1. Introduction
The capital city of Saudi Arabia, Riyadh is one of the fastest-growing cities in the world, with rapid growth in the population of 8.4 million in 2018 [
1]. The gradual economic boom of the last two decades has led to a significant increase in motorized traffic outgrowing the capacity of the city’s road network [
2]. According to Ar-Riyadh Development Authority (ADA), more than 92% of daily trips are made by private cars, only to increase road congestion [
3]. The growing car use is not favorable to the global sustainable goals of reduced energy consumption and improved air quality [
4]. Moreover, road congestion causes increased travel time and road safety issues, socio-economic problems, and Green House Gas emissions (GHG) [
5]. Road transportation alone is responsible for 14% of global GHG emissions in 2015 per se and the demand for transportation sector energy consumption is expected to increase by 300% in 2050 [
6]. The negative impacts impair the quality of urban life and mobility to the city dwellers, thereby, making the transport system unsustainable [
7]. Introducing public transport is considered as a remedial measure of limiting car users and solo trips in Riyadh City. To combat such multidimensional transport-related issues the authority has commissioned a new public transport system comprising six Metro lines complemented with bus networks in 2012. However, most of the dwellers in Riyadh had no prior experience of using public transport and are mostly accustomed to using their private cars for daily commuting [
8,
9]. In a study, Al-Fouzan reports that higher family income, improved economic factors, and modernization, state-sponsored fuel subsidy, and urban sprawl have contributed to shaping the lifestyle of Saudi families relying more on private vehicles than other modes [
4,
10]. Lower fuel price, comfort, privacy, and socio-cultural aspects kept the demand for using cars for city trips steady in Riyadh [
4].
Aldalbahi and Walker considered Riyadh as a unique case study for both a rapidly moving microcosm trend in transportation, facing significant traffic congestion, and growing transportation demand due to the high rate of urbanization and auto-dependency [
11]. Growing transportation demands in Riyadh urban areas makes it vital to introduce major public transportation as a sustainable solution to reduce traffic congestion, especially, with the current trend, it is estimated that 90% of total roads will be overloaded and congested by 2021 [
11].
Excessive single occupancy vehicle use leads to adverse social and economic effect costs from reduced air quality, congestion, decreased urban livability [
12]. Therefore, Transportation planning policies in congested metropolitan areas often seek to create a more effective, attractive, and sustainable transit service to compete with the single-occupant automobile. The policy goal is to attract travelers away from their private cars toward transit use; yet, various case studies conducted on cities with traffic congestion demonstrate that it is possible to reduce car dependence even in affluent societies with high levels of car ownership if the transit services are designed to meet public expectation [
13].
This study attempts to analyze the modal choice shifting from private car to Metro in light of the Metro service attributes of Riyadh City. New Riyadh Metro should attract car users and not “Captive riders”. The above goal can be achieved by investigating how people react to a set of travel attribute factors that contribute to the commuter’s choice across different socioeconomic characteristics of the population upon planning the metro system services scheme. The city dwellers in Riyadh is heavily dependent on the use of private cars for their daily commute [
3]. The proposed Riyadh Metro is supposed to attract the car mode commuters that constitute 85% of trips in Riyadh. To archive the goal of sustainable transportation there is a need to test the commuter’s preferences towards the new proposed metro service. The information on mode choice would help plan and operate the metro service better by knowing the extent of modal shift in terms of travel attributes. The study will provide an initial assessment to test various combinations of policies to reduce car usage such as parking price, congestion price, and road toll and increase metro ridership.
One of the main objectives of this study is to build a discrete mode choice model using the stated preferences method for a business trip in Riyadh considering several travel attributes and socioeconomic variables. Based on the discrete choice model, sensitivity and simulation study would be conducted to test the effect of changes in travel attributes (time, cost, walking time) on the individual choice probability to ride the Metro. However, the scope is limited to the business trip in Riyadh, which constitutes the biggest share of what will have a significant impact on the travel behavior in Riyadh. The study focuses on business trips as nonbusiness travelers are less elastic than business travelers with regard to the transportation attributes (e.g., travel time and frequency of service). This study offers an opportunity to assess people’s sensitivity to various mode choice scenarios with cars and metro service such as travel time, walk time to the metro station, and fuel cost.
The remainder of this paper is structured as:
Section 2 provides a detailed literature review about Riyadh Metro and pertinent studies.
Section 3 presents a description of the study area and data collection.
Section 4 discusses the data description and study methodology;
Section 5 highlights results and discussions. Finally,
Section 6 summarizes study findings, provides study limitations and outlooks for future research.
2. Literature Review
Each travel mode is dominant in various travel situations due to the difference in travel speed, comfort, and travel cost of each mode. The understanding to what extent travelers’ socio-economic, demographic, and trip characteristics affect the choice of individuals travel mode is significant to the analysis of mode choice behavior.
Numerous studies in the literature have investigated the influence of several factors that would affect an individual’s travel mode choices. Beirão and Cabral explored the traveler’s attitude towards transport and perception of public transport quality among public transport and car users [
14]. The study found that individual characteristics and lifestyle, the journey type, and the perceived service performance of each transport mode tend to influence the choice of transport. They suggested that public transport should be designed to meet the required level of service of the customer to encourage them using it. Hartgen maintains that socio-economic attributes and travel attitude are very important to shape travelers’ decisions on mode choice [
15]. Forward indicated that the individual status and habit along with the quality and supply of alternative modes are influential in mode choice [
16]. Travel purpose and personal characteristics are also found to impact travel mode choice [
5]. Albalate and Bel identified factors explaining local public transportation of large European cities from both supply and demand sides. The study stated operational cost, income, and city characteristics influence the supply of public transport (PT), whereas travel cost and travel time have a significant impact on the PT demand [
17].
Bhat and Srinivasan showed that households with higher income have a propensity to use auto mode [
18]. Yang et al. found that due to several advantages, females prefer to choose public transport than males [
19]. Affordable ticket fares and saving of travel time are vital to public transit attractiveness [
20,
21]. Punctuality in the arrival schedule is another influential factor for choosing PT [
22]. Unlike cost and other variables, time is considered as a constraint as people cannot increase the time spent on traveling infinitely [
23]. Polat mentioned that three key components comprise travel time by public transport; the time taken to walk to the nearest transit station or bus stop, waiting for service, and time spent in the vehicle [
23]. Some other studies added that transfer between vehicles or modes is accounted for in the public transport travel time [
24,
25].
Chauhan et al., studied the efficacy of a multivariate statistical model to predict the probability of non-Metro commuters to shift to the Metro service at Delhi [
26]. A binomial logistic model was developed to predict the switch of existing Metro commuters who used to travel on private motor vehicles or busses. They found that 57% of Metro users have switched from personal vehicles or buses. The reason for switching from private vehicles and busses to Metro is attributed to the longer travel time when compared to Metro services. Their study also analyzed the cannibalism effect (i.e., modal shift within the same category) shift from busses to Metro service. In a similar study by Jain et al. Analytical Hierarchy approach to prioritize the different criteria for urban commuters from private vehicles to Metro service in Delhi, India [
27]. Based on reliability, comfort, safety, and cost, the public preference was examined for a potential modal shift of passengers to Metro service. The result revealed that safety was the major reason for which commuters wanted to switch to metro service from other available modes. Commuters were willing to pay more for better public transit.
Wang et al. used Binary Logistic Analysis to assess the impact of modal shift from automobiles and busses after a Bus Rapid Transit (BRT) was introduced along six representative corridors in China [
28]. The results of the study showed that commuters’ demographic, socioeconomic and trip attributes were vital to modal shift to BRT. Ladhi et al. reviewed and assessed modal shift behavior using a discrete choice model due to the introduction of a new metro mass transit [
29]. The result of the study revealed several causes of modal shift from personal vehicles and buses to Metro rail service. Excessive road congestion, less travel time, and lower travel fare were found to be the main cause of shifting from personal vehicle to Metro. A similar study conducted in Thessaloniki, Greece attempted to analyze the modal shift of private car users to a newly constructed metro service for a sustainable mobility solution [
30]. Interestingly, through a stated preference survey this study revealed that the car users are not willing to switch to Metro service even after knowing the benefits of using a mode of public transport. However, the existing bus riders would shift to metro service as they think that metro service will benefit them from several aspects.
Sohoni et al., deployed drafting, executing, and testing revealed preference (RP) and stated preference (SP) questionnaire surveys to investigate mode shifting behavior in Mumbai Metro, India [
31]. The RP survey was performed on passengers on the newly constructed Metro corridor, while the SP survey was performed on a proposed extension of the Metro line. A Sequential estimation method was adopted to the combined RP and SP dataset to develop an econometric mode choice model. Sixty percent of the respondents from the SP survey were willing to adopt the proposed metro extension for their regular commute. Ding and Yang estimated commuters’ mode choice behavior against a raised parking fees [
32]. The variability of travel times is considered and analyzed in the stated choice survey conducted among car, bus, and Metro users. The study results concluded that the increment in driving cost would significantly reduce the driving demand, whereas discounted travel fare was unable to drive car commuters shifting to Metro.
Ashalatha et al. assessed mode choice behavior using a Multinomial Logistic regression model at Thiruvananthapuram city in India [
32]. The investigation disclosed that the older age of commuters has a direct repercussion on mode choice as they tend to favor cars more than public transport citing comfort and safety. Increased travel time and cost by public transport caused a shift of passengers to cars and two-wheelers.
Transport planners often need to forecast impacts on travel demand of transport policies, e.g., construction of a new transport alternative, changing public transit fares, or imposing road pricing schemes. In such forecasting of mode choice concepts, stated preference (SP) methods are often used where the individual chooses among different transportation means, which is perceived as a consumer evaluating the available alternatives and selecting the best one [
33]. This analysis is rooted in the consumer utility maximization theory as the model choice of traveler is defined through tradeoffs among specific characteristics associated with different modes and that the traveler is willing to maximize his utility [
34].
Utility function associated within the alternative is given by:
Vi-is the observed utility or representative component of Utility as it is the attribute that reflects the choice
εi is the unobserved utility
Both are assumed to be additive and independent [
35]. The above can be interpreted into a functional form
where β
0i-represent the unobserved utility called” Alternative specific constant
β1i-weight of the parameter associated with attribute (x1) for an alternative I, assuming that component εi is identically distributed.
The probability of choosing a mode can be expressed through the logit model given in equation [
36]
where, P
ki is the probability of k to take mode i, and V
ki is the observed component of the utility function of mode i by k as a function of socioeconomic and characteristics of the mode.
Stated preference (SP) has become the principal method in transportation planning; the stated preferences of travel mode takes one of the appropriate data collection methods, e.g., ranking-based, rating-based, or choice-based [
37]. The service attribute for the transportation mode may include trip-related factors: travel cost, travel time, vehicle-related attributes such as comfort, accessibility, and punctuality; these terms perception vary among modes, for instance [
38].
Stated preference techniques have advantages over revealed preference methods, which are based on actual choices, on the ability to make more than one transportation choice and can be presented with tradeoffs rather than dominated choices and learn the importance that people devote on each attribute based on the choices they make [
39]. One of the advantages of stated preference is to collect data with as little bias as possible [
40].
Also, SP gained popularity, according to Ortuza and Willumsen, due to its ability to [
41]:
Deal with situations when a new alternative is introduced with no background knowledge about how people would react.
Determine the separate effects of two variables on the consumer’s choice provided.
Observe the variability in choices and the variables can be controlled
Deal with sensitivity and elasticity when it is more important than forecasting the substantial mobility level.
Demonstrate cost-effectiveness.
The base of the SP experiment carried out in cases where the desire is to assess the consequences of a new policy or new technology, such as high-speed transit, is by investigating the reaction to a hypothetical situation. However, in SP, at least three characteristics for each alternative should be present for respondent evaluation bearing in mind that these characteristics should appear realistically by asking the decision-maker to choose among different alternatives, the analyst gathers information about the relationship between the varying attribute level of the transportation mode and the choice that the decision-maker takes based on tradeoffs on these attributes [
42].
It is worth noting that these characteristics should appear realistically, furthest, the varying attribute level of the transportation mode, and the choice that the decision-maker takes based on tradeoffs on these attributes [
35]. Hensher highlighted the ambiguity faced by the researchers in defining the public perception of some travel attributes that are associated with public transport, apart from travel cost, travel time, safety, level of comfort, and convenience [
35]. Safety, for instance, could mean personal assault, but for others, it may mean the vulnerability of train derailment; however, the sources of the estimated parameter are taken from past studies and pilot surveys [
35].
In defining the attribute level based on RP, Hensher et al. recommend two methods: first: assign a percentage from the attribute level reported by the decision-maker (e.g., −10%–+10%), second: treat every decision-maker in the associated segment or range of attribute levels [
35]. However, the attribute level range can be derived by a focus group or initial survey in a careful way that needs to be factual [
35]. Habibian and Kermanshah studied the car commuters’ change to public transportation by stated preferences when transportation demand management measures are hypothetically applied; they have modeled the commuter’s choice in logit binary and concluded that parking cost, transit access by walk, and fuel cost are highly correlated with commuters’ choice mode [
43].
Ahern and Tapley conducted a study on the preferences of passengers on interurban rail and bus in Ireland using stated preferences and revealed preferences; in comparing the two methods, they identified limitations in both methods, especially by the limited ability of the respondent to understand the hypothetical situation which can be overcome by generating realistic alternatives [
44]. Habibian and Kermanshah studied the car commuters change to public transportation by stated preferences when transportation demand management measures are hypothetically applied; they have modeled the commuter’s choice in logit binary and concluded that parking cost, fuel cost, car ownership for car mode, and travel time and transit accessibility for public transits were the influencing factors [
43]. The study concluded that parking cost, transit access by walk, and fuel cost are positively correlated with commuters’ choice mode. Chakour and El-Geneidy studied the travel mode choice and transit route choice behavior in Montreal, Canada [
45]. The study objectives are two-fold. First, investigate an individual’s choice between transit and car mode of transportation for commuting to McGill University. Second, for transit commuters, the decision that influences their decision is to be analyzed. The study considered several variables in the empirical analysis, socio-demographic aspects, age, gender, driving license, employment status, and vehicle ownership. At the travel attribute, travel time, travel time by mode, walking time, initial waiting time, waiting time in transit, a number of transfers, and time of day were accounted for.
A stated preference survey by Gleaves on the rail network in England investigated the importance of various characteristics given by passengers to the rail transportation such as time to access the rail station, headway, and in-vehicle time; the aim was to recommend whether to test the feasibility to build new lines in the future (future trend) [
6]. The weights of these parameters were tested in an initial study in 2002. The respondents were faced with hypothetical but realistic value alternatives; each alternative has been described by attributes variation to reflect the people’s perception towards these attributes [
46]. From a data collection perspective, Antoniou et al. maintain that most studies use stated preference data as obtaining revealed preference data is not always favorable [
47]. Furthermore, due to practical reasons, most studies use mixed discrete choice models or logit models for mode choice analyses.