Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country

Abstract

Motorization has been escalating rapidly in developing countries, posing a severe challenge to sustainable urban mobility. In the past two decades, car-sharing has emerged as one of the most prominent alternatives to facilitate smart mobility solutions, thereby helping to reduce air pollution and traffic congestion. However, before its full-scale deployment, it is essential to understand the consumers’ acceptance of car-sharing systems. This study aimed to assess the public perception and acceptance of the car-sharing system through a stated preference (SP) questionnaire in the city of Lahore, Pakistan. The collected data contained detailed information on various service attributes of three alternative modes (car-sharing, private car, and taxi) in addition to the sociodemographic attributes of respondents. Data analysis and interpretation were performed using econometric models such as the Multinomial Logit Model (MNL), the Nested Logit Model (NL), and the Random Parameter Logit Model (RPL). Study findings revealed that several generic attributes such as travel time, travel cost, waiting time, and privacy were predicated as significant influential factors towards the adoption of car-sharing. Sociodemographic attributes, including age, education, monthly income, the individuals who had driver’s licenses, and frequency of travel in a week, were also found to be significant. The findings of the current study can provide valuable insights to stakeholders and transportation planners in formulating effective policies for car-sharing.

1. Introduction

Globalization and digitization encourage greater interaction, and the emergence of new economic structures such as the collaborative economy or sharing economy can pave the way for new business models [1]. The sharing economy paradigm and transportation demand management strategies aim to maximize and optimize resource usage while lowering costs and increasing accessibility to products, goods, and public services. Shared mobility is the segment of the sharing economy that describes the communal use of a vehicle (van, car, bicycle, scooter, etc.). It is regarded as one of the most promising concepts that has the potential to reshape future urban mobility. It has been related with relieving traffic congestion and air pollution [2]. It also deals with understanding how individuals make transportation decisions and assists people in using existing infrastructure for transit, car-sharing, ride-sharing, walking, biking, and teleworking [3,4,5]. The business models of shared mobility are car-sharing (short-term auto use), ride-sharing (carpooling and vanpooling uses for the common origin and destination), ride-sourcing (transportation network companies use a driver of private vehicles connected with passengers and provide prearranged and on-demand service), and e-hail services (which connect passengers with a conventional taxi using a smartphone). Shared mobility provides opportunities to bridge equity gaps rapidly and cost-efficiently. Shared mobility can increase mobility for users who are unable to access private vehicles and enable those who own cars to drive them at higher occupancy, for fewer trips, or forego ownership altogether, potentially reducing household transportation expenditures while providing more transportation options [6,7].

Car-sharing is one of the novel forms of shared mobility that allows travelers to access a mode of transportation for short-term access when they need it. Car-sharing users deposit a registration fee or monthly fee to become a member of a fleet distributed throughout a city. Passengers typically pay an hourly and per-kilometer charge based on their vehicle use [6,8]. The systems offer different kinds of sharing models to users, such as one-way car-sharing, two-way or round-trip car-sharing, and free-floating car-sharing [9]. The first model of car-sharing appeared in 1948 in Zurich, Switzerland, and later it was propagated in many cities worldwide [10]. Twenty-six countries rolled-out car-sharing schemes in 2010 as a novel mode of transportation to minimize transportation costs and negative external social impacts such as congestion, energy consumption, and carbon dioxide (CO₂) emissions [11]. According to a previous study [8], globally, car-sharing members and vehicles are spreading rapidly at a rate of 35% and 30% per annum, respectively. In addition, a study reported that it would reach 12 million in 2020, compared to 2.3 million in 2013. Another study by Kireeva et al. [12] reported that there were only 0.35 million shared vehicles in the world in 2006, but their number rose to 7 million by the end of 2015 and it could reach 36 million by 2025.

Rapid population growth and rising urbanization in recent decades have resulted in ever-increasing travel demand in major cities worldwide, posing serious challenges to urban transport networks and transportation alternatives [13,14,15]. In developing countries such as Pakistan, the vehicle growth of passenger cars and two- and three-wheelers has increased exponentially and is depicted in Figure 1. Lahore, the second biggest megacity in the country, has been witnessing an average urbanization and motorization rate of nearly 3% and 17% per year, respectively [16]. The city has a total population of over 11 million and a land area of 1792 km² [17]. According to a previous study [17], the annual cost of traffic congestion in Lahore is estimated to be 519 million USD. Previous studies [17,18] affirm that Lahore has numerous problems such as traffic congestion, air pollution, parking, and a disintegrated land-use system. Lahore city recorded a Particulate Matter (PM) rating of 188, which classifies the city as the “unhealthy” category of air quality. Excessive vehicular emissions are among the main causes of air pollution in the city [19]. According to some researchers [17,20], gender disparity and inequality were identified in Pakistan’s transportation sector. These disparities result from varying cultural values, different beliefs, and privacy concerns.

Private cars provide more comfortable, flexible services and personal freedom, yet they remain parked for approximately 95% of the time and carry fewer than two passengers on average when moving [21]. Aside from low occupancy, private cars are universally criticized for major negative externalities such as air pollution, traffic crashes, congestion, and a significant impact on transportation system quality. As a result, these problems have a significant impact on the country’s economy [17]. Effective management of cities’ infrastructures is the only solution to ensure adequate living conditions for the majority of the world’s population. The advancements and evolutions in new technologies are bringing changes in transportation systems as well. All types of shared mobility rely on technology, and car-sharing is no exception. Smart locks enable renters to use a car without meeting the owner. According to some researchers [22,23,24], autonomous vehicles, the Internet of Things (IoT), energy storage, and cloud technology will be the key drivers of the smart urban transportation system in the coming era. The breakthrough in the public transportation system, “Mobility-as-a-Service (MaaS),” will provide different packages to travelers as per their needs and seamless travel for multimodal trips [22,25]. The sustainable and continuous development of public transport systems ensures robust and resilient transport and economic activity whilst improving the urban environment. Through technological improvement, cities can increase the competitiveness of public transport, promote equality, and pursue multimodal shifts to greener solutions [26].

Increased car ownership in developing countries, specifically in South Asia, has led to extensive social and environmental issues and unsustainable growth patterns. In recent years, car-sharing has emerged as a promising approach to changing the way of travel and public preferences towards the adoption of different travel modes. Psychological, socioeconomic, infrastructure, and service attributes are the key influential factors of transport mode choice. With the widespread adoption of car-sharing, academic research in this regard has grown in recent years; however, the available studies in the field are far from saturated, and additional empirical case studies are urgently required. Furthermore, most of these studies focused on shared mobility were performed in developed countries (Europe and America), with travelers’ perspectives in the developing countries, including Pakistan, remaining largely unexplored. In Pakistan, the car-ownership ratio in terms of total population or individual household varies significantly from that in developed countries. The impacts of car-sharing on private car ownership and other modes of transport are likely to vary across different countries due to the differences in the motorization process. Additionally, the debate remains as to whether this mode of transport can draw the consumer’s attention under various travel scenarios, privacy, and safety concerns. There is also a dearth of research on car-sharing that addresses the privacy and security issue, particularly in the context of developing countries. By including this critical predictor, one can obtain interesting results from low-income developing countries where high rates of social inequality and gender disparity exist. In view of the aforementioned motivations and highlighted gaps in the literature, the present study is dedicated to exploring the influential factors (both socioeconomic and service attributes) towards car-sharing adoption intentions for the case study in Lahore, Pakistan.

The purpose of this study is two-fold: first, to ascertain public opinions, attitudes, and preferences regarding car-sharing systems in Pakistan’s megacity and second, to investigate the factors that influence car-sharing mode by including a privacy and safety variable in an SP survey in order to gain better insights from a developing country context with diversified culture. Car-sharing services are not yet available in Pakistan; therefore, stated preference surveys and econometric models are employed to thoroughly assess travelers’ behavior. This new mode of shared mobility will assist stakeholders and policymakers in developing suitable strategies for successfully implementing car-sharing, particularly in low-income nations and throughout the world.

The rest of the paper has been structured into five sections. Section 2 presents the related literature review describing the key factors influencing car-sharing systems and an overview of past studies. Section 3 provides the details on the stated preference survey, study area, data collection, setting up of stated choice experiments, sample compositions, and econometric modeling approaches. Section 4 describes the study descriptive statistics and model estimations results. Section 5 explains the critical discussion in light of comparative literature. Finally, Section 6 provides the study conclusions, limitations, and perspectives for future research.

2. Literature Review

Numerous researchers have assessed the advantages of car-sharing using econometric models, structural equation models, and mathematical simulation [14,27,28,29,30]. Research has revealed that affordability, accessibility, time savings, and vehicle diversity all have a substantial impact on citizens’ adoption of car-sharing schemes [31]. A study conducted on the car-sharing impact on vehicle ownership shows that, on average, every car-sharing vehicle could reduce by around 15.3 the number of personal cars in Philadelphia, in the United States. The reduction of vehicle ownership leads to decreased air pollution and congestion and increased available parking spaces [32]. Another study conducted in the city of Toronto, Canada found that 55% of the respondents expressed willingness to forgo purchasing a new vehicle after getting a subscription to a car-sharing service, while 29% gave up their private cars [33]. Similarly, a study conducted in England and Wales for round-trip car-sharing indicated that car-sharing could replace on average of 8.6 private vehicles [34]. One of the main advantages of becoming a member of a car-sharing system is to get rid of fixed, maintenance, repair, and parking costs. Car-sharing contributes to more effective and rational mobility, particularly for those who do not have a vehicle [8,9]. A previous study [9] suggested that car-sharing could decrease CO₂ emissions.

Car-sharing services have been supported by many countries as a solution to societal problems of air pollution, heavy traffic congestion, and vehicle kilometers/miles traveled (VKT/VMT) [25]. Car-sharing networks are provided by well-known companies such as Swiss Mobility, German Stat auto Drive, and American Zipcar in development-intensive areas of various cities [11,35]. Additionally, Kim et al. [23] discovered that lowering deposit requirements, monthly membership fees, and/or hourly rates are more effective at attracting new car-sharing users than lowering distance-based or fuel expenses. In a stated choice experiment conducted in South Korea, it was found that time savings are more important than cost savings and social value [36]. Car-sharing demand is greater in urban areas than in rural areas [37]. This can be due to factors that make car ownership more difficult and expensive in urban areas, such as a scarcity of parking. Car-sharing adoption is facilitated by the availability of alternative forms of transport and the dearth of parking spaces [38]. Increased technology integration into car-sharing services improves the customer value proposition by making the service more flexible and convenient [31]. Kim et al. [39] confirmed that as waiting time increases, the likelihood of using car-sharing decreases. Cars must therefore be strategically located to serve a larger pool of customers in the case of access-based services. Previous research assessed the effectiveness of one-way car-sharing and provided an optimization model for operational challenges concerning vehicle movement. Increased fleet size, in the proposed model, results in reduced time required for relocation but also results in increased costs. Additionally, the same study suggested that fleet size may be lowered by increasing reservation time to approximately 30 min. However, this may result in decreased demand since user privacy may be compromised in some situations by the requirement to indicate a return time range for vehicle relocation predictions [40].

Clark and Curl [41], in their study of bicycle and car-sharing programs in the United Kingdom, emphasized the relevance of station placement in attracting potential and willing customers. According to a previous study [42], spatial and temporal factors in the use of shared cars is influenced by station-level turnover and the interaction between transit and car-sharing. Car-sharing companies are encouraged to maximize efficiency when determining optimal locations for car-sharing stations, particularly in densely populated areas where cars occupy a substantial quantity of expensive parking space. Wu et al. [43] proposed a novel choice-based framework for optimizing the dynamic pricing of Free-Floating Car-sharing Networks (FFCS) operators under various forms of consumers’ risk preferences. The numerical study demonstrated the adequacy of the proposed approach to cover a range of user attitudes towards risk, i.e., neutrality, risk aversion, and risk-seeking. It was reported that FFCS operators may suffer significant revenue loss in case of incorrect estimation of user risk preference. Ma et al. [2] suggested in their study that a Variational Inequality (VI) model to solve the Ridesharing User Equilibrium (RUE) problem on an urban transportation network based on Origin-Destination (OD) caused a surge in pricing strategy. Numerical simulations were conducted to evaluate the effectiveness of the proposed solution algorithm and it was found that the proposed algorithm can be used for solving complex and large-scale problems with satisfactory computational efficiency.

Extensive research has discovered that the propensity to use car-sharing is frequently impacted by sociodemographic, regional, and socioeconomic characteristics such as mobility patterns, family decisions, the cost and quality of other forms of transport, as well as consumer segmentation, travel time, travel cost, walking time, and waiting time. However, very few of them have examined safety and privacy concerns. Pakistan’s demographic and socioeconomic conditions are significantly different from those of developed countries, with distinct beliefs, cultures, and social functions. This study expands on the influence of car-sharing in diverse cultures with high levels of social inequality. The current study incorporates the privacy factor with primary factors (i.e., travel time, travel cost, and waiting time) in order to ascertain travelers’ perspectives from developing countries such as Pakistan. In addition, the introduction of a car-sharing system into existing public transport systems has potential impacts on mobility behaviors and may replace private cars. The previous studies [17,44] indicate that public behaviors are changing with the introduction of new shared mobility services. Thus, this study bridges the literature gaps to assess public perception before implementing car-sharing programs since this will provide significant directions and guidance.

3. Methodology

3.1. Stated Preference Survey

A stated preference questionnaire was developed and designed to collect data on car-sharing in Lahore, which were not yet available. SP surveys are mainly conducted to study emerging transportation alternatives to elicit preferences from potential users [17]. The advantage of the SP survey is that each individual can get multiple hypothetical scenarios; resultantly, fewer respondents are needed in data collection administration [8,28,35]. In this study, the SP survey was split into three phases. Firstly, the respondents were presented the introduction about the proposed car-sharing and its advantages, shown in Figure 2. In addition, questions were asked about awareness, perceptions about car-sharing, driving licenses, possession of a private car, and frequency of travel in a week. Secondly, the respondents were presented with choice scenarios of the SP survey for different trip purposes such as working, shopping, and recreational trips. Lastly, the respondents were asked about socioeconomic characteristics, including gender, age, marital status, monthly income, employment, and educational level.

3.1.1. SP Survey Design Considerations

Generating an effective SP survey design can be accomplished in a variety of ways. Thus, the researcher’s goal is to choose the most appropriate way to achieve the experiment’s objective. To obtain this, a range considerations for SP surveys must be taken into account.

• Level balance: The level balance means maintaining the attribute levels of each attribute, so they come about with equal frequency. It reflects the desired property, although it can influence the statistical efficiency of the design [45].
• Design type (orthogonality): The design type selection for scenario generation is more important either to choose a full factorial design or partial factorial design. Full factorial designs estimate all possible combinations and make the design more optimal. In contrast, fractional factorial design chooses the random selection of a full factorial design. The design should also preserve the orthogonal properties. The main aim of orthogonality is for independent variables to remain uncorrelated and to reduce multicollinearity [46].
• Minimum overlap: The attribute level should not repeat itself in the choice sets. The minimum overlap means that the occurrence of the attributes levels itself in each scenario remains at a minimum. Overlap provides a means for simplifying choice questions by reducing the number of attribute differences that respondents must evaluate [47].
• Utility balance: The design of the fractional factorial experiment of the SP survey should present an accurate estimation of the number of choices set. There should be a clear difference and a vivid set of questions for each set of respondents [48].

3.1.2. Experimental Design of SP Survey

The purpose of experimental design is to break up the products or services into a set of attributes and levels. The researcher uses the experimental design to allocate the attribute levels and values amongst the choice scenarios and then distribute these choice scenarios to the respondents. This would aid in treating each scenario of the experiment and ensure the best possible outcome [49]. The present study considered three alternatives such as car-sharing services, private cars, and conventional taxis. Private car ownership is high in Lahore, so the private car was selected to check the influence of car-sharing on it [16,18]. This study considers a one-way car-sharing system, which means that a traveler can utilize the service without returning the vehicle to its origin, and it can be parked at any designated station [50]. The mode’s service attributes considered include waiting time, travel time, travel cost, parking cost, and privacy. Privacy was added in this study, as it has rarely been studied in the car-sharing context in previous studies. In addition, the literature has also argued that people are highly concerned with these attributes in developing countries such as Pakistan [20,51]. Levels of privacy are categorized as low, medium, and high. The travel cost for a private car was considered out-of-pocket money (gasoline, tolls, routine maintenance such as washing, cleaning, and tire puncture repair) [52]. The travel cost for the taxis was calculated from the existing fare. The travel cost for car-sharing was calculated from a previous study [53]. The travel time for private car and taxis was calculated based on the average speeds. The travel time of car-sharing was calculated from the previous study [52]. The waiting time for car-sharing and taxis was calculated as time spent waiting for the vehicle or getting access to the vehicle. The parking fee for the private car was calculated using the current parking rates in Lahore [54]. There were three alternatives, four attributes, and three levels of each attribute. Car-sharing, taxis, and private cars have four attributes and three levels $(3^4\times 3^4\times 3^4=3^{12})$.

A Taguchi fractional factorial design was implemented for choice experiments using Minitab software, version 19.1, and a total of 27 travel choice scenarios were generated [17]. Minitab is an excellent statistical analysis tool developed by researchers at Pennsylvania State University, USA. To get realistic and reliable responses, the choice experiments were broken down into three subsets. It means that each individual faces nine scenarios of choice experiments. To get plausible responses, the nine scenarios were randomly selected from the available 27 sets of sceneries. The values of waiting time, travel time, parking cost, privacy, and travel cost in each scenario are pivoted to the mentioned levels in

Table 1. Experimental design of alternatives, attributes, and their levels (PKR = Pakistani Rupees, 1 PKR = 0.008 USD).

Alternatives	Attributes
	Levels	Travel Time (Min)	Travel Cost (PKR)	Waiting Time (Min)	Parking Cost (PKR/hour)	Privacy
Car-sharing	1	20	200	3	0	Low
	2	25	250	6	0	Medium
	3	30	300	9	0	High
Taxi	1	25	350	4	0	Low
	2	30	400	8	0	Medium
	3	35	450	12	0	High
Private Car	1	15	180	0	0	Low
	2	20	230	0	20	Medium
	3	25	280	0	50	High

. By pivoting on the levels of attributes of alternatives, the SP experiment is more realistic and enables preferences to be expressed in a context close to the respondent’s actual behavior. The survey’s nine sets of scenarios were established to consider the design criteria for assigning attribute levels to the alternatives. A snapshot of SP survey choice scenarios is presented in Figure 3, Figure 4 and Figure 5. The remaining choice scenarios are attached in the “Appendix A”. The SP scenarios in this study asked participants to consider and potentially adjust their mode of transportation choices for daily trips while imagining the following modes of transportation were available and assuming they had the same living arrangements as they do now (e.g., the same home, the same location for work, jobs, shopping, and recreational activities, etc.) [46]. The experimental design of alternatives, attributes, and their levels is shown in Table 1.

3.2. Data Collection

The targeted area of this study was Lahore, Pakistan. Lahore is the capital of the Punjab Province and is the second largest city in the country. The city hosts over 11 million people and has a land area of 1792 km². It currently has three distinct modes of public transport: the Punjab Metro Bus Authority (PMA), the Lahore Transport Company-operated buses, and the Qing qi Rickshaws. Approximately 135,000 people use these services daily [17,18,55]. The city has a Gross Domestic Product (GDP) per capita of about 5855 USD in 2017. Lahore is a focal point for educational, medical, and industrial employment opportunities, as well as a collection of allied amenities [17]. The public transportation of Lahore is insufficient and ineffective due to disjoined land use and poor planning of the transit network [18]. The map of the study area (the city of Lahore) is shown in Figure 6.

The questionnaire was conducted through an online Google form, where hyperlinks were generated to collect data. The questionnaire was randomly distributed among the respondents via Facebook, WhatsApp, and emails in the entire city. The data were gathered with the assistance of colleagues, friends, and university students who had been introduced to the contents and objectives of the questionnaire. Additionally, friends and colleagues helped to disseminate questionnaires across multiple WhatsApp and Facebook groups targeted to Lahore residents only to obtain a random sample and avoid bias in the sample [51]. Previous studies have used the same approach for collecting responses in Lahore, Pakistan [17,56]. To check the survey’s adequacy, credibility, and validity, the current study used a 45-respondent pilot survey sample. The results of the pilot surveys indicated the expected signs of variables, and the survey then proceeded to collect the final survey data. The survey was completed in November 2018. A total of 265 responses were received. After discarding 23 responses from the final survey that were incomplete and respondents who chose the first choice for all questions, the remaining 242 valid responses were used for final data analysis.

3.3. Survey Sample

The breakdown of socioeconomic variables is shown in

Table 2. Sample compositions and socioeconomic characteristics of surveyed respondents.

Explanatory Variables	Category	Percentage of Sample
		(N = 242)
Gender	Male	72%
	Female	28%
Age groups (Years)	18–29	40%
	30–39	35%
	40–49	15%
	50–60	7%
	Over 60	3%
Education level	Under high school	10%
	High school	28%
	Bachelor’s degree	46%
	Master’s degree and above	16%
Occupation	Student	35%
	Unemployed	4%
	Employed	53%
	Entrepreneur	8%
Monthly income (PKR)	<30,000	38%
	31,000–60,000	22%
	61,000–90,000	27%
	91,000–120,000	8%
	Over 120,000	5%
Marital status	Married	39%
	Single	61%
Frequency of travel in one week	0–2 days	43%
	3–5 days	57%
Car ownership	Have	59%
	Do not have	41%
Driving license	Have	58%
	Do not have	42%

. The male respondents contributed 72% of the total sample size, while the rest were females. Regarding employment status, the proportion of students was 35%, whereas the employed proportion was 53%. In the context of monthly income, the distributions of lower income (0–30,000 PKR) were 38%, middle income had a 22% part in the sample (31,000–60,000 PKR), and high-income represented one-fourth of the sample having more than 60,000 PKR. Considering the age distribution of the sample, the respondents aged 18–39 comprised three-fourths of the sample. In contrast, the part of older individuals, 40–60 years old, was 22%. In terms of education breakdown, the respondents holding a Bachelor’s, Master’s, and above represented 62% while the under high school and high school were 38% of the sample.

3.4. Methods Used

In this study, econometric models such as MNL, NL, and RPL were employed for analysis. According to random utility maximization theory, each respondent will choose the alternative with the highest utility. The collected data were calibrated and analyzed by using Python Biogeme software [57], an open-source statistical package for the maximum likelihood estimation of parametric models, with a specific emphasis on discrete choice models.

The transportation alternative choices were evaluated using econometric models such as MNL, NL, and RPL models. Numerous studies have used these models for predicting and analyzing travelers’ behavior, route choices, and mode choices [8,27,28,58,59]. These econometric models follow the principles of random utility theory, which states that a user chooses an alternative over a set of alternatives that give maximum satisfaction. The choice set in the random utility maximization theory of discrete choice analysis shows three characteristics: mutually exclusive, collectively exhaustive, and finite set. A set of alternatives must have the following characteristics to fit within the discrete choice models framework [60].

Discrete choice models typically encompass the following elements in the choice process and concerns of behavioral choice of discrete alternatives [52,60].

• Decision-makers: Decision-makers can be individual persons or households. Governments or firms or any decision-making units that possess preferences or tastes over alternatives.
• Alternatives: Alternatives are the products or services or course of action over which decisions are being made. The set of alternatives should be feasible for decision-makers. For example, private car, public transport, taxi, etc.
• Attributes: Attributes represent the characteristics, values, and properties of alternatives that make the alternatives useful, for example, travel cost, travel time, privacy, and comfortability.
• Attributes Levels: The value that is assigned to attributes is called attributes levels. The levels are fixed as realistic market values which capture the individual perception. For example, the fare level takes the average of fares concerning time. This depends on the analyst’s decision.
• Decision rule: The decision rule refers to the principles and criteria used to assist the traveler in making a decision. A discrete choice model uses the random utility maximization theory as the decision rule.

The graphical representation of econometric choice models is shown in Figure 7.

3.4.1. Multinomial Logit Model

The Multinomial Logit Model (MNL) is often used to evaluate choice behavior in travel, such as drivers’ mode selections route choices [8,58]. MNL is the simplest, most fundamental, and simplest mathematical model for predicting mode selection decisions. The MNL model is used to validate the mode choice base model. The MNL model constitutes two components of utility: systematic utility and random error component. The Equation (1) is given below:

$$U_{in}=V_{in}+{\epsilon }_{in}$$

(1)

$$Pr(in)=\frac{e^{\left(V_{in}\right)}}{{\sum }_{j=1}^J{e}^{\left(V_{jn}\right)}}.$$

(2)

where $U_{in}$ is the total utility of the alternative i to the individual n, ${\epsilon }_{in}$ is the random error component, and $V_{in}$ is the systematic utility function.

The MNL formula for the computation of probability of decision individuals selecting by alternative i is given in Equations (2) and (3).

$$V_{in}={\beta }_{i0}+{\beta }_{alt}{X}_n+{\beta }_{ind}{q}_{ni}$$

(3)

where, $X_n$ is the alternative specific variables, $q_{ni}$ is the individual-specific variables, ${\beta }_{\mathrm{alt}} \mathrm{and} {\beta }_{\mathrm{ind}}$ are the vectors of alternatives and individual’s attributes, and ${\beta }_{\mathrm{o}}$ is the alternatives constant.

3.4.2. Nested Logit Model

The nested logit model is mostly used to capture the correlation among alternatives. This model relaxes the IIA property of the MNL model but keeps the generalized distribution. The NL models disintegrate the alternative into different clusters. Usually, those clusters have the same sharing properties [27,51]. In this study, the alternatives are split into owned (private car) and shared nests (taxi and car-sharing), which are shown in Figure 8. The mathematical form of nested logit is shown in Equations (4)–(8).

$$U_{nj}=q_{nk}+Z_{nj}+{\epsilon }_{nj} for i\in B_k$$

(4)

where the utility of choice j in set $B_k$ for individual n is: $q_{nk}$ rely only on attributes that define nest k. These attributes vary across nests but not across alternatives within each nest. $Z_{nj}$ relies on attributes that define alternative j. These variables vary over alternatives within nest k. The NL model is split into two portions:

Pj = Pr[nest holding j] × Pr[j, given nest holding j]

$$P_{in}=P_{i_n\mid B_k}{P}_{n,B_k}$$

(5)

$$P_{i_n\mid B_k}=\frac{e^{\left(Z_{in}/{\lambda }_k\right)}}{{\sum }_{j\in B_k}{e}^{\left(Z_{nj}/{\lambda }_k\right)}}$$

(6)

$$P_{n,B_k}=\frac{e^{\left(Z_{nk}+{\lambda }_k{\mathsf{\theta }}_{\mathrm{nk}}\right)}}{{\sum }_{l=1}^K{e}^{\left(Z_{nl}+{\lambda }_l{\mathsf{\theta }}_{\mathrm{nl}}\right)}}$$

(7)

$${\mathsf{\theta }}_{\mathrm{nk}}=\ln {\displaystyle \sum }_{\mathrm{j}\in {\mathrm{B}}_{\mathrm{k}}}{e}^{\left(Z_{nj}/\lambda \mathrm{k}\right)}$$

(8)

where λk is the degree of independence in random error part of the utility among the alternatives in nest k. ${\mathsf{\theta }}_{\mathrm{nk}}$ is nesting coefficient or log sum parameter or dissimilarity parameter.

Figure 8. Two-level nest structure of NL model.

Figure 8. Two-level nest structure of NL model.

3.4.3. Random Parameter Logit Model

The Random Parameter Logit (RPL) model is the more powerful and flexible model of econometric models. The main advantage of this model is that it captures the heterogeneity among the alternatives of the respondents. The RPL model relaxed all the weaknesses of the MNL model, i.e., IIA property and distribution. The random parameter of the RPL model can be estimated through various distributions such as normal, lognormal, triangular, and uniform distribution. The RPL model is widely employed for estimating public behaviors towards mode choices [28,58]. The mathematical form of the RPL model is given in Equations (9) and (10).

$$L_{in}\left(\beta \right)=\frac{e^{\left(V_{in}\left(\beta \right)\right)}}{{\sum }_{j=1}^{\mathrm{J}}{e}^{\left(V_{jn}\left(\beta \right)\right)}}$$

(9)

$$P_{in}={\displaystyle \int }{L}_{in}\left(\beta \right) f\left(\beta \right) \mathrm{d}\beta { }_{ }$$

(10)

where $f\left(\beta \right)$ is the density function, $V_{in}\left(\beta \right)$ is the systematic portion of utility which relies on random parameter $\beta$.

The utility formulation of the study of three alternatives, including car-sharing (CS), personal car (PC), and conventional taxi (TX) is given in Equations (11)–(13). The taxi was considered as a reference alternative in the model calibration.

$$\begin{array}{l}{V}_{CS}={\beta }_{10}+{\beta }_{wt}\times WT+{\beta }_{tt}\times TT+{\beta }_{tc}\times TC+{\beta }_{priv}\times PRIV+{\beta }_{age}\times Age\\ +{\beta }_{edu}\times EDU+{\beta }_{inc}\times INC+{\beta }_{drlic}\times DRLIC+{\beta }_{trlwk}\times TRLWK\end{array}$$

(11)

$$\begin{array}{l}{V}_{PC}={\beta }_{20}+{\beta }_{tt}\times TT+{\beta }_{tc}\times TC+{\beta }_{priv}\times PRIV+{\beta }_{prkc}\times PRKC+{\beta }_{age}\times Age\\ +{\beta }_{edu}\times EDU+{\beta }_{inc}\times INC+{\beta }_{drlic}\times DRLIC+{\beta }_{trlwk}\times TRLW{K}_{ }\end{array}$$

(12)

$$V_{TX}={\beta }_{wt}\times WT+{\beta }_{tt}\times TT+{\beta }_{tc}\times TC+{\beta }_{priv}\times PRI{V}_{ }$$

(13)

4. Results

4.1. Descriptive Statistical Analysis

The descriptive statistics show the awareness and familiarity of respondents in Figure 9. Approximately 72% of the respondents knew about car-sharing services, and 28% did not know. In addition, Figure 10, Figure 11 and Figure 12 present the findings of the respondents’ familiarity with various modes of transportation and trip purposes. According to Figure 10, respondents who were aware of car-sharing services preferred a private car for working trips as compared to those who were not aware of car-sharing services, while those who were unaware of car-sharing indicated a preference for car-sharing for working trips. This could be because car-sharing provides the same opportunity for people who do not own a car. Additionally, these findings corroborate a recent study conducted in Lahore, which found that mobile app taxis were more reliable and safer than public transit [17]. In Figure 11, the respondents who were unaware of car-sharing indicated equal preferences for shopping trips. As demonstrated in Figure 12, respondent preferences for private cars and car-sharing were not significantly different for recreational trips. According to a previous study involving university students in Qingdao, China [61], persons who are familiar with car rental services are more likely to use car-sharing. The respondents’ perception was evaluated through a five-point Likert scale (extremely unlikely, unlikely, unsure, likely, and extremely likely), shown in Figure 13. The results show that one-third of the respondents are more likely to postpone purchasing a new vehicle. This result is intuitive and valid because the main reasons for excessive vehicle ownership in Pakistan are inadequate and insufficient public transport. In addition, around 70% of respondents showed that car-sharing could mitigate the proportion of CO₂ greenhouse gas emissions. This finding is also reasonable in showing that lowering car ownership in Lahore can ultimately reduce the CO₂ greenhouse gas emissions and traffic congestion problems. Previous studies have suggested that public awareness and perception have largely impacted the acceptance of the car-sharing system [35,51,61].

4.2. Models’ Estimation Results

This research used econometric models such as the multinomial logit model, nested logit model, and random parameter logit model for the data estimation and interpretation. These models are based on random utility maximization theory. The specifications of these models are the multinomial logit model, which is basic, easy analyze and interpret, and has the simplest mathematical form among econometric models. The fundamental specification employed was an MNL model with the assumption that there was no correlation between any of the alternatives. However, the MNL model has two main drawbacks: the Independent and Irrelevant Alternative (IIA) and Gumbel distribution. The NL model captures the correlation among alternatives and uses generalized extreme value distribution. The RPL model captures the correlation and heterogeneity among respondents of the alternatives using different distributions.

The current study adopted an iterative approach to employing econometric models, starting with the simplest models. The authors proceeded with the MNL model, which is the simplest and most fundamental model. The MNL model was tested in terms of generic and socioeconomic characteristics. The authors incorporated those attributes with p-value < 0.05 and 95% confidence interval in the final model. The generic attributes, including travel time, waiting time, and travel cost, all resulted in negative values logical and relative scale, which were statistically significant and consistent with study expectations shown in

Table 3. Models’ estimation results.

Variables		Multinomial Logit Model			Nested Logit Model			Random Parameter Logit Model
		Car Sharing	Private Car	Taxi	Car Sharing	Private Car	Taxi	Car Sharing	Private Car	Taxi
		Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)	Coef. (t-Value)
β0	Constant	−0.538 (−2.31 *)	−1.90 (−6.00 ***)	_	−0.454 (−3.66 ***)	−1.80 (−7.32 ***)	_	−0.575 (−2.39 *)	−2.07 (−5.73 ***)	_
β age	Age 1 = > 39-year-old, 0 = otherwise	0.341 (2.03 *)	0.287 (1.62)	_	0.144 (1.94′)	0.141 (1.05)	_	0.336 (1.97 *)	0.284 (1.54)	_
β edu	Education 1 = Bachelor’s and Master’s degree and above, 0 = otherwise	−0.304 (−2.31 *)	−2.271 (−1.89′)	_	−0.134 (−2.20 *)	−0.120 (−1.04)	_	−0.312 (−2.32 *)	−0.277 (−1.84′)	_
β inc	Monthly income 1 = > 60,000 PKR, 0 = otherwise	0.355 (2.57 *)	0.355 (2.42 *)	_	0.138 (2.09 *)	0.219 (1.89 ′)	_	0.379 (2.67 **)	0.380 (2.45 *)	_
β drlic	Driver’s license 1 = Yes, 0 = No	0.434 (2.15 *)	0.643 (3.05 **)	_	0.150 (1.60)	0.424 (2.52 *)	_	0.456 (2.22 *)	0.690 (3.09 **)	_
β trlwk	Frequency of travel in a week 1 = > 2 day, 0 = otherwise	0.573 (4.74 ***)	0.585 (4.35 ***)	_	0.230 (3.01 **)	0.353 (3.11 **)	_	0.586 (4.74 ***)	0.613 (4.29 ***)	_
β priv	Privacy	0.253 (6.97 ***)			0.156 (4.17 ***)			0.253 (6.72 ***)
β prkc	Parking cost	3.01 × 10−3 (1.25)			6.37 × 10−4 (0.26)			3.12 × 10−3 (1.24)
β wt	Waiting time	−8.17 × 10−2 (−5.97 ***)			−5.83 × 10−2 (−4.81 ***)			−8.58 × 10−2 (−5.80 ***)
β tc	Travel cost	−2.53 × 10−3 (−2.62 **)			−2.15 × 10−3 (−3.73 ***)			−2.89 × 10−3 (−2.77 **)
β tt	Travel time	−5.01 × 10−2 (−6.91 ***)			−2.66 × 10−2 (−3.41 ***)			−5.62 × 10−2 (−6.00 ***)
${\mathsf{\theta }}_{\mathrm{nk}}$	Nesting coefficient	_			2.55 (3.91 ***)			_
σ	Travel time Std. dev.	_			_			5.63 × 10−2 (2.30 *)
		Observations: 2178 Initial Log-likelihood = −2392.778 Final Log-likelihood = −2170.716 $\mathrm{McFadden} {\rho }^2$ = 0.093			Observations: 2178 Initial Log-likelihood = −2392.778 Final Log-likelihood = −2161.229 $\mathrm{McFadden} {\rho }^2$ = 0.097			Observations: 2178 Initial Log-likelihood = −2392.778 Final Log-likelihood = −2169.740 $\mathrm{McFadden} {\rho }^2$ = 0.093 Halton draws = 500

Note: ′, *, **, and *** indicate statistical significance of p < 0.1, p < 0.05, p < 0.01, and p < 0.001, respectively. Std. dev.: Standard deviation.

. The negative coefficients for travel time and travel cost indicated that the decreasing utility is associated with increased travel time and travel cost. The negative sign next to these criteria indicates that respondents are willing to use a mode of transportation that requires less waiting, trip time, and expense. In Table 3, the parking cost was found positive and insignificant. In comparison, the privacy attribute was positive and highly statistically significant. This finding indicates that travelers in Lahore are more likely to use the modes of transport which are safe and secure. This result is intuitive in developing countries since most individuals in Pakistan continue to avoid public transportation due to social conventions, security, and privacy concerns. The results of the current study aligned with the motive of car-sharing, which provides the same alternative mode of private vehicles without the cost of ownership and responsibilities.

Second, the authors applied the NL model to check the correlation among alternatives. The alternatives were split into two nests, owned and shared, to test the correlation between ownership and shared use of alternatives. To keep the objective of the study in mind, the author kept the private car in the owned nest and taxi and car-sharing in a shared nest (shown in Figure 8). This model was found to be invalid due to failing the initial assumption that taxi and car-sharing are in the same nest as the NL model. In Table 3, the coefficient of the log sum parameter (nesting coefficient) is (θ_nk = 2.55), which indicates that there is no correlation among the modes in nests. This implies that taxi and car-sharing cannot be kept in the same nest. The range of log sum parameter or nesting coefficient is bounded by zero to one to ensure consistency with random utility maximization theory. According to a previous study [27], the log sum parameter (θ_nk = 2.55), which is greater than 1, indicated that the group of modes has higher independence and lesser correlation. The authors rejected the NL model, which did not fit with the data.

The random parameter logit model was applied to estimate the result of the data. Table 3 indicates the RPL model estimation results. The RPL model takes into account random parameters to check the heterogeneity among respondents of alternatives. The travel time was taken as a random parameter, and its distribution was considered as normally distributed. Most of the previous studies have assumed travel time to be normally distributed for the RPL model [28,39,44]. The travel time parameter has a lower value of standard deviation (σ = 0.0563) and is shown in Table 3. The standard deviation of the random coefficient was found statistically significant (p < 0.05) and at the 95% confidence level, indicating that the variables do vary across populations in Lahore. These results showed that RPL is a better model for predicting mode choice than MNL and NL models. However, this model captures the heterogeneity and correlation among respondents.

4.3. Socioeconomic Variables Results

The results of socioeconomic variables are presented in Table 3. The socioeconomic variables such as age, education, monthly income, driver’s license, and frequency of travel in the week were found to be statistically significant. In terms of age, the respondents who are older than 39 years were more willing to use car-sharing services and private cars. Regarding education level, the respondents under high school and of high school level were more likely to use car-sharing services. Similarly, from the perspective of income, high-income people with incomes more than (60,000 PKR/month) were inclined to adopt car-sharing services. This result is intuitive because the motive of car-sharing is to use it for a short period. The high earner public in Lahore will probably use it for short trips in the city. Lahore is considered one of the congested and disorganized systems of land use. This mode of transport will have discouraged people from buying second vehicles. The descriptive results of Lahore residents suggested that people will probably postpone buying a new vehicle when the car-sharing service fulfills their travel demands. Driver’s license was also found statistically significant for private cars and car-sharing services. This finding is intuitive because a driver’s license is mandatory for operating private cars and car-sharing services.

Lastly, the travel in a week was positively significant, indicating that respondents who traveled more than two days were more likely to use car-sharing services and private cars. The finding is intuitive because car-sharing services offer subsidization of private cars and flexible services without the cost of parking and ownership. These positive behaviors of respondents showed that travelers would probably switch to an eco-friendly mode such as car-sharing in the future. The insignificant socioeconomic variables such as gender, occupation, car ownership, and marital status were discarded from the final results.

5. Discussion

The present study aimed to investigate the public perception and acceptance of car-sharing for a case study of a megacity (Lahore) in Pakistan. The research work involved two main phases, i.e., descriptive statistical analysis of survey respondents and exploratory analysis covering detailed information about the survey design consideration and public travel behavior preferences towards car-sharing in relation to other modes such as private cars and taxis. Participants of varying proportions of diverse sociodemographic traits were interviewed with survey questions under multiple travel choice scenarios. The travel mode choice analysis was conducted using three widely used econometric analytic methods, namely, multinomial logit model, nested logit model, and random parameter logit models. Results from stated preference questionnaire surveys largely confirmed and extended the findings of similar previous studies. For example, generic attributes such as waiting time, travel time, travel cost, and privacy all turned out to be statistically significant in conducting the mode choice experiments which is shown in Table 3. Attributes including travel time, waiting time, and travel cost were negatively correlated towards the adoption of car-sharing, which is intuitive since an increase in their parameters is accompanied by a decreased utility. These results are in line with a number of previous studies [8,17,51]. On the other hand, the attribute of “privacy” was positively correlated for participants intending to join the car-sharing fleet. In Table 3, privacy is found to be statistically significant. This outcome is also intuitive and acceptable, as travelers desire a more secure mode of transportation. This is an interesting finding, given the city’s cultural diversity and different beliefs and social norms. In Pakistan, due to gender disparity and lack of law enforcement, most women prefer to use ride-sharing services and conventional taxis. It has been suggested that car-sharing services might serve as an effective alternative mode of transport and eliminate the privacy and gender disparity [8,20,51].

The study also investigated the effects of respondents’ sociodemographic variables toward the adoption of specific travel mode choices. The influential socioeconomic factors that were statistically significant included respondents’ age, education level, monthly income, driver’s license status, and travel frequency per week which is depicted in Table 3. Considering the travelers’ age, the elderly survey participants (aged 39 and above) were more inclined toward car-sharing and private cars. This result is intuitive because car-sharing provides a substitute for the private car. This result is in line with prior studies conducted in Beijing that showed that older individuals are more inclined to use car- sharing for a particular trip [50]. On the other hand, a study conducted in North America showed that participants aged 25–35 were more willing to use a car-sharing system [62]. The survey respondents belonging to under high school and high school education levels were also more willing to join car-sharing. This result is reasonable and expected in developing countries such as Pakistan in which people preliminary use a shared bus system for schooling and commuting. Because of their culture and social cohesion, people in low-income developing countries prefer to employ shared systems to save money on travel. This finding contrasts with the fact that car-sharing is primarily used by highly educated persons in developed countries such as Europe and North America [38,63]. Interestingly, respondents belonging to high-income groups (>60,000 PKR/month) also expressed their willingness to join the car-sharing system, particularly for short intra-city trips. A previous study [64] also reported that having a high income improves the likelihood of car-sharing adoption. Another study conducted in the United States analyzed data from the 2014–2015 Puget Sound Regional Travel Study. The findings indicated that car-sharing and ride-sourcing services are predominantly used by higher-income, young, well-educated, and working individuals who live in high-density locations [65]. In contrast, a study conducted in Toronto, Canada, revealed that lower-income individuals were more inclined to utilize car-sharing services. Additionally, members who reside in lower-income neighborhoods are frequent car-sharing customers [66]. Lastly, respondents who took more than two trips a week also indicated their willingness to use car-sharing mode. The inconvenience and hecticness caused by frequent routine driving and high costs of private taxis are considered the main barrier to adopting the other travel modes, i.e., private cars and taxis. This finding is also in agreement with several previous studies [35,67]. Moreover, another study conducted in Greece on car-sharing also reported similar findings to the current study, indicating that travelers are more satisfied to make three trips per week [68].

6. Conclusions

The purpose of this study was to examine travelers’ attitudes and preferences about car-sharing systems in Pakistan’s megacity and to investigate the factors that influence car-sharing mode by including a privacy and safety variable in an SP survey. The travelers’ attitudes and preferences were explored using a stated preference survey and econometric models. The authors examined the data using three econometric models: MNL, NL, and RPL. RPL outperformed the other models. The random parameter travel time of the RPL model was found to be statistically different from zero, indicating that respondent choices are heterogeneous. It was revealed that car-sharing has enormous promise as a viable alternative to private automobiles in dense urban areas.

While car-sharing has been extensively explored in previous studies, privacy and safety concerns have received less attention, particularly in developing nations. In developing countries such as Pakistan, privacy is seen as a critical predictor along with travel time and cost. Due to their culture, religious beliefs, and iconic status, the majority of people prefer to travel in private cars and taxis with their households. The results demonstrate that instrumental factors such as privacy, travel costs, travel time, and waiting time are all significant. The findings indicate that these instrumental factors are significant predictors for travelers when deciding between private cars and public transportation. Age, education, monthly income, driver’s license, and weekly trip frequency were all statistically significant. In Lahore, elderly travelers were more inclined to opt for car-sharing services. Additionally, travelers with an under-matric or intermediate level of education are more likely to use car-sharing services. Individuals with a household monthly income of more than 60,000 PKR were likely to use a car-sharing system.

Although car-sharing has been investigated in Lahore using an SP survey, this study has a few limitations. Due to the widespread criticism leveled at the initial SP survey due to hypothetical scenarios, subsequent studies should thoroughly analyze revealed preferences and latent factors. Additionally, it is advised that future research should investigate the influence of car sharing on other modes of transportation such as ride-hailing and e-hail. According to the findings of this study, additional research on targeted groups such as gender and age is necessary to obtain more meaningful results. To address mobility concerns effectively, policymakers and stakeholders must develop shared mobility services such as car-sharing and Mobility-as-a-Service (MaaS). Vehicles were blamed for up to one-quarter of pollution, and efforts were concentrated on mitigating their impact by renovating older diesel public transportation systems and introducing electric alternatives (such as trolley buses, e-bikes, and private electric vehicles). Given the critical role of technology in the growth of car-sharing, future studies should examine how recent and imminent technological advancements in the automotive sector might be leveraged to spur innovation and improve the car-sharing customer experience. Likewise, future studies could focus on analyzing public attitudes towards electric car-sharing systems as it is different from traditional fossil-fuel-based car-sharing systems due to driver range anxiety and charging limitations. It is also essential to examine differences between various car-sharing services such as peer-to-peer, station-based, and free-floating systems. Similarly, the survey sample size may be increased, or specific population groups may be targeted to have more confidence in the obtained results. For instance, several providers have included EVs in their car-sharing fleets to promote sustainability and save operational expenses. Additionally, future studies should examine how digital technology may enable seamless multimodal mobility services that are consistent with customer choices and policy objectives.