Travel Satisfaction of Bus Rapid Transit Users in A Developing Country: The Case of Bhopal City,India

Abstract

India's burgeoning population has led to an increased demand for transportation services, particularly in urban areas. To address the growing mobility needs and mitigate the adverse effects of personal vehicle usage, efficient public transit services are imperative. Several Indian cities have implemented bus rapid transit (BRT) systems to address this problem, but ridership for the majority of the BRT systems remains below breakeven (this refers to the point at which the revenue generated from ridership equals or surpasses the costs associated with operating and maintaining the transportation system). Thus, it is necessary to assess the determinants of commuter satisfaction with BRT. The present study aims to examine factors affecting travel satisfaction among BRT commuters. By using structural equation modeling (SEM), the effect of demographic (age and gender) and travel-related variables (fare price, travel time, trip frequency, reliability, ease of using bus service, and comfort level) on BRT commuters' satisfaction and usage of BRT services were analyzed. It was found that while ease of using the service, comfort level, and reliability positively affect BRT travel satisfaction, trip frequency is significantly affected by the ease of using the service only. Furthermore, demographic variables (age, and gender of respondents) were not found to have significant effect of travel satisfaction and trip frequency. The findings from this can study serve as a base for policymakers, city officials, and urban planners to identify commuters' priorities for a well-functioning BRT in India and formulate policies to attract riders by enhancing their preferences.

Keywords

Public transportation Bus rapid transit system Travel satisfaction,Structural equation modeling

Urban transport is linked to a variety of detrimental externalities, encompassing significant repercussions from emissions, congestion, noise pollution, and road crashes. Extensive academic and non-academic research has thoroughly examined and documented these adverse effects (1–4). Of these negative externalities, traffic congestion remains a prominent challenge in major cities globally. Cities such as Mumbai, Delhi, and Bengaluru in India have consistently ranked among the most congested, according to the TomTom Traffic Index, a congestion measurement index that sorts and ranks cities worldwide based on average travel times. This index covers approximately 390 cities in 56 countries across six continents ( 5 – 7 ).

To address these challenges, numerous global initiatives have been implemented. These initiatives aim to mitigate negative externalities associated with transportation, focusing on issues such as traffic congestion (8–10), reducing greenhouse gas (GHG) emissions, and minimizing carbon footprints on a global scale (11, 12). A key component of these efforts involves transitioning towards sustainable mobility alternatives, particularly emphasizing the enhancement of local public transit services (13, 14).

Given the substantial costs linked to establishing and maintaining rail-based mass rapid transit (MRT) systems, policymakers are increasingly investigating the viability of bus rapid transit (BRT). Distinguished by its capacity for accommodating large passenger volumes, BRT combines the speed and reliability of rail transit with the cost-effectiveness and adaptability of buses. The key features that differentiate BRT from conventional public services include exclusive bus lanes, pre-payment of fares, facilitated boarding through multiple entrances, signal prioritization at intersections, upgraded bus stops, modern buses, and advanced intelligent transport system (ITS) technologies ( 15 ). BRT stands out from traditional bus systems by delivering expedited journey times, reduced traffic congestion, heightened passenger satisfaction, and the potential for increased ridership ( 16 , 17 ). Thus, BRT emerges as a pragmatic solution to the challenges associated with urban mobility. As the literature suggests, BRT systems offer a cost-effective alternative, providing efficient and passenger-friendly transportation services in urban areas ( 18 ).

Extensive research and analysis have been conducted by numerous scholars on the advantages and benefits of BRT services on a global scale. Based on the findings of Levinson et al. ( 19 ) and Wirasinghe et al. ( 20 ), it has been established that BRT can serve as a financially viable strategy for delivering a transport service that is characterized by both effectiveness and superior standards ( 19 , 20 ). According to research findings, BRT exhibits the capacity to achieve a commendable level of performance while maintaining a comparatively economical expenditure. The subsequent sections of this paper will provide a more detailed analysis of the topics “BRT in India” and “BRT in Bhopal,” respectively.

Bus Rapid Transit in India

As of 2050, India is expected to have a population of 1.5 billion ( 21 ). With such a large population, there will be a strong demand for transportation, as transportation demand is a derived demand. Because India is a low–middle-income country, conventionally, public transportation (bus, metro, etc.) and intermediate public transportation (auto-rickshaws) largely provide the mobility services. However, in recent years, the number of vehicles registered in major Indian cities has suddenly increased. There has been an annual average growth of 9.9% in the number of registered vehicles in India during the decade from 2009 to 2019 ( 22 ). The increasing use of private vehicles has a negative impact on public transportation in two ways: firstly, if existing commuters switch to private vehicles, a definite economic loss is incurred to public transportation services; secondly, as the number of vehicles on the road increases, congestion levels rise, and buses suffer the most because of their high passenger car unit (PCU) values, making public transportation less appealing. Fatima and Kumar ( 23 ) stated that a major objective of implementing public bus transit systems is to decrease commuters' dependence on private vehicles, thereby promoting sustainability in urban transportation. In addition, the use of private vehicles has led to negative externalities, such as traffic congestion, air pollution, noise pollution, and road accidents, which have had a detrimental impact on the national economy ( 24 – 26 ).

Considering all such reasons, state and city authorities in multiple Indian cities have launched BRT systems. The advantage of implementing a BRT lies in the savings of travel time, since this system is designed to run on exclusive and dedicated lanes. In addition, compared to a metro system, the capital cost of implementing a BRT system is significantly lower ( 27 – 29 ). A BRT system has the capability to operate for 15,000–20,000 peak hour peak direction traffic (PHPDT) ( 30 ). For the implementation of BRT projects in India, financial assistance had been provided to cities under the Jawaharlal Nehru National Urban Renewal Mission (JNNURM). The government of India provided at least 50% of the financial outlay, while cities and states were responsible for the remainder ( 31 ).

BRT operations have been executed in several Indian cities, such as Ahmedabad, Indore, Bhopal, Pune, Jaipur, Delhi, Rajkot, Hubballi-Dharwad, Amritsar, and Surat. Detailed information on BRT systems in different Indian cities is listed in Table 1.

Table 1.

Characteristics of Currently Operating Bus Rapid Transit Systems in Indian Cities

City	Passengers per day	Number of corridors	Length (km)
Ahmedabad	130,000	1	82
Amritsar	60,000	3	31
Bhopal	99,304	1	24
Hubballi-Dharwad	90,000	1	22
Indore	45,500	1	12
Jaipur	6622	1	7
Pune	67,000	3	29
Rajkot	7500	1	11
Surat	13,500	1	10

Source: Retrieved from www.brtdata.org and Smart City, Bhopal.

Moreover, a few researchers have attempted to study different dimensions of BRT systems in India. Kathuria et al. ( 28 ) assessed the development of BRT systems in India from 2008 to 2015. They studied the diverse systems and corridor features, including off-board and on-board ticketing systems, and analyzed revenue models for each BRT system in eight distinct Indian cities. They found that Ahmedabad had almost 30% of the total fleet size of overall BRT systems in India. With respect to regulatory context, SPV (special purpose vehicle) companies were formed in almost all eight cities after observing Ahmedabad’s BRT success ( 28 ). Mahadevia et al. ( 31 ) looked into inclusivity, social issues, and challenges related to the implementation of BRT systems in India and observed that because of urban sprawl, the travel distances are increasing, accessibility is decreasing, and the urban poor are the worst sufferers in this state of affairs. They opine that the location of BRT stations should be designed considering these aspects. Rizvi and Sclar ( 29 ) investigated the relative relevance of “suitable” design standards, the “correct” institutional structure, and the necessity for “political will” to project success and propose a “three-dimensional” planning approach. Mahendra and Rajagopalan ( 32 ) assessed the effect of implementing a BRT system on health of urban residents for the city of Indore and found that an increase in walking or cycling, a decrease in the use of private vehicles, and the consequent decrease in air pollution exposure could save about 14 lives annually. In addition, they observed that relative to existing trends on motorization, more than 96 deaths could have been avoided between 2013 and 2017 along the BRT line with no investment in the BRT system. Further analysis found that the difference between the post-BRT scenario and the business-as-usual scenario was 11%, with a 1.1% reduction in mortality risk associated with exposure to particles small enough to be measured. Tripathy and Samanta ( 27 ) established the advantages of the BRT system over regular buses and came to the conclusion that it offers commuters a very quick, dependable, comfortable, and cost-effective form of transportation. They further observed that there are very few chances of traffic congestion and accidents because BRT systems operate in their own dedicated lanes. Air and noise pollution are relatively low under the BRT system, which also offers enough facilities for passenger rights-of-way, simple boarding, and alighting.

Bus Rapid Transit in Bhopal

The authors learned about the Bhopal BRT system from the officials of the Smart City Mission and the concerned operating officials of the Bhopal BRT. The Smart City Mission integrates with Bhopal’s BRT system to advance urban mobility through sustainable transportation, technological integration, infrastructure development, and enhanced civic engagement, aligning with the mission’s goal of creating a smart city. The BRT system in the town follows a “flexible integrated operation,” implying that routes can operate both inside and outside the dedicated corridor, thus reducing the number of passenger transfers in the system, enabling the use of both existing buses and special BRT buses, and consequently lowering the construction cost relatively.

The system runs in mainly three types of routes: trunk routes (TRs) for 67.6 km; standard routes (SRs) for 123.6 km; and complimentary/intermediate public transit (IPT) routes. The TR is the central route in a BRT system, often forming the backbone of the entire network. It typically covers longer distances and connects significant destinations within a city. The SR refers to the regular bus paths within the designated BRT corridor, and they usually include defined stations, stops, and lanes to ensure efficient and organized transit along the predetermined path, offering reliability and predictability for commuters. Finally, the IPT route in a BRT system involves the seamless integration of different modes of transportation, such as feeder services, metro lines, or other public transit options.

The system functions based on a net-cost model. In a net-cost contract, the operator (private operator) assumes the responsibility of delivering a chosen service for a predetermined period and retains all money generated from that service. In the event of financial difficulties encountered by a bus service operating along a specific route, Bhopal City Link Limited (BCLL) provides a subsidy to the operator. BCLL is a SPV in the form of a public limited company set up to operate and manage public transport in the city of Bhopal. In contrast, should the services demonstrate profitability, the operator is obligated to remunerate BCLL through the payment of a royalty. The other features related to the operating model of the BRT in Bhopal are as follows.

Thirty-eight percent of the bus cost is shared by the operator as an advance charge before taking over the bus for operation from BCLL.

The operator pays INR 4444 (USD 53.35) as the average monthly royalty to BCLL.

The operator is responsible for all taxes, insurance, and other expenses associated with the operation. The operator is responsible for collecting all ticket revenue.

Revenue sharing of ticket revenue between the operator and BCLL is 80:20.

Based on the information collected by the authors from BCLL, there are 205 buses available for operation on the corridor. However, the average fleet utilization rate is 85.08%. The daily revenue generated by the BRT system is INR 1,198,941 (USD 14,394.47) from an average daily ridership of 99,304. The average distance covered by all fleet in a day is around 39,113 km. The fuel efficiencies for new and old buses are 5.46 and 4.06 km/l respectively. The average age of the fleet is close to 8 years and staff to bus ratio is 2.80.

Through discussions with stakeholders, it was determined that the utilization of BRT services in Bhopal is below expectations. Recognizing the benefits of BRT in reducing urban transport externalities, it is essential to address the deficiencies of the Bhopal BRT system so that sustainable commuting can be promoted. Therefore, this study concentrates on three objectives: (i) understanding the factors that influence BRT travel satisfaction; (ii) assessing the factors that influence trip frequency for BRT services; and (iii) investigating the relationship between travel satisfaction and trip frequency. The current literature has explored little about the determinants of travel satisfaction with respect to BRT in India. Using insights from the global literature on travel satisfaction, the study’s findings can assist municipal officials and policymakers in India in developing strategies to increase BRT ridership and user satisfaction.

Literature Review

The literature review focuses on three areas: travel satisfaction in general; travel satisfaction with respect to public transport; and travel satisfaction with respect to BRT.

In an effort to make the transportation ecosystem more sustainable and livable, transportation planning agencies all over the world are investing in sustainable modes of transportation such as transit and bicycle/walk infrastructure in addition to implementing a variety of voluntary behavior change programs ( 33 ). Travel experiences can lead to satisfaction directly as a result of cognitive or affective responses. Analyzing the variables affecting the travel satisfaction of bus service patronage is imperative for customer retention ( 34 ). Measurement and analysis of consumer satisfaction with public transit services make it easier to track service performance, analyze the market, compare services, and identify priority areas ( 35 ). Previous research has defined satisfaction as a personal evaluation of whether a product or service has provided a desirable level of enjoyment, including both under- and over-fulfilment ( 36 ). Several studies have demonstrated the significant impact of service satisfaction on overall travel satisfaction and trip frequency ( 37 – 50 ).

Sukhov et al. ( 36 ) find that overall travel satisfaction is significantly influenced by transit service attributes (reliability/functionality, information, courtesy/simplicity, comfort, safety) in Karlstad, Sweden. Susilo and Cats ( 51 ) utilized multivariate statistical analysis to identify significant factors that affect the satisfaction levels of different trip phases and overall journey experiences across various transport modes and traveler categories in eight European cities. Their study revealed that while satisfaction with the primary trip stage significantly affects overall trip satisfaction, satisfaction with the access and egress trip stages strongly influences satisfaction with the primary trip stage. Majumdar et al. ( 45 ) conducted a study involving 898 respondents who utilized various modes of transportation and identified key factors that drive travel satisfaction for policy formulation aimed at enhancing the quality of life in New Delhi, India. Their study revealed that socioeconomic variables such as gender and age along with accessibility and built-environment characteristics heavily influence commuters’ trip satisfaction levels. The study also found that mode choice and associated safety perceptions significantly affect commuters’ trip satisfaction levels.

In the specific geographical setting of Bucaramanga, Colombia, Márquez et al. ( 52 ) conducted a study utilizing a hybrid discrete choice model to examine the determinants influencing individuals’ selection of a BRT feeder service or a motorbike taxi. The researchers identified safety perception, income level, gender, and age as the primary variables influencing mode choice, and found safety perception, income, gender, and age to be the key variables in choosing the mode. Guzman and Gomez Cardona ( 53 ) developed a method for correlating changes in density and mixed activities, as well as their impact on ridership levels for the Bogota BRT. They also discovered that a diverse mix of occupations and households affect the demand for BRT boarding. Although the role of psychological or subjective factors on public transport use behavior has received the attention of researchers, BRT use behavior (which refers to the usage behavior of BRT passengers and the factors affecting it) has received relatively less attention ( 54 ).

Sun et al. ( 43 ) investigated satisfaction differences in bus travel before and after COVID-19, utilizing data from interviews with 930 commuters in Taiyuan, China. They observed that a substantial association between happiness levels and modes of travel by bus, both alone and with companions, exists. According to Fang et al. ( 50 ), key factors that affect the travel satisfaction of both choice and captive public transport users in China include the waiting area, driver operating behavior, complaint handling, and the performance of top officials.

Table 2 lists the salient variables considered by existing studies for determining travel satisfaction in relation to public transport or BRT.

Table 2.

Selection of Variables for Travel Satisfaction

Study	Study location	Variables considered	Method for analysis
( 55 ) Deepa et al. (2022)	India	Gender, age, education qualification, income, vehicle ownership, comfort level, cleanliness, fare price, reliability	Generalized heterogeneous data model
( 56 ) Matubatuba & De Meyer-Heydenrych (2022)	South Africa	Perceived usefulness, subjective norm, attitude, perceived behavioral control	Technology acceptance model and structural equation modeling (SEM)
( 45 ) Majumdar et al. (2021)	India	Age, gender, income, marital status, education level, accessibility, safety, congestion level, side walk availability	Ordered logit model
( 57 ) Atombo & Dzigbordi Wemegah (2021)	Ghana	Safety, travel time, accessibility, comfort, fare price, travel time, availability	Confirmatory factor analysis, SEM
( 58 ) Guzman et al. (2021)	Colombia	Fare price, travel time, fuel price, income level, unemployment rate	Coarsened exact matching
( 59 ) Ganji et al. (2021)	Iran	Safety, travel time, accessibility, comfort, staff behavior, fare price, travel time, trip frequency	Q-methodology, factor analysis
( 42 ) Zhao & Li (2019)	China	Age, gender, income, last-mile options, accessibility, travel time, fare price, land use	Ordered logit model
( 60 ) Ratanavaraha et al. (2016)	Thailand	Education level, gender, income, reliability, accessibility	Exploratory factor analysis and SEM
( 61 ) dell’Olio et al. (2010)	Spain	Gender, age, availability of driving license, frequency of using, income, waiting time, accessibility, safety, comfort level, fare price, travel time	Ordered logit model
( 62 ) O’Farrell & Markham (1974)	Ireland	Travel time, fare price, waiting time	Analysis of variance

Based on the literature review, the present study considers demographic factors (age, gender), travel characteristics (fare price, travel time), and service quality parameters (reliability, ease of using service, comfort level) for analyzing the travel satisfaction and trip frequency of BRT users.

To analyze the travel satisfaction and trip frequency of BRT users in Bhopal City, India, this study considers demographic factors (age, gender), travel characteristics (fare price, travel time), and service quality parameters (reliability, ease of using service, comfort level). For the econometric analysis, this paper utilizes structural equation modeling (SEM), which simultaneously captures the impact of exogenous variables on endogenous variables and the influences of endogenous variables on each other based on a stated framework ( 63 , 64 ). It is suitable for addressing the complex relationships between the observed variables (age, gender, fare price, travel duration) and latent variables (comfort level, reliability, trip frequency, and travel satisfaction). To the best of the authors' knowledge, this study is the first to determine the satisfaction factors of BRT users in India based on a SEM structure that accounts for the intricate interdependencies between the observed and the latent variables.

Research Contribution

The literature review shows that although the studies about travel satisfaction for different types of public transit are extensive, there is a lack of literature combining both determinations of travel satisfaction and usage of the BRT system, especially in influential developing countries such as India. Since BRT is an integral part of the public transit system in India, it is imperative to analyze the factors that affect the travel satisfaction and use behavior of the BRT systems in India. The present study considers the BRT system of Bhopal, the capital city of Madhya Pradesh (MP), as the study area for two reasons: firstly, a well-invested BRT system in Bhopal exists; secondly, it is noteworthy that most of the existing studies in this domain have predominantly centered on Tier 1 cities, such as Delhi ( 45 ) and Bangalore ( 55 ). Consequently, a significant research gap exists concerning the factors influencing travel satisfaction and trip frequency, specifically within BRT services in Tier 2 cities, such as Bhopal. The present study considers age, gender, accessibility, last-mile connectivity, reliability, comfort level, fare price, and travel cost as the explanatory variables to determine the travel satisfaction and use behavior of the BRT system in Bhopal.

Methods and Data Collection

This section is divided into three parts: (i) profile of the study area; (ii) design of the survey questionnaire; and (iii) approach for the data analysis.

Study Area

Bhopal is the capital of MP and is also referred to as the “City of Lakes.” The city’s population was 2.3 million in 2011. The city is spread over 2772 square kilometers, with a population density of 855 people per square kilometer. Twelve BRT stations were selected to conduct the intercept survey within the city: (i) Bagumugalia Extension Station; (ii) Board Office Station; (iii) Habibganj Naka Station; (iv) Roshanpura Station; (v) Kalpana Nagar Station; (vi) Nadra Station; (vii) Indrapuri Station; (viii) Manisa Market Station; (ix) Sarvadharma Colony Station; (x) Harsh Vardhan Nagar station; (xi) New market square station; and (xii) Lalghati bus station. These locations were chosen to ensure that most of the city was covered. The survey took place outside the buses (only at bus stops) with the permission of the concerned authorities responsible for operating the Bhopal BRT.

Design of the Survey Questionnaire and Collection of Samples

Before commencing the primary data collection phase, the surveyors undertook comprehensive training to acquaint themselves with the content and structure of the survey questionnaire. The survey was meticulously designed to accommodate the linguistic context of Bhopal, where Hindi serves as the prevalent language. The utilization of Google Forms facilitated the administration of the survey, streamlining the procedure and avoiding the necessity for manual data input. The process of data gathering was conducted with mobile devices such as smartphones and tablets. The collection of data was limited to bus stops, specifically focusing on those who were in the process of waiting for BRT services. An intercept survey was conducted by randomly selecting the respondents. The total number of samples collected from primary survey was 403, as shown in Table 3.

Table 3.

Primary Data Collection From Survey Locations

Survey location	Number of samples collected	Percentage ofsamples collected (%)
Bagumugalia Extension Station	35	8.7
Board Office Station	34	8.4
Habibganj Naka Station	37	9.2
Roshanpura Station	43	10.7
Kalpana Nagar Station	28	6.9
Nadra Station	29	7.2
Indrapuri Station	24	6.0
Manisa Market Station	30	7.4
Sarvadharma Colony Station	31	7.7
Harsh Vardhan Nagar Station	40	9.9
New Market Square Station	44	10.9
Lalghati Bus Station	28	6.9
Total	403	100

Addressing concerns about data bias, the surveyors meticulously collected data during both peak and non-peak hours to mitigate any potential biases. Notably, a substantial portion of BRT commuters, habitually traveling during peak hours for work-related purposes, also utilize the service during non-peak hours, both for work and non-workdays. A random sampling approach, considering various individuals and diverse travel times, was employed to ensure the representativeness of our data samples.

Before conducting the intercept survey, the surveyors (graduate students) undertook comprehensive training (the author provided training on the approach to conduct the survey, emphasizing the significance of each survey question and instructing on how to effectively engage with respondents) to familiarize themselves with the survey content and structure of the questionnaire. The survey was conducted in both Hindi and English, keeping in mind the demography of the target population (daily commuters using BRT from the targeted stations). The BRT system primarily caters to a demography with financial constraints that frequently relies on public transportation. The questionnaire was designed to capture this user group’s distinct experiences and preferences using the Bhopal BRT service. Surveyors were provided with smartphones and tablets to collect information utilizing Google Forms, which helped avoid manual data input. A verbal agreement from the surveyors was gathered before the data collection to ensure proper ethical standards. The survey team randomly selected the respondents waiting for the BRT buses in the stations.

A map of the selected stations is depicted in Figure 1.

Five constructs related to the BRT service quality are defined: reliability (RL), ease of using the service (EoU), comfort level (CL), trip frequency (TF), and travel satisfaction (TS). Several response items were created to measure these constructs: three responses for RL, two for EoU and CL, three for TF, and two for TS. The items/statements for constructing these latent variables are depicted in Table 4.

Table 4.

Survey Questions for Constructing Latent Variables

Construct	Response items	Codes for response items
Reliability	Buses arrive at scheduled time	RL1
	Buses reach the destination at scheduled time	RL2
	While transferring between the BRT buses, waiting time is as per schedule	RL3
Ease of using the service	I find it easy to use BRT since it is very accessible to my home and workplace	EoU1
Ease of using the service	I find it easy to use BRT since the first and last-mile connectivity is good	EoU2
Comfort level	Seating space and ride quality is comfortable in BRT	CL1
Comfort level	I feel that it is more comfortable to use BRT compared with driving a private vehicle	CL2
Trip frequency	I often use BRT services for work trip/education	TF1
	I often use BRT services for shopping trips	TF2
	I often use BRT services for recreational trips	TF3
Travel satisfaction	I am satisfied with the existing BRT services when I am traveling at peak hours i.e., 8–10 a.m. and 5–8 p.m.	TS1
Travel satisfaction	I am satisfied with the existing BRT services when I am traveling at non-peak hours	TS2

Note: BRT = bus rapid transit.

For operationalizing the response items a three-point Likert scale was used (from 1: disagree to 3: agree). A three-point Likert scale (from 1: disagree to 3: agree) was used to capture the three constructs’ responses and ensure comfortable and effective communication, because the target population falls into a group with relatively low literacy rates ( 65 ). Respondents’ travel time, travel cost, and age were defined as continuous data, while gender was defined as nominal (0 = female; 1 = male). The travel time and cost are for a one-way trip regardless of trip purpose.

Research Hypothesis and Approach for Data Analysis

This study used R language based software for statistical analysis, specifically the JASP package for SEM. Firstly, exploratory factor analysis (EFA) was performed to determine whether the constructs and response items could be grouped ( 66 , 67 ). To assess the appropriateness of the EFA method for the survey data, Kaiser–Meyer–Olkin (KMO) and Bartlett tests were performed. A KMO value larger than 0.5 and Bartlett’s test significance level less than 0.05 imply a substantial correlation in the data ( 68 ). To check the plausibility of the constructs, a minimum correlation value of 0.3 was adopted ( 69 ). Eigenvalues over one were considered to determine the number of constructs ( 68 ). The varimax orthogonal rotation method was used to avoid any correlation between the derived constructs ( 70 ). A correlation test was performed to reject the presence of any collinearity between the derived constructs. To check the internal consistency of the response items, Cronbach's alpha was applied to each construct, and a cut-off value of 0.7 was examined for each construct ( 71 ).

The conceptual structural equation model for the present study is depicted in Figure 2.

In this study, the SEM framework is designed to evaluate how three constructs—EoU, RL, and CL—along with exploratory variables such as age, gender, fare price, and travel time, affect the trip frequency construct and trip satisfaction construct, both of which serve as dependent variables (DVs). In addition, the study aims to investigate the relationship between the trip satisfaction construct and trip frequency construct, providing a comprehensive analysis of the factors influencing travel experiences. In technical terms, EoU, RL, and CL are considered exogenous variables, meaning they are not influenced by other variables in the model. On the other hand, trip frequency is an endogenous variable, as it is influenced by the exogenous variables mentioned. Notably, trip satisfaction acts as both an exogenous and endogenous variable, signifying its dual role in being influenced by certain factors and, in turn, influencing others. This framework enables a comprehensive analysis of how these variables interact and contribute to overall travel experiences.

To analyze this, the following hypotheses were considered in the present study.

H1: Age influences trip frequency significantly.

H2: Age influences travel satisfaction significantly.

H3: Gender has a significant effect on trip frequency.

H4: Gender has a significant influence on travel satisfaction.

H5: Usability has a significant and positive impact on trip frequency.

H6: Usability has a significant and positive impact on trip satisfaction.

H7: Trip frequency is significantly and positively affected by reliability.

H8: Reliability has a significant and positive impact on travel satisfaction.

H9: The level of comfort has a significant and positive influence on trip frequency.

H10: The level of comfort has a significant and positive impact on trip satisfaction.

H11: The price of a ticket (fare price) has a significant and negative impact on travel frequency.

H12: The fare price has a significant and negative impact on travel pleasure.

H13: Travel time has a significant and negative influence on trip frequency.

H14: Travel time has a significant and negative effect on travel satisfaction.

H15: Travel satisfaction has a significant and positive effect on trip frequency.

The next step of the statistical analysis is to formulate the SEM framework to identify the determinants of travel satisfaction and trip frequency for the Bhopal BRT system. SEM is utilized to examine one or more independent variables (IVs), which can be either continuous or discrete, as well as one or more DVs. In SEM, it is possible for a construct or a measured variable to serve as both an IV and a DV ( 72 ). In the field of behavioral sciences, several non-continuous variables are employed, encompassing dichotomous, ordinal, and nominal variables, as well as counts and durations. Structural equation models have been extended to accommodate various response types ( 73 ). SEM involves defining a model of the theory to be tested with respect to structural characteristics (regression) equations. The use of SEM in travel behavior dates back to the 1970s. Its main application lies in establishing cause and effect relationships involving theoretical constructs for which there are no direct operational measurement methods or known measurement scales.

The basic formulation in the structural equation model is as follows ( 74 ):

η_{j} = α + B_{η_{j}} + Γ_{X_{1_{j}}} + ζ_{j}

(1)

The specific equation for the proposed structural equation model in the present study can be written as follows:

\begin{matrix} Trip frequency (TF) = α + B_{TF} + Γ_{EoU} + Γ_{RL} + Γ_{CL} \\ + Γ_{Age} + Γ_{Gender} + Γ_{Fare Price} \\ + Γ_{Travel Time} + ζ_{TF} \end{matrix}

(2)

\begin{matrix} Trip satisfaction (TS) = α + B_{TS} + Γ_{EoU} + Γ_{RL} + Γ_{CL} \\ + Γ_{Age} + Γ_{Gender} + Γ_{Fare Price} \\ + Γ_{Travel Time} + ζ_{TS} \end{matrix}

(3)

where α represents the intercept; B_TF and B_TS are the coefficients for the respective trip frequency and trip satisfaction constructs; Γ terms represent the coefficients for the influences of EoU, RL, CL, age, gender, fare price, and travel time on both trip frequency and trip satisfaction constructs; ζ_TF and ζ_TS are the error terms associated with the trip frequency and trip satisfaction constructs, respectively.

To validate the models, five model fit indices (comparative fit index [CFI], standardized root mean square residual [SRMR], root mean square error of approximation [RMSEA], goodness of fit index [GFI], and chi square/df) were considered. The determination of the cut-off values for each model’s indices was taken from previous investigations ( 71 , 75 –80). The CFI is a statistical measure that assesses the degree to which a proposed model fits the observed data in comparison to a baseline model. A CF) value that is near to 1, preferably above 0.95, signifies a strong alignment between the proposed model and the empirical data. The Tucker–Lewis index (TLI) is a statistical measure used to evaluate the degree of improvement in model fit relative to a null model. A TLI value of more than 0.90 indicates a satisfactory level of fit, whereas values about equal to 0.95 are regarded as highly favorable.

The SRMR is a statistical measure used to assess the level of difference between observed and projected covariance. A SRMR result that falls below 0.08 is generally considered to be suggestive of a satisfactory fit. The RMSEA is a statistical measure that quantifies the level of difference between a given model and the population covariance matrix. A RMSEA result that is less than 0.08 (preferably less than 0.06) suggests a satisfactory level of fit. The GFI is a statistical measure that assesses the degree to which a model's fit approximates a perfect model fit.

A GFI score greater than 0.90 indicates a satisfactory level of fit. The ratio of chi-square to degrees of freedom (chi square/df) is used to assess the goodness of fit. A smaller value of this ratio suggests a more favorable fit. Values falling within the range of 2–3 are commonly seen as suggestive of a satisfactory level of conformity. For analyzing the effect of exogenous variables on trip frequency and travel satisfaction, SEM was performed. The constructed structural equation model was used to test the hypothesis.

Results

This section is divided into three parts: (i) descriptive statistics; (ii) results obtained from EFA; and (iii) results obtained from SEM.

Descriptive Statistics

A total of 403 responses were collected in the present study from BRT commuters. With respect to demographic characteristics, the average age of respondents was 29 years, while the maximum and minimum ages were 16 and 70, respectively. Some 58% of the respondents were male, and 42% were female. The mean travel time and cost value were INR 34 (USD 0.41) and 20 min, respectively. The mean value, standard deviation, and maximum and minimum values obtained for the Likert scale items are depicted in Table 5.

Table 5.

Descriptive Statistics for Likert Scale Items

Codes for response items	Statement	Mean	Standard deviation	Minimum	Maximum
TF1	I often use BRT services for work/education trips	2.44	0.84	1	3
TF2	I often use BRT services for shopping trips	2.40	0.88	1	3
TF3	I often use BRT services for recreational trips	2.39	0.88	1	3
EoU1	I find it easy to use BRT since it is very accessible to my home and workplace	2.48	0.75	1	3
EoU2	I find it easy to use BRT since the first- and last-mile connectivity is good	2.40	0.84	1	3
RL1	Buses arrive at scheduled time	2.60	0.76	1	3
RL2	Buses reach the destination at scheduled time	2.61	0.75	1	3
RL3	While transferring between the BRT services, waiting time is as per schedule	2.39	0.88	1	3
CL1	Seating space and ride quality is comfortable in BRT	2.73	0.63	1	3
CL2	I feel that it is more comfortable to use BRT compared with driving a private vehicle	2.64	0.72	1	3
TS1	I am satisfied with the existing BRT services when I am traveling at peak hours, i.e., 8–10 a.m. and 5–8 p.m.	1.91	0.64	1	3
TS2	I am satisfied with the existing BRT services when I am traveling at non-peak hours	2.13	0.72	1	3

Note: RL = reliability; EoU = ease of using the service; CL = comfort level; TF = trip frequency; TS = travel satisfaction; BRT = bus rapid transit.

The participants generally demonstrate a moderate degree of consensus in their comments. Table 5 shows that the mean for using the Bhopal BRT during non-peak hours is higher than that for peak periods. This can be attributed to the increased demand for BRT at peak hours, which may result in a lack of seating spaces during peak hours. The mean values of ease of using the service indicate that respondents find it reasonably convenient to reach the BRT stations from their residences and businesses and value last-mile connectivity. The reliability (RL1, RL2, RL3) and comfort (CL1, CL2) aspects of BRT services exhibit comparatively elevated ratings, suggesting that respondents usually perceive the buses as being punctual, arriving at their destinations on schedule, and offering comfortable seating and a smooth riding experience.

Exploratory Factor Analysis

As discussed in the Research Hypothesis and Approach for Data Analysis section, using varimax rotation under the orthogonal method, a total of three factors were derived from seven items. The values from the KMO test are shown in Table 6. Bartlett’s values were found to be less than 0.01. The factor loading for each of the items and the factor loading from each of the items are depicted in Table 6. The MSA value represents the measure of sampling adequacy. In the context of the KMO test and the Bartlett’s test of sphericity, “uniqueness” refers to the proportion of variance in a variable that is not shared with other variables in the dataset. Table 6 depicts the factor loading obtained from the EFA and KMO test results.

Table 6.

Factor Loadings Obtained from Exploratory Factor Analysis and Kaiser–Meyer–Olkin Test Results

Codes for response items	Factor 1	Factor 2	Factor 3	Uniqueness	MSA
RL3	0.657	na	na	0.489	0.733
RL1	0.646	na	na	0.536	0.769
RL2	0.63	na	na	0.566	0.762
EoU1	na	0.779	na	0.342	0.597
EoU2	na	0.769	na	0.379	0.599
CL2	na	na	0.997	0.002	0.541
CL1	na	na	0.523	0.665	0.637

Note: MSA = measure of sampling adequacy; RL = reliability; EoU = ease of using the service; CL = comfort level. ‘na' represents not applicable values considering that only value above 0.3 were considered for checking the correlation between an item and a construct.

Furthermore, no correlation was found between the derived constructs. As shown in Table 6, the three constructs can be termed as EoU (alpha value: 0.769), RL (alpha value: 0.712), and CL (alpha value: 0.702). It was found that all constructs (RL, EoU, and CL) could be considered as latent factors for further analysis.

Structural Equation Modeling

The maximum likelihood was utilized to get the model estimates. The statistical measures for the model goodness of fit are provided in Table 7.

Table 7.

Obtained Value for Model Fit Indices

Model fit indices	Obtained value
CFI	0.953
TLI	0.921
SRMR	0.042
RMSEA	0.076
GFI	0.991
Chi square/df	3.69

Note: CFI = comparative fit index; SRMR = standardized root mean square residual; RMSEA = root mean square error of approximation; GFI = goodness of fit index; TLI = Tucker–Lewis index.

As shown in Figure 2, the three exogenous constructs (CL, EoU, and RL) were derived from seven items, while the endogenous constructs (TF and TS) were derived from five items. The factor loadings for these five derived latent factors are depicted in Table 8. From Table 8, it is observed that all the response items have positive signs and significant factor loadings. The regression coefficients obtained for the constructed model are shown in Table 8.

Table 8.

Factor Loading from the Constructed Structural Equation Model

Latent	Indicator	Standard error	Standardized factor loadings
Comfort level	CL1	0.10	0.580
Comfort level	CL2	0.20	0.583
Ease of using the service	EoU1	0.10	0.666
Ease of using the service	EoU2	0.12	0.945
Reliability	RL1	0.20	0.681
	RL2	0.09	0.643
	RL3	0.07	0.702
Travel satisfaction	TS1	0.11	0.543
Travel satisfaction	TS2	0.17	0.598
Trip frequency	TF1	0.04	0.818
	TF2	0.04	0.998
	TF3	0.04	0.996

Note: All the obtained factor loadings are significant at p<0.01

A factor loading value greater than 0.5 is considered suitable to extract a latent variable ( 81 – 83 ), which, in this case, is fulfilled—all values in Table 9 are above 0.5.

Table 9.

Regression Coefficients

Outcome	Predictor	Standard error	Standardized values
Trip frequency	Ease of using the service	0.05	0.53***
Travel satisfaction	Ease of using the service	0.12	0.36***
Trip frequency	Reliability	0.06	0.05**
Travel satisfaction	Reliability	0.19	0.52***
Trip frequency	Comfort level	0.05	−0.20
Travel satisfaction	Comfort level	0.17	0.60***
Trip frequency	Fare price	0.73	−0.16**
Travel satisfaction	Fare price	1.97	−0.19**
Trip frequency	Travel time	1.94	−0.15**
Travel satisfaction	Travel time	5.26	−0.14**
Trip frequency	Travel satisfaction	0.18	0.48***
Trip frequency	Age	0.88	−0.03
Travel satisfaction	Age	2.41	0.16
Trip frequency	Gender	0.04	0.05
Travel satisfaction	Gender	0.12	−0.1

***

P < 0.01; **P < 0.05; *P < 0.1.

From Table 9, it can be assessed that the demographic variables (gender and age) were not found to have any significant effect on travel satisfaction, as well as the trip frequency of respondents. The rest of the variables were found to affect the travel satisfaction and trip frequency of respondents significantly.

Discussion and Conclusion

The findings of this study highlight the significant and unfavorable impact of fare prices and travel time on both trip frequency and travel satisfaction. Hypotheses H11, H12, H13, and H14 have been confirmed, which aligns with our logical expectations. In Bhopal, the majority of BRT riders belong to the informal worker, low-income, and captive consumer groups. These riders are particularly sensitive to fare increases, and any further rise in fares would undoubtedly make the BRT system less appealing to them, as supported by Kholodov et al. ( 84 ) and Paulley et al. ( 85 ).

In India, where public transportation serves as the predominant means of conveyance for individuals across all social classes, urbanization and population growth present formidable obstacles. Migration becomes an unavoidable phenomenon, which places additional burdens on urban areas and leads to urban encroachment, increased property values in proximity to city centers, extended travel distances, and reduced public transportation accessibility. The urban poor are disproportionately affected by this escalating urbanization, as they rely heavily on public transportation ( 86 – 88 ).

Moreover, the congestion issues are exacerbated by the increase in private vehicle usage, which has a significant impact on buses as a result of their space demands. The diminished appeal of buses among affluent individuals, who prefer alternative modes of transportation, results in a decline in bus ridership. This presents transit agencies with a financial obstacle and puts them in a situation wherein they are forced to reduce the service frequency and compromise the service quality ( 89 ). It is apparent that the predominant users of public transport in India are individuals with low incomes and those engaged in informal labor when this is linked to our findings with respect to fare price and travel duration ( 90 ). Therefore, the urban poor ultimately endure the brunt of this circumstance.

Through the developed structural equation model, it was found that the reliability of BRT services has a significant and positive effect on travel time and travel satisfaction (H7 and H8) ( 91 ). Also, the ease of using the service was revealed to have a positive impact on travel satisfaction and trip frequency (H5 and H6). This observation could be explained by the negative elasticity associated with travel time. Since both criteria in the “ease of use” category correspond to accessibility and first/last-mile connectivity, it can be assessed that if these characteristics improve, commuters can save considerable amounts of travel time and money (with better accessibility, they do not have to take another mode). The existing literature strongly establishes the substantial and positive influence of accessibility and first/last-mile connectivity on travel satisfaction and trip frequency ( 92 – 96 ).

The item RL3 (when transferring between BRTs, waiting time is as per schedule) had the highest positive association with the reliability factor, showing that waiting time for BRT services is crucial for travel satisfaction and trip frequency. Recognizing the significance of travel time for individuals who are often compensated based on their work hours, policymakers should consider improving last-mile connectivity (which will reduce the overall journey time), service frequency, and flexible and dynamic routing options. In general, trip chaining is mostly linked with informal workers ( 97 – 99 ). The level of comfort was found to have a significant and positive effect on travel satisfaction (H10), but an insignificant effect on travel frequency (H9). This can be linked to the majority of public transit passengers being captive users, meaning therefore that they will use the service even if it means sacrificing comfort.

Currently, the accessibility of public transport (with respect to BRT) in Bhopal is below average, especially among those living in slums who are captive users of BRT services. Since route alignment and station placement of the Bhopal BRT are in proximity to higher-income localities ( 100 ), this issue of accessibility of the Bhopal BRT needs to be addressed by the public transport authorities considering the socioeconomic profile of potential public transport commuters. Furthermore, the city public transport authorities can also focus on using real-time demand prediction and advanced reservations, and also explore demand-responsive transit services ( 101 ), well-maintained pedestrian infrastructure for a better walking experience ( 102 ), and an efficient and dense public bicycle sharing system, preferably in lower-income residential areas ( 103 ). As the Bhopal BRT is a single corridor, a complementary feeder system can provide adequate coverage for this corridor type ( 28 ).

Finally, the constructed structural equation model also verified the final hypothesis (H15: Travel satisfaction has a significant and positive effect on trip frequency). In fact, travel satisfaction has a huge impact on trip frequency. Favorable travel experiences increase the probability that individuals will decide to travel more often in pursuit of similar pleasurable encounters. In addition, a high level of travel satisfaction encourages individuals to embark on journeys more frequently without reservations or qualms, thereby mitigating potential deterrents. It may therefore be inferred that improving trip satisfaction is a critical step in increasing public transportation ridership ( 57 , 59 , 104 ).

Scope and Future Directions

The current study focused on the Bhopal BRT system; however, to generalize the applicability of the model developed in this study, a similar type of study in other Indian cities with BRT systems is required. In future research, the effect of other socioeconomic variables, such as income, marital status or household structure, driving license availability, and job profile, can be considered. The incorporation of economic indicators, such as income level, smartphone ownership, and vehicle ownership, has the potential to enhance the linkage between findings and the development of a more robust research trajectory pertaining to the impact of inadequate BRT services on disadvantaged urban populations (low-income populations). Existing research studies has shown that individuals who utilize public transportation are significantly more exposed to personal noise and, as a result, report decreased levels of travel satisfaction ( 105 , 106 ). Furthermore, the impact of adverse externalities associated with urban transportation, including as air pollution, traffic congestion, road accidents, and noise pollution, on the level of satisfaction experienced by users of BRT or other forms of public transportation remains an understudied topic in the context of middle- and low-income countries.

Using the findings of the current study as a foundation, future studies based on stated preference surveys can be conducted to examine the effect of providing free public transportation on ridership. As a part-analytical framework, the present study primarily focuses on capturing the quantitative aspect of travel time rather than delving into the perception of travel time convenience or savings. However, future studies may consider incorporating assessment of the perception of BRT users toward the travel time and fare price of the service, since the perception of the final user may be different from real measurements. Also, the present study did not consider the effect of trip purpose on travel satisfaction. This aspect is worth incorporating, considering that satisfaction related to the BRT (or any public transit) varies among trip purposes. For instance, for a worker using transit to commute to work during the peak hour, the reliability of buses reaching the stops on time is a major issue. In contrast, the intervals between buses would be more critical for a student. Furthermore, future research can also consider the safety perception or the stop conditions as an exploratory variable.

The present study focused on analyzing the factors affecting travel satisfaction for internal combustion engine (ICE) buses; however, with the advent of technology, electric vehicles are scaling in India in private vehicles as well public transportation ( 107 ). In the future, comparative studies on the effect of selected factors on travel satisfaction for ICE versus electric buses can also looked into. Finally, extensive research has been conducted on the effects of pandemics on freight in the Indian context ( 108 – 110 ); however, there is a paucity of such studies specific to the BRT system in India.

Supplemental Material

sj-docx-1-trr-10.1177_03611981241230503 – Supplemental material for Travel Satisfaction of Bus Rapid Transit Users in A Developing Country: The Case of Bhopal City, India

Supplemental material, sj-docx-1-trr-10.1177_03611981241230503 for Travel Satisfaction of Bus Rapid Transit Users in A Developing Country: The Case of Bhopal City, India by Aditya Saxena, Binayak Choudhury and Premjeet Das Gupta in Transportation Research Record

Footnotes

Acknowledgements

The authors would like to thank the officials of Bhopal Smart City Development Corporation Limited (BSCDCL) and Bhopal City Link Limited (BCLL) for sharing their insights and latest data on BRT in the city and Mr. Vipul Parmar for helping out in the preparation of the study area map.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: A. Saxena; software: A. Saxena; data collection: A. Saxena; analysis and interpretation of results: A. Saxena, B. Choudhury, P. Das Gupta; draft manuscript preparation: A. Saxena, B. Choudhury, P. Das Gupta. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Aditya Saxena

Premjeet Das Gupta

Supplemental Material

Supplemental material for this article is available online.

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