in Jiangsu Province was chosen as the case study for this paper for three main reasons:

• In first-tier cities and super first-tier cities, there are many interested parties in rail transport, and operational information and data are relatively difficult to obtain. Such cities include Beijing and Shanghai, where rail transport operational data are relatively scattered and confidential. Conversely, Wuxi, as a second-tier city, has a single interested party in rail transport [51], and information and data are relatively easy to obtain;

• The average daily passenger flow of the Wuxi urban rail transit network is above 500,000, and the total passenger flow in 2022 has exceeded 600 million [52]. Moreover, Wuxi is the first city in China to use QR codes for rail travel [53], and its overall intelligence level is higher than that in most cities, so its rail-station-related data are more comprehensive and accurate than those in other cities;

• Combined with the algorithm in this paper, a high-quality rail transit station is needed as a reference for the virtual ideal solution. Wuxi City’s Sanyang Square Station, as the largest metro station in China, received a perfect score in the TOD Development Quality Index in 2018, ranking first in the number of indicators, such as compactness of development, connecting bus lines, and number of entrances [54], which will be beneficial as a reference for this study.

3.1. Parameter Setting

Other data for the model are $C, I$,$B_{iq}, S_i$, $D_i$, and $K_i$, which are obtained as follows:

• (1). C indicates the target city of the study;

• (2). I indicates the rail station under study;

• (3). $B_{iq}$ denotes a bus stop within a certain range around the rail station under study. Research generally considers the optimal range for walking connections to be 500 to 800 m [55,56]. Beyond this range, walking connections will be less efficient and less comfortable, so this model uses a range of 600 m for interchanging rail stations with bus stops;

• (4). $S_i$ is the comprehensiveness of the information and services provided for station i in relation to the interchange, judged by counting the relevant facilities described above;

• (5). $D_i$ is the regional accessibility of station i, indicating the number of commercial complexes and residential areas within the service radius of a combined rail and regular bus trip;

• (6). $K_i$ is the interchange comfort of station i. A questionnaire was used to collect evaluation information on the passenger flow at different times of the day.

3.2. Example Studies

First, we obtained the fourth quarter 2022 operation and maintenance data from the operating company of the Wuxi rail transit system, including data on interchange requirements, interchange passenger flow, capacity matching and average transfer time. By examining the availability of facilities and services at each rail station in Wuxi, the data on interchange information service were obtained; by using mapping software to count and collect the number of bus stops and bus routes within a range of 600 m around the stations, the ratio of bus stops and routes was calculated to obtain data on the reasonability of regular bus setting; by putting the data on interchange passenger flow into Equation (1), data on the per capita interchange area were obtained; by counting the number of commercial complexes and residential areas that can be served by rail transit stations connected to regular buses (set at 2 km), data on regional accessibility were obtained. With the assistance of the railway system’s operation platform, an embedded small questionnaire was released within the rail transit travel service software during November and December, 2022, asking interchange travelers questions related to the interchange experience and scoring the relevant interchange services on a scale of 1 to 10; more than 20,000 responses were obtained in total, and they were screened and tallied to obtain data on interchange comfort. The present study focuses on evaluating these eight indicators.

In this system, the weights of the indicators are as follows: interchange information service > transfer time > interchange comfort > reasonability of regular bus setting > regional accessibility > interchange demand > per capita interchange area. Here, the weighting of interchange information service is the highest at 0.192, while the weighting of capacity matching is the lowest at 0.0624 (see Table 2), indicating that the interchange information service has the greatest influence on the level of connectivity, while capacity matching has the least influence.

A comprehensive TOPSIS evaluation model was then constructed to derive the assessed value of the connection level of each rail station in Wuxi. Here, a higher assessed value represents a higher connection level. According to the differences between the minimum and maximum values, the stations were divided into four levels of connectivity: Level 4, with an assessed value of [0, 1.2788); Level 3, with an assessed value of [1.2788, 1.4006); Level 2, with an assessed value of [1.4006, 1.5223); and Level 1, with an assessed value of [1.5223, ∞). The stations were divided into 5 stations of Class 1, 29 stations of Class 2, 30 stations of Class 3, and 16 stations of Class 4 (see Table 3). Based on the results, most of the present-day rail stations in Wuxi have a low-to-medium connection level.

3.3. Rail Station Analysis

There is a total of 80 rail transit stations in Wuxi, and we have carried out a preliminary analysis of them, in terms of indicators, based on four connection levels. The stations in Connection Level 1 have excellent connection performance in most aspects, while those in Connection Level 2 have good connection performance, second only to those in Level 1, but those in Connection Levels 3 and 4 have a lot of room for improvement. Therefore, we have randomly selected two stations in Level 1 and Level 3 for in-depth analysis, which will help to clarify the gap between stations with high connection levels and those with low connection levels, and subsequently provide suggestions for improving the stations’ connection capacity.

3.3.1. Civic Center Station Analysis

The Civic Center Station is a metro station with a connection level of 1 and an assessed connection level of 1.597 (see Figure 1). The higher-weighted indicators include a transfer information service level of 9, an average transfer time of 6 min with the surrounding regular buses, an interchange comfort level of 8.56, and a regional accessibility level of 18. Additionally, the reasonability of the regular bus setting for this station is 5.5. All of these indicators are at a relatively high level. These results demonstrate that the spatial arrangement of the station and the level of intelligence of the equipment are high. Moreover, the surrounding regular buses are reasonable, and there are many commercial and residential areas within the service radius. The remaining indicators are the daily interchange demand of 2200 passengers, the per capita interchange area of 2.248 square meters, and the capacity matching ratio of 0.879, which indicate that the average daily interchange demand is high, and the surrounding bus capacity is slightly inadequate to meet the interchange needs. Overall, the connection between the Civic Center Station and the surrounding regular buses is at a high level, with little room for optimization.

3.3.2. Xibei Canal Station Analysis

The Xibei Canal Station is a metro station with a connection level of 3, whose assessed connection level was evaluated at 1.306 (see Figure 2). Considering the higher-weighted indicators, the interchange information service level of this station is 6, the average transfer time with the surrounding regular buses is 7 min, the interchange comfort level is 5.65, the regional accessibility is 8, and the reasonability of the regular bus setting is 1.25. These results indicate that the transfer path between the Xibei Canal Station and regular buses is short, the station information and service facilities are lacking, and the levels of the station environment and equipment intelligence are low. Additionally, there are few regular bus stops or routes in the vicinity, and the number of commercial and residential areas within the service radius is low. Among the remaining indicators, the average daily interchange demand is 1.124, the per capita interchange area is 1.779 square meters, and the capacity matching ratio is 0.356. These indicators demonstrate that although the average daily interchange demand is high, the surrounding bus capacity cannot meet the interchange needs, and the interchange is slightly congested. From a comprehensive point of view, the connection between the Xibei Canal Station and the surrounding regular buses is at a low level.

3.3.3. Improvement Suggestions

Based on the analysis and comparison of the Civic Center Station and Xibei Canal Station, it can be seen that stations with a high level of connectivity perform better in most aspects of connectivity than stations with a low level of connectivity. Stations with a high level of connectivity have less room for improvement and can be improved on their existing characteristics, such as optimizing the synchronization of bus and metro schedules, reducing the waiting time for transfers, or opening new entrances and exits to ease the pressure of traveler flow, while stations with a low level of connectivity need to start improving with indicators that carry more weight, such as: (1) Enriching the rail station’s transfer information and service facilities; (2) Increasing the number of routes and trips of regular bus stops around the stations to improve the evacuation capacity of the surrounding buses; (3) Developing TOD mode to increase the commercial services around the stations, thus enhancing the regional accessibility.

4. Results and Discussion

For the connection between rail stations and regular buses, this article proposes a sound evaluation method: the establishment of an evaluation indicator system and the analysis of connectivity levels. Firstly, by evaluating the characteristics of rail stations and regular bus connections, eight indicators are proposed in terms of both technical and social aspects. The clarification of the connection indicators is useful as a reference for future station planning or renovation, allowing the combination of the rail network and bus network to function at a stronger passenger capacity. This is in line with the findings of Kang et al. [57], who optimized the rail–bus connection by adjusting the schedule of buses, as well as their operating routes, reducing passenger transfer waiting time by 53.4%, suggesting that with the analysis and adjustment of the rail-regular bus connection, future urban public transport will be more in line with the sustainable mobility needs of travelers.

Secondly, by analyzing and comparing the level of connectivity of rail stations and quantifying the capacity of each station, it is possible to “personalize” the improvement of a single station, enhance the accessibility of the station, and share the pressure of passenger flows with the neighboring stations, thus enhancing the performance of the rail network, which is in line with the findings of several studies by Kim [58], Softie [59], and others.

The study of the connection levels of the rail stations in Wuxi city has allowed us to analyze the actual situation of the case stations in relation to the indicator data and to make recommendations for improvement. Based on our study findings, we consider that major improvements should be made to the planning of stations with lower connection levels, such as the 30 metro stations at Connection Level 3—Zhuangqiao, Baizhuang, Xizhang, etc.—and the 16 rail stations in Connection Level 4—Grand Theatre, Xuelang, Yangming, etc. Moreover, the findings of Noriyasu et al. [60] demonstrate that the rail network will significantly improve the overall accessibility of the city. This suggests that rail transit is an axis that connects the urban center to the periphery of the city, and that increasing the connectivity between stations and buses can effectively increase the accessibility and range of travel, thereby increasing the value of the surrounding land use and promoting the urbanization of the suburban areas, which is conducive to the overall sustainable development of the city.

5. Limitations and Future Study

During the preliminary investigation of the study, we reviewed a large number of relevant papers and reports from the transportation industry, in which a large number of indicators were proposed from both economic and social aspects, but some of these indicators had problems, such as vague definitions and difficulties in quantification, and because the stakeholder structures of public transportation are more complex, such as the rail transportation operation and maintenance data in the relevant government departments or the data related to interchange users in the system operating enterprises, many indicators are difficult to obtain. In our study, we focused on the accessibility and practicality of the data to establish an interchange indicator system, and after research and communication with rail transport system operators, we chose the eight indicators mentioned above. However, in the process of acquiring and processing the data, we found that the rail departments and enterprises could only provide data from within the rail system, and we had to collect the remaining data around the rail stations ourselves. Because of the wide distribution and large number of stations in the city, it takes a lot of time and effort to collect and measure the data, which limits the updating of the data and the real-time nature of the model.

In the process of data collection and processing, we found that the current indicators could be deepened; for example, the data on regional residential area accessibility show that some residential areas are large and most of them are outside the set area, which means that the interchange system is less able to serve the travelers in that residential area. Then, in future studies, we will continue to explore the indicators in depth to evaluate the interchange capacity of rail transport more accurately. Furthermore, we will gradually enrich the indicators according to the development and needs of the urban public transport system to make the indicator system more complete.

6. Conclusions

Our study defines and quantifies eight interchange indicators by examining the factors that influence the ability to transfer between urban rail stations and regular bus stations in order to establish an evaluation indicator system. With the digital transformation of public transport, the connection of rail transport to other public transport needs to be considered from its construction. Defining the connection indicators will help provide guidance during the construction process, thus reducing the cost of later transformation.

In addition to establishing an evaluation index system, this study also constructs an interchange capacity evaluation model to quantify and grade the interchange capacity of rail transit stations and regular bus stations. This does not only reflect the gap in interchange capacity between different stations on a macro level, but also facilitates a pain point analysis of different stations on a micro level. As China’s cities are expanding, rail connectivity is closely linked to the connectivity between urban areas. Quantifying rail connectivity can also visually reflect the ability of public transport to serve different areas of the city, thus helping city managers to rectify urban planning and contribute to the sustainable development of the city.

This study also applies and validates the evaluation model using Wuxi as a case city. The results show that 57.5% of rail transit stations in Wuxi have low connectivity, with average transfer time and interchange information service being the most influential indicators. This is mainly due to the fact that rail transit stations in high-traffic areas are more complete in all aspects, while those in medium- and low-traffic areas are relatively poor in all aspects. However, the future development of urban areas needs the support of public transport, and stations with weak connection capacity hinder this development. Therefore, rail transit stations and related transport facilities around them should be adjusted and improved according to the characteristics and needs of different urban areas, so that they can better meet the interchange needs of travelers in order to promote the integrated development of public transport.

In conclusion, this paper helps to explore the influencing factors of rail and bus interchange in greater depth, bringing about an intuitive and directed improvement in the interchange capacity of rail transport and promoting the integration of major public transport networks. Practical implications include: (1) Helping the government to make clearer decisions on public transport planning and neighborhood planning, which is conducive to sustainable urban development; (2) Helping rail transport to improve connectivity and increase the service capacity and efficiency of rail transport as the core of combined travel, thereby improving the passenger flow sharing of rail transport; (3) Providing guiding data and feedback to rail transit system operators, which helps to provide more refined interchange services to travelers during the operation phase.

Author Contributions

Writing—original draft preparation, H.Y.; writing—review and editing, Y.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Restrictions apply to the availability of these data. Data were obtained from BWTON Technology Co., Ltd. (Hangzhou, China), and are available from authors with the permission of BWTON Technology Co., Ltd.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. Civic Center Station with 10 bus stops within a 600 m radius.

Figure 2. Xibei Canal Station with 6 bus stops within a 600 m radius.

Table 1

Grading of evaluation indicators.

Evaluation Indicators	Evaluation Level
	E(Poor)	D(Poor)	C (Medium)	B(GOOD)	A (Excellent)
Transfer time(min)	[40, 60]	[25, 40)	[14, 25)	[6, 14)	[0, 6)
Interchange requirements	[0, 200]	(200, 400]	(400, 600]	(600, 800]	(800, ∞)
Interchange information and services	[0, 2]	(2, 4]	(4, 6]	(6, 8]	(8, 10]
Reasonability of regular bus setting	[0, 2]	(2, 4]	(4, 6]	(6, 8]	(8, 10]
Capacity matching	[0, 0.4]	(0.4, 0.8]	(0.8, 1.2]	(1.2, 1.6]	(1.6, 2.0]
Per capita interchange area	[0, 0.6]	(0.6, 1.2]	(1.2, 1.8]	(1.8, 2.4]	(2.4, ∞)
Regional accessibility	[0, 5]	(5, 10]	(10, 15]	(15, 20]	(25, 30]
Interchange comfort	[0, 2]	(2, 4]	(4, 6]	(6, 8]	(8, 10]

Table 2

Weighting of indicators.

Regional Accessibility	Interchange Demand	Transfer Time	Interchange Information Service	Transfer Comfort	Per Capita Interchange Area	Capacity Matching	Reasonability of Regular Bus Setting
0.1330	0.0798	0.1677	0.1920	0.1496	0.0724	0.0624	0.1432

Table 3

Classification of station connection levels.

Gradation Criterion	Level	Amount
>1.5223	Level 1	5
1.4006~1.5223	Level 2	29
1.2788~1.4006	Level 3	30
<1.2788	Level 4	16

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