Stated Preference Approach in Shaping Urban Sustainable Multimodal Transport—A Literature Review

Abstract

Stated preference surveys have been utilized in the field of sustainable multimodal transport planning for some time. Stated preference, which relies on hypothetical scenarios to determine user preferences, offers critical insights into travelers’ choices between different transport modes. The stated preference method is used in a wide range of transport studies, such as the mode choice, route choice, service attribute analysis, pricing and fare policies, and technical innovations. On the basis of the collected data in stated preference studies, it is possible to optimize current services, forecast future demand, or analyze the possibilities of nonexistent services. A literature review reveals that there are certain gaps regarding the calibration of utility functions in multimodal and new services.

1. Introduction

Achieving sustainable urban mobility increasingly depends on promoting multimodal travel behavior. However, despite extensive use of stated preference techniques, a comprehensive synthesis of their application in shaping multimodal and sustainable transport solutions remain limited. The stated preference (SP) approach can be crucial in transport modeling because it captures hypothetical choices and user preferences and behavior by asking them about their choices. On the basis of captured user preferences, it is possible to determine what users prefer with respect to goods, services, or attributes [1].

By adopting the Paris Agreement in 2016, 196 countries set the goal of achieving carbon neutrality. From the perspective of the transport sector, the aim of carbon neutrality can be achieved by promoting the use of sustainable transport modes and combining different forms of travel [2]. Sustainable mobility represents a way of moving and transporting goods with minimal environmental, economic, and social impacts and has long-term positive effects, often comparing travel to the use of private cars [3]. The use of public transportation and other forms of active travel, such as walking or cycling, is considered less negative than the use of cars, and sustainable travel is recognized as a fundamental element in the development of environmentally, economically, and socially sustainable societies [4,5].

In addition to the use of sustainable modes of transport, multimodality is also presented as a potential solution for a sustainable society. Multimodality involves the use of multiple transport modes during a single journey within a specific time interval. It is considered a fundamental mechanism for reducing dependence on fossil-fuel-powered vehicles and encourages the shifting of a certain number of trips to sustainable modes [6,7]. As an important element of multimodality, the integration and organization of all transport modes with the optimization of different modes in urban areas has become a significant issue in urban areas because of the increased number of vehicles, leading to congestion [8]. Traffic modeling, as an integral part of transportation planning, is highly important for analyzing the current state of traffic with the aim of predicting future traffic conditions. By using simulation tools that display traffic models, it is possible to develop and test new solutions related to traffic flow organization, the implementation of new infrastructure solutions, or the introduction of new services.

The SP method is a widely used element in research on travel user preferences. On the basis of SP surveys, it is possible to identify behavioral responses to situations not yet revealed in the market [9]. The advantage of SP surveys is their ability to evaluate potential responses to new transport modes or demand scenarios before their implementation in the real world. This allows researchers to assess how individuals might react to new transportation options or changes in service levels, providing valuable insights for transport planning and policymaking. On the basis of the given data, it is possible to perform a detailed analysis of the factors that are most influential for decisions. SP surveys can consider many factors, but the most common factors are the cost of transport, travel time, comfort level, and number of transfers. On the basis of the survey, it is possible to define a utility function that defines the probability of choosing between mode options. Calibrated utility functions are critical for accurately forecasting travel behavior in response to new policies, technological advancements, or infrastructure developments.

To support this review, a targeted literature search was conducted across three major scientific databases: Web of Science, Scopus, and Google Scholar. The search included combinations of relevant keywords including “stated preference”, “mode choice”, “route choice”, “multimodal transport”, “fare policy”, and “autonomous vehicles”. The review focused on peer-reviewed studies published between 2000 and 2024 that applied SP methods in the context of urban mobility, transport planning, and emerging transport services. Inclusion criteria required that studies were published in peer-reviewed journals between 2000 and 2024, written in English, and applied stated preference or combined stated preference–revealed preference methods in the context of passenger transport. Eligible studies had to report empirical data or model estimations related to sustainable or multimodal transport, including mode choice, route choice, service attributes, pricing and fare policy, or technological innovations. Exclusion criteria were studies focused on freight transport, land-use valuation or non-transport applications, and purely theoretical econometric papers without empirical data. Foundational textbooks and classic methodological works were included only to provide theoretical background.

This document is structured as follows. Section 2 discusses the modal distribution in traffic modeling, emphasizing its importance, particularly through discrete choice models, and the growing relevance of multimodality in transport systems. Section 3 discusses the core elements of the stated preference method, highlighting the utility model as a foundation for understanding user behavior. Later, key areas of application are mentioned, including the mode choice, route choice, analysis of service attributes, pricing and ticketing scheme predictions, and the evaluation of technological innovations such as electric and autonomous vehicles. Section 4 presents key findings from the literature review with potential improvements based on previous work, and finally, the Conclusion section identifies potential research gaps, outlining areas where further study is needed to advance the field of transport modeling and decision-making.

2. Modal Distribution in Traffic Modeling

Modal distribution, one of the four fundamental steps in transport model development, represents the distribution of travelers’ trips on the basis of the selected mode of travel. It represents a crucial element in traffic planning and policy implementation, necessitating the development of models sensitive to factors influencing modal choice [10].

The selection process of transportation mode is influenced by numerous elements, which can be represented as user preferences determining their mode of transport. One of the most accepted categorizations of factors influencing the mode choice was devised by Ortuzar and Willumsen, who classified these factors into three primary categories: characteristics of the trip maker, characteristics of the journey, and characteristics of the transport facility. A detailed breakdown of these elements is shown in

Table 1. Influencing factors for the mode choice [10].

Category	Factors
Characteristics of the trip maker	Car availability and/or ownership
	Possession of a driving license
	Household structure (young couple, retired, singles, etc.)
	Income
	Decisions made elsewhere (use car for work, take children to school, etc.)
	Residential density
Characteristics of the journey	Trip purpose
	Time of the day
	Trip taken alone or with others
Characteristics of the transport facility	Travel time
	Travel cost
	Availability and cost of parking
	Reliability of travel time and regularity of service
	Comfort
	Safety
	The demands of the driving task
	Opportunities to undertake other activities

.

In addition to Ortuzar and Willumsen, Gandhi and Tiwari, through extensive research conducted in Delhi, India, identified 13 significant factors influencing users’ choice of transport mode. The most crucial factors highlighted qualitative aspects such as time, cost, convenience, and safety, whereas other factors were categorized into sociopsychological factors (habits, purpose, etc.) and socio-demographic characteristics (education, income, car ownership) [11]. Esztergar-Kiss et al. reported that mode choice is not only shaped by socioeconomic characteristics and past habits but also by key attributes such as travel time, cost, environmental impact, and health considerations. While travel time remains the dominant determinant, findings demonstrated that increasing awareness of environmental and health considerations can gradually reshape preferences towards more sustainable modes [12].

Mode choice models can be aggregated or disaggregated. Aggregate models are based on observed relationships for groups of travelers or, on average, at the zone level, whereas disaggregate models are based on observed choices made by individual travelers [10]. The common analytic method involves subdividing the study area into zones, which represent the origins and destinations of all travelers in each zone by a single point, typically the geographical centroid. Aggregate travel demand models are then estimated via travel behavior data aggregated at the zone level, which serve as the observational unit for statistical analysis. Unlike aggregate models, disaggregate models focus on the choices made by individual travelers, such as the mode of transport, route selection, and travel time, which enables traveler preferences, leading to more accurate predictions [13]. Disaggregate models are based on theories of individual behavior; therefore, an important potential advantage over aggregate models is that disaggregating demand models are more likely to be stable in time and space [10]. Disaggregate mode choice models offer significant advantages over conventional aggregate models by focusing on individual behavioral units, allowing for a detailed analysis of factors influencing travel behavior and making more efficient use of available data [14].

Discrete choice models are a subset of disaggregate models. Discrete choice models specifically aim to understand and predict individual choices among a finite set of alternatives, such as transportation modes or routes. In discrete choice modeling, each choice is treated as a discrete event where individuals select one option from a set of alternatives on the basis of their preferences, constraints, and characteristics of alternatives [13].

2.1. Discrete Choice Models

Discrete choice modeling is an established area of research in econometrics, psychology, marketing, and transportation, where it is used to analyze and predict travel decisions [15].

Discrete choice models are widely used to estimate behavioral metrics such as consumer preferences for various transportation goods and services and willingness to pay for specific attributes such as travel time savings. Respondents participate in one- or multiple-choice tasks, selecting their preferred alternative from a choice set characterized by different attribute levels. By analyzing these choice observations, researchers can define preferences and evaluate trade-offs between attributes [16].

According to Ben-Akiva and Lerman [13], the general framework of discrete choice models can be presented by a set of general assumptions, which are as follows:

• Decision-maker;
• Alternatives;
• Attributes;
• Decision rule.

As previously stated, discrete choice models are based on individual decisions, with the importance of defining the socioeconomic characteristics of the decision-maker, such as age, sex, education, and income. On the basis of the decision-maker’s choice, it is necessary to know what has been chosen as well as what has not been chosen between the considered alternatives, which are called the choice set. Importantly, the choice set for each individual can differ on the basis of the availability of alternatives to the individual. Each of the available alternatives in the choice set is defined by a set of attributes, such as travel time, cost, or comfort, which represent and differentiate each alternative. The decision rule describes the internal mechanisms used by the decision-maker to process the available information and to make a choice from a given choice set. Most models that track travel behavior are based on the utility theory class of decision rules, which assumes that the decision-maker attempts to maximize its positive value by comparing the attributes of the choices and selecting the choice with the maximum utility [13].

2.2. Multimodality

Traditional transport development has historically focused on individual modes of transport, such as cars, bicycles, buses, or trains. However, urbanization and increased traffic congestion, particularly in cities, have stimulated the need to integrate various forms of transportation to establish sustainable transportation systems. Key motivations for integrating products and services at special prices include expanding market share, cost savings, and customer satisfaction [17,18,19,20]. The effective integration of existing transport infrastructure and the understanding of user needs are crucial elements for the development of sustainable transportation systems [21]. By combining the best elements of each transport mode, efficient utilization of transport infrastructure is enabled, ultimately leading to better outcomes than promoting and using a single mode of transport independently [22]. The term multimodality is used inconsistently in the literature, sometimes referring to the individual behavior of combining multiple modes within a trip or over a time period and sometimes to the integration of transport services and infrastructures enabling such behavior [23]. Some authors discuss that new services emerging in the market, such as public bicycles [24,25], public e-scooters [26], car-sharing [27], and autonomous vehicles [28,29], provide a perfect complement to public urban transportation systems. Figure 1 illustrates a typical multimodal trip, where travelers combine different transport modes within a single journey.

A crucial element in establishing multimodality is the quality integration of systems, and according to certain studies, there is uncertainty about whether the development of individual systems will create competition for public transport. Ricci argued that public bicycle services can compete with and complement public transport simultaneously, depending on price, station location, and passengers’ perceptions of bike-sharing services [30]. Shaheen et al. reported that car-sharing systems can also compete with and complement public transport, depending on the pricing model and service organization [31]. On the other hand, some authors have demonstrated how the integration of new micromobility services with public transportation certainly provides a complement to public transport services while further developing sustainable mobility forms [32,33,34].

Some authors have conducted research to examine which demographic characteristics, travel habits, user attitudes, and infrastructure conditions influence the choice of transport mode and acceptance of multimodality [22,35]. Through their research, Bhat and Srinivasan concluded that households with higher incomes more frequently use cars, whereas according to Li and Zhoao’s study, bicycles are a popular choice for short trips [36,37]. The most significant factors for choosing public transportation during travel are time savings during the journey, punctuality in arrivals, and ticket prices [38,39,40]. Molin et al. [41,42,43] showed that travelers can be grouped into different profiles, ranging from those who rely almost exclusively on cars to others who regularly combine several modes. They found that people’s attitudes toward cars and public transport usually align with these patterns of use, which suggest that policies should be tailored to the specific needs and perceptions of each group [44]. Scheiner et al. emphasized that life events such as job loss, household changes or residential relocation often disrupt habitual travel behavior and create windows of opportunity for adopting alternative modes [42]. Klinger extended this perspective by demonstrating that the mobility culture of the city strongly shapes outcomes, with moves to alternative modes and multimodality [43].

As previously mentioned, effective integration of different transport modes, or multimodality, is considered an important tool for addressing traffic congestion and improving the accessibility of locations without expanding the transportation network, which presents an opportunity for significant economic savings [41]. The most common form of integrated public and private transportation is the Park and Ride (P&R) system. Bos and Molin argued that the implementation of P&R locations on the outskirts of cities, along with the implementation of restrictive parking policies in the city center, represents a promising measure that encourages the reduction in traffic congestion in many cities across numerous countries [45,46].

In addition to such a system, the development of a multimodal hub is also presented as an important step towards multimodality expansion. A multimodal hub represents a location where multiple different modes of transport intersect, allowing seamless transfers between different modes. Ultimately, the main idea behind creating multimodal hubs is to achieve optimal travel efficiency while providing access to various forms of transportation [47,48]. A well-planned multimodal hub significantly impacts the development of a station’s vicinity and attracts many users, acting as a driving force for urban development in both large and smaller cities [49]. In addition to infrastructure and strategic solutions, the development of information technologies enables progress towards planning and organizing multimodal trips [50].

Ultimately, for the quality of the integration of multiple forms of transport and the creation of multimodality, it is necessary to consider the payment system. Several studies have shown that unified tickets and integrated mobile applications can enhance and simplify the integration process by creating a system that is more focused on the user [25,31,33,34].

3. Stated Preference Method

Modal distributions and user preferences are typically gathered through revealed preference (RP) surveys by observing actual travel behavior and choices made by individuals in real-world scenarios. This method relies on obtaining data on what people report they carry out or, more often, what they have previously carried out. Importantly, RP studies have several limitations, such as not obtaining data to provide sufficient variability to develop forecasting models, and observed behavior may be dominated by a few factors, making it difficult to detect the relative importance of other variables and difficulties in collecting responses for policies that are entirely new [10].

Addressing these limitations would ideally involve conducting controlled experiments in real-world transportation environments; however, practical opportunities for such studies are highly constrained. In the absence of real-market data to predict behavior, researchers have increasingly relied on SP methods. These methods include multiple techniques that involve gathering data on respondents’ intentions within hypothetical scenarios, as opposed to observing their actual behaviors under existing market conditions. Individuals have to select one of the offered alternatives only by varying the attribute levels that define them, thus enabling analysts to interpret how attribute level variations affect alternative choice selection [51,52]. An example of such mode choice SP survey is illustrated in Figure 2, which demonstrates how respondents are presented with hypothetical travel alternatives characterized by different attribute levels. On the basis of the gathered data of individuals, it is possible to determine the utility function of a certain travel mode and determine important attributes of the given alternative [10,53].

A crucial factor in the success of SP surveys is the degree of realism of the responses, which can be achieved through various approaches. First, focusing on specific rather than general behaviors, such as how respondents react to a particular alternative in a given situation, can enhance realism. Employing a realistic choice context and incorporating existing and perceived levels of attributes ensures that the options are grounded in respondents’ actual experiences. Additionally, including all relevant attributes while avoiding excessive complexity is essential for maintaining respondent engagement and accuracy [10].

One of the main advantages of SP studies is their ability to evaluate potential responses to new transport modes or demand scenarios before their implementation. This allows researchers to assess how individuals might react to new transportation options or changes in service levels, providing valuable insights for planning and policymaking. SP methods also facilitate the exploration of hypothetical scenarios that may not yet exist in the real market, offering a proactive approach to understanding preferences and predicting future trends [55]. However, despite their strengths, SP methods also have certain limitations, such as hypothetical bias and the risk that respondents stated choices may differ from their actual behavior in real situations. Additionally, designing realistic and comprehensible choice scenarios can be challenging, especially when complex attributes are involved [56]. These strengths and limitations are often summarized in a comparative framework between revealed and stated preference surveys. Figure 3 illustrates this comparison, highlighting how RP captures actual observed behavior while SP enables the evaluation of hypothetical scenarios, new policies or emerging technologies.

3.1. Utility Model in the SP

Consumer preferences in transport are commonly represented using utility functions. The concept of utility, originally developed in economics, was among the first systematically applied to mode choice decisions in transport by Ben-Akiva, who played a central role in popularizing its use within the field [57]. Utility reflects the perceived benefit of satisfaction that an individual derives from selecting a specific good or service. However, as a subjective measure, utility varies between individuals due to differences in preferences, values, and perceptions for the same good or service [1].

The utility maximization approach assumes that individuals possess adequate information about available transport alternatives, enabling them to make rational decisions regarding their mode of transportation [58]. Utility can be defined as the result of individual n choosing alternative j in choice situation t as U_jnt, where in a random utility concept, utility is composed of a deterministic component V_jnt and a random component ε_jnt. The deterministic component is defined as V_jnt = g(β, x_jnt, z_n), where β is a vector of estimated parameters, x_jnt are attributes describing alternative j in the given situation, and z_n are characteristics of individual n. A random part of the utility measures deviates from the modeled utility for alternative j and respondent n [59].

U_jn = V_jn + ε_jn

(1)

With all utility parts combined, the probability of individual n choosing alternative i (out of j = 1, …, J) in choice task t is given by the following:

P_int = P (V_int + ε_int > V_jnt + ε_jnt, Ɐj ≠ i)

(2)

In the multinomial logit model (MNL), which is most commonly used in the disaggregated approach, the random variable behaves in accordance with Gumble’s distribution. In the multinomial logit model, there are multiple possible outcomes, where 1 means that the individual has chosen an alternative and 0 means that the individual did not choose other alternatives [60]. The given probability for choosing a specific alternative can be presented as follows:

$${\mathrm{P}}_{\mathrm{n}}(\mathrm{i})={\displaystyle \frac{e^{{\mu V}_{in}}}{{\sum }_{jϵC_n}{e}^{{\mu V}_{jn}}}}$$

(3)

The utility function can be constructed by several variables with its own coefficients that can be calculated via logistic regression. To obtain a logistic regression function, it is necessary to obtain several individual observations that represent how alternatives have been chosen.

The utility function varies for each proposed alternative on the basis of predefined attributes established during the design of the SP survey. Each of the proposed alternatives comprises attributes that influence its utility value, as well as coefficients determined through statistical analysis on the basis of previously collected user responses. Alternatives can have very different utility functions since each alternative has some unique attributes attached to it, but they can also have similar utility functions owing to similarities in operating functionalities. It is important to note that most utility functions derived through SP studies have traditionally focused on single mode choice context, while the complexity of multimodal trip chains, including transfers and multimodal coordination, has often been underrepresented. A key challenge remains as to how to appropriately define and capture multimodal attributes, such as transfer time, synchronization, and service integration, where carefully designed SP experiments could provide more realistic insights into user preferences.

While the multinomial logit model remains the most widely used due to its simplicity and closed-form probabilities, its main limitation is in the restrictive Independence of Irrelevant Alternatives (IIA) property, often illustrated by the “red bus/blue bus” problem [15]. This assumption imposes proportional substitution across alternatives, which can lead to unrealistic forecasts when new or similar options are introduced. Historically, MNL dominated the early applications of SP methods in transport during the 1970s and 1980s because of its tractability and data requirements [61]. Empirical comparisons confirm that MNL performs poorly in cases with similar alternatives due to this IIA property [62].

To address this, nested logit (NL) models were developed in the 1980s and 1990s, partially relaxing the IIA by allowing correlation structures within groups of alternatives, improving realism without sacrificing analytical tractability. NL models are particularly effective when alternatives can be meaningfully grouped, such as transit modes with shared characteristics [63]. Around the same period, multinomial probit (MNP) models also appeared as a theoretically robust alternative, using normally distributed error terms to allow for flexible substitution patterns and correlation across alternatives. However, their computational demands have constrained their use in large-scale transport studies, particularly in the presence of many alternatives or large datasets [62].

The 1990s and 2000s saw the rise of the mixed logit (ML) models extending the framework further by introducing random parameters that capture unobserved preference heterogeneity. In this context, heterogeneity refers to differences in how individual travelers value attributes such as time, cost, or comfort which cannot be explained simply by observable socio-demographic variables. They provide superior forecasting accuracy and flexibility over MNL, especially in heterogeneous populations [64,65,66]. This development represented a major advance in SP modeling, with ML emerging as the dominant tool for demand forecasting across both passenger and freight applications [67].

More recently, hybrid choice models and machine learning techniques such as random forests and neural networks have emerged as promising alternatives. These methods often outperform traditional discrete choice models in terms of prediction accuracy but tend to sacrifice transparency and behavioral interpretability [68]. Advances in survey design and the use of psychometric indicators have also improved the realism of SP experiments, while joint SP and RP modes are increasingly used to strengthen external validity [69]. The field of discrete choice modeling has expanded rapidly beyond transportation, with health economics now surpassing transport in publication volume since 2014, while applications in environmental valuation are also steadily increasing. Transportation research remains vital, especially in areas such as electric and automated mobility, but the disciplinary spread highlights the broad relevance and adaptability of SP methods [70].

Consequently, the standard multinomial logit model is frequently treated as a baseline, with more advanced specifications applied when greater behavioral realism or forecast reliability is required. This trajectory highlights the broader historical development of SP in transport, from early reliance on simple logit structures to today’s flexible, hybrid and machine learning approaches that aim to balance predictive power with interpretability.

3.2. Key Areas of Application

SP methods are crucial in transport research for understanding hypothetical travel behavior and preferences. They are widely used to explore various aspects of transportation, including the mode choice, route choice, and service attributes. Key applications of SP methods also include assessing pricing strategies, fare policies, and preferences for emerging transport technologies such as electric and autonomous vehicles.

3.2.1. Mode Choice

Stated preference (SP) methods are widely applied in mode choice analysis because they enable researchers to identify the most influential attributes even when revealed preference (RP) data are insufficient. By quantifying user trade-offs, SP models provide insights into the probability of choosing each alternative under hypothetical scenarios, offering a reliable basis for policy evaluation. Across a wide body of literature, travel time and travel cost consistently emerge as the most decisive determinants of mode choice [12,71,72]. In addition to these core variables, reliability and convenience, captured through waiting time, transfer frequency, and service headways, are shown to be equally important and often have a stronger marginal effect than further reductions in in-vehicle travel time [73,74]. These findings underline that policies targeting time savings alone are unlikely to achieve significant mode shift if reliability and convenience are not simultaneously addressed.

In the case of private cars, users are particularly sensitive to parking-related factors. Several studies demonstrate that parking costs, search time, and access distance are among the strongest deterrents to car use, often outweighing the effect of fuel costs or in-vehicle time [72,75]. This highlights the effectiveness of parking pricing and availability policies as levers for reducing car dependence. Raising parking fees and reducing the supply of free spaces can encourage transitions toward public transport or shared modes, especially when combined with reliable alternatives.

Public transport quality attributes also significantly affect choice. Cleanliness, comfort, and perceived safety are repeatedly identified as drivers of satisfaction and loyalty, while crowding, irregular headways, and insufficient passenger information remain persistent barriers [76,77,78]. Importantly, SP studies show that passengers are willing to pay extra for improvements in these service dimensions, suggesting that fare increases may be acceptable when clearly linked to quality upgrades. Such findings support the view that improving service quality, rather than focusing on pricing alone, is a more effective strategy for attracting and retaining passengers.

Beyond traditional modes, a growing number of studies investigate multimodal and shared alternatives. Transfers emerge as one of the most sensitive elements, where every additional transfer reduces utility and discourages public transport use, reinforcing the importance of seamless integration across modes [79,80]. Micromobility options, such as shared bicycles and e-bikes, are increasingly seen as effective first- and last-mile connectors. Their adoption is strongest among younger and lower-income users, groups that often rely on affordable and flexible solutions. Car-sharing services represent another attractive alternative within multimodal systems. Free-floating car-sharing in particular can substitute public transport during peak hours, though cost remains the dominant attribute in these decisions [81,82].

Mobility as a Service (MaaS) has introduced a broader framework for integration, combining conventional public transport with emerging mobility services into subscription-based bundles. SP research indicates a high willingness to pay for such packages, but acceptance is highly sensitive to the composition and pricing of the bundle. Public transport generally serves as the backbone of MaaS, while car rental and taxis are less valued in integrated offers [83,84]. These results emphasize the importance of careful package design, where reliability and flexibility determine the attractiveness of MaaS compared to traditional single-mode use.

Geographical and contextual differences further moderate mode choice. In European metropolitan areas, cost, reliability, and comfort are decisive, particularly when embedded in multimodal hubs and MaaS solutions [83,84]. In Asian megacities such as Beijing or Riyadh, policies targeting fuel prices, parking restrictions, and station accessibility are often more influential than marginal reductions in in-vehicle time, while micromobility solutions play a central role as first-/last-mile connectors [35,80,85]. In North American metropolitan and suburban contexts, minimizing transfers and ensuring reliability dominate, with integrated ride-sourcing–public transport services emerging as promising competitors to private cars [74,79]. By contrast, in smaller and mid-sized European cities, car-sharing schemes and parking management policies have proven especially effective in shifting users toward sustainable alternatives [75,81].

In summary, travel time, cost, and reliability are universal determinants of mode choice, but contextual differences highlight that no single policy can achieve mode shift across all regions. Measures targeting parking management, transfer penalties, and service quality improvements are often more effective than fare reductions alone, particularly when combined with multimodal integration and shared mobility options. SP evidence thus provides policymakers with a nuanced understanding of how universal drivers interact with local conditions to shape sustainable mobility transitions [12,35,71,72,74,75,77,79,80,81,82,83,84,85,86,87,88,89,90].

3.2.2. Route Choice

Route choice is a critical step in journey planning, where users seek to maximize their utility by selecting routes that best align with their preferences. SP methods have been widely used to identify how attributes such as safety, travel time, infrastructure, and environmental conditions influence these decisions.

For pedestrians, safety and comfort dominate route preferences. SP studies show that sidewalks, traffic signals, and calming installations strongly increase the attractiveness of routes [91]. Perceptions of personal security are also critical, as women and pedestrians walking at night avoid poorly lit or vacant areas, and mixed-use or residential environments are preferred over isolated zones [92,93]. Even aesthetic factors, such as the presence of trees, can make routes more attractive by enhancing perceived comfort and livability [92]. These findings underline that pedestrian route choice is influenced not only by physical distance but also by psychological and environmental perceptions.

Among cyclists, safety consistently emerges as the primary determinant of route choice. Dedicated cycling lanes, wider roads, and fewer intersections increase utility, while curbside parking and shared infrastructure with motorized vehicles reduce it [94,95,96]. Cyclists of all ages are highly sensitive to traffic volumes, with elderly and younger users especially preferring routes with limited exposure to cars [95]. Although travel time remains important, many cyclists accept longer trips in exchange for segregated lanes or green surroundings [96]. E-scooter users show similar preferences: infrastructure quality, intersection density, and traffic levels significantly shape their route choice [97].

For motorized traffic, efficiency plays a greater role. Motorcyclists prioritize routes based on smooth traffic flow and minimal delays [98]. Car drivers often prefer familiar routes, even when navigation apps suggest alternatives, but are willing to switch when informed of incidents or when traveling in unfamiliar areas [99]. Real-time information strongly influences route choice: SP studies show that delay length and road user charges reduce route utility, while reliable traffic updates encourage adaptation [100]. These findings emphasize that trust in real-time data is central to influencing driver decisions.

SP methods are especially valuable in capturing the preferences of vulnerable groups. Children in rural India and China, for example, prefer routes with bicycle lanes, fewer intersections, and lower traffic speeds, highlighting the importance of safe infrastructure for school travel [101,102]. Tourists, by contrast, emphasize cost and distance, seeking routes that minimize travel time and expenses while visiting attractions [103]. These examples show how SP experiments can uncover unique priorities for distinct user groups, which are often overlooked in aggregate models.

For battery electric vehicle (BEV) users, route choice is uniquely constrained by energy requirements. The state-of-charge (SOC) at trip origin and the availability of charging stations strongly shape preferences. Routes with fast-charging stations, shorter charging times, and convenient locations are consistently favored [104,105]. These findings demonstrate how technological change introduces new attributes into traditional route choice models, underscoring the adaptability of SP methods.

Global evidence reveals clear regional differences. In North America, safety and infrastructure quality are critical for pedestrians and cyclists, with sidewalks and bike lanes being decisive [91,106]. In Europe, where cycling networks are well developed, cyclists prioritize segregated lanes, fewer intersections, and routes with lower traffic volumes, with age-specific variations in sensitivity to safety and comfort [94,96]. In Asian contexts, perceptions of safety are equally decisive, particularly for women and nighttime pedestrians [92,93]. Motorcyclists in Asia primarily respond to traffic flow and delay conditions [98], while BEV users depend heavily on charging infrastructure [104,105]. Finally, rural and school contexts underline the importance of safer routes with fewer intersections and dedicated infrastructure for children [101,102].

Overall, route choice decisions are universally shaped by safety, travel time, and infrastructure quality. Yet regional, demographic, and technological factors significantly moderate these preferences. For policymakers, this implies that interventions such as improved lighting, segregated cycling lanes, and reduced transfer penalties may be more effective in encouraging active and sustainable route choices than travel time reductions alone. Similarly, designing routes that consider the needs of vulnerable groups and emerging technologies is essential for achieving broader goals of accessibility and sustainability [91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107].

3.2.3. Service Attributes

Public transport is a complex system shaped by multiple attributes, ranging from travel time and frequency to comfort, safety, and pricing. SP methods provide an effective way to quantify how users perceive these service dimensions, especially attributes that are difficult to capture with RP data.

Across studies, travel time consistently emerges as the most critical factor for mode choice. When public transport travel time is competitive with that of private modes, users are more likely to switch, particularly on short trips [108]. In-vehicle time and headways become more important as trip distance increases, and users often show a willingness to pay more for convenience and reliability [73]. In addition, comfort, cleanliness, and vehicle frequency are highly valued [38,76]. Together, these findings underline that users reward improvements in quality as well as efficiency.

SP studies also highlight the disutility created by factors such as fare prices, long waiting times, crowding, and transfers. High vehicle occupancy substantially reduces perceived comfort. For instance, the perceived in-vehicle time at 6 passengers/m² is experienced as nearly twice as long as in vehicles with low occupancy [109]. Transfers are particularly costly in terms of utility, with users perceiving them as equivalent to 10 additional minutes of waiting or walking [110]. These insights emphasize that reliability and comfort improvements may be more influential than simple fare reductions in attracting new users.

Geographical context strongly moderates the importance of service attributes. In European cities, comfort, cleanliness, and tariff structures are decisive factors for both existing and potential users [38,76]. In Latin America, irregular headways and crowding are the most pressing challenges, undermining confidence in public transport [78]. In Asian contexts, heterogeneity is pronounced: men are more sensitive to travel time, women and low-income users to fares, and older passengers to crowding [111]. In African cities, where basic infrastructure is often lacking, the quality of stations, perceived safety, and affordability are dominant [112].

Overall, waiting time and reliability emerge as universal determinants of public transport attractiveness. However, socio-demographic and regional differences strongly shape how service quality is perceived. For policymakers, this implies that interventions targeting overcrowding, headway regularity, and station safety may be more effective in encouraging modal shift than fare reductions alone. At the same time, users demonstrate a clear willingness to pay more for cleaner, more comfortable, and more reliable services, suggesting that quality improvements can justify moderate increases in fares if communicated transparently [38,73,76,78,108,109,110,111,112,113].

3.2.4. Pricing and Fare Policies

Pricing and fare policies are among the most influential instruments for shaping travel behavior, as they affect demand, modal share, and perceptions of fairness. SP methods provide insights into how fare levels, structures, and complementary pricing tools impact user decisions, capturing both existing practices and future scenarios such as MaaS bundles.

Users respond not only to the absolute price of services but also to how fares are structured and communicated. Research in the UK demonstrated that the complexity of fare calculation itself influences demand, with counterintuitive effects: simplifying tariff structures reduced demand by 3–11%, while more complex structures increased it by up to 15% [114]. These results highlight that transparency and fare design can shape perceived value as much as cost levels [114].

Evidence shows that free or subsidized fares can attract users but are insufficient unless service quality is guaranteed. In Athens, free public transport increased ridership, yet speed and comfort remained decisive [115,116]. In Malaysia, users were willing to pay higher fares if frequency improved [116], while in Belgium, student discounts strongly shifted travel behavior away from cars [117]. In Bogotá, equity emerged as central: low-income users were disproportionately affected by fare increases [118]. Together, these studies show that equity and quality improvements must accompany fare changes to achieve lasting impacts.

SP methods also help to identify groups most prone to fare evasion. In Belgium, young men were most likely to evade fares due to price perceptions and lack of inspection [117]. In Bogotá, insufficient enforcement was the leading cause of evasion, with younger, frequent users most likely to avoid paying [118]. Importantly, satisfaction with service quality was shown to reduce evasion, suggesting that enforcement strategies must be linked to service improvements.

Fare policies extend beyond tickets to include parking management. A Spanish university case study showed that introducing parking fees significantly reduced car use while increasing the attractiveness of PT and micromobility [119]. Parking pricing thus appears as an effective lever for discouraging car dependency and supporting sustainable transport.

Emerging services such as MaaS require new approaches to pricing. In Sydney, Australia, users favored subscription-based models, whereas in Tyneside, UK, pay-as-you-go service was preferred to maintain existing travel habits [120]. In the Netherlands, long-term subscriptions were valued over monthly plans, with public transport as the backbone and taxis or rentals being the least attractive [84]. In Switzerland, users were willing to pay for bundles combining public transport, car-sharing, and park-and-ride, while bike-sharing and taxis were valued less [121]. These results confirm that pricing strategies must reflect local travel cultures and emphasize integration to ensure MaaS adoption.

SP evidence highlights strong regional contrasts. In Europe, tariff clarity and MaaS bundling are decisive for acceptance [84,114,121]. In Latin America, equity dominates, with low-income groups most sensitive to fare increases [118]. In Asia, socio-demographic heterogeneity creates variation: men are more sensitive to time, older users to crowding, and low-income users to fares [111]. Free or subsidized fares, effective for students in Belgium and Athens, show that cost reductions alone do not guarantee success without parallel improvements in comfort and reliability [115,117]. Parking charges, as demonstrated in Spain, remain highly effective in smaller or controlled environments [119].

Overall, SP studies confirm that fare levels and structures strongly influence demand and modal share, yet their effectiveness depends on simplicity, fairness, and integration with service quality. For policymakers, this implies that equity-oriented fare policies, combined with reliable and comfortable services, parking charges, and MaaS bundles, are more effective levers for promoting sustainable urban mobility than fare reductions alone [84,111,114,115,116,117,118,119,121,122].

3.2.5. Technological Innovations

SP methods are essential for evaluating acceptance of emerging transport technologies, where RP data are not yet available because such services do not yet exist in the real market. By testing user reactions to hypothetical scenarios, SP surveys capture how potential passengers value cost, reliability, safety, and additional features in technologies such as autonomous vehicles (AVs), autonomous buses, battery electric vehicles (BEVs), and air taxis.

Research shows strong but heterogeneous preferences toward AVs. In South Korea, users valued modern features such as connectivity and voice command [123], while in Japan, respondents were willing to pay up to USD 7000 extra for private AVs, with younger travelers more open to shared services [124]. Studies in Australia confirmed that shared AVs are attractive when costs and waiting times are competitive, though high-income groups consistently prefer private options [125]. Across contexts, willingness to adopt AVs depends on socio-demographic factors and perceptions of convenience, with recent work highlighting that users still do not perceive AV travel time as fully productive [126].

AVs are often viewed as complementary feeders to public transport. In the Netherlands, AV shuttles were attractive as first- and last-mile connections to rail, especially when waiting times were minimized [127]. Public transport passholders without cars were particularly inclined to use autonomous buses, with on-demand options more attractive than scheduled services [128]. These findings suggest that AVs can strengthen multimodal networks rather than compete with existing PT services.

BEV adoption highlights how technology introduces new attributes into SP models. Route preferences are strongly shaped by state-of-charge and charging infrastructure. Users favor routes with fast charging, shorter charging times, and convenient station locations [104,105]. In Beijing, shared BEVs were seen as substitutes for taxis on longer trips, though range anxiety and charging availability remain key barriers [129].

SP studies also explore future disruptive modes. Research in U.S. cities found highly polarized attitudes toward eVTOL air taxis: around 14% of respondents would always adopt them, while 14% would always reject them. Male and ridesharing users were more likely to choose the service, with willingness-to-pay around USD 25 per hour [130]. Safety, travel time, and cost remain critical determinants for wider acceptance.

Across technologies, price and waiting time are the most consistent drivers of adoption, while comfort and the potential for multitasking increase willingness to adopt AVs and air taxis [126]. Demographic differences are significant: younger, male, and tech-oriented users are more willing to adopt disruptive modes, while older and higher-income groups prefer private ownership. Regional differences further shape outcomes: Asian studies emphasize heterogeneity and barriers such as range anxiety [123,129], while European research highlights integration with PT and multimodal hubs [127,128], and U.S. studies focus on aerial mobility, revealing both strong willingness-to-pay and resistance [130].

SP studies show that disruptive technologies are attractive when they are affordable, reliable, and integrated with existing transport systems. Adoption, however, is strongly shaped by demographics, perceptions of safety, and supporting infrastructure. For policymakers, this means that investments must go beyond technology itself to include charging networks, multimodal hubs, and regulatory frameworks that address equity and trust [89,123,124,125,126,127,128,129,130,131].

3.2.6. Overview

To provide a concise overview of the key findings discussed in this section,

Table 2. Summary of reviewed stated preference application and key findings.

Area of Application		Related Literature	Model Used	Key Findings
Mode choice	Key attributes in mode choice	[35]	NL	Travel distance, time, and cost are key factors affecting mode choice.
		[12]	MNL
		[71]	MNL
		[72]	MNL
	Attributes to make users switch to public transport	[86]	/	Users would switch to public transport if convenience improved (fewer transfers, better travel info), reliability, comfort and punctuality increased, and ticket prices remained competitive.
		[81]	ML
		[75]	MNL; ML
		[79]	ML
		[87]	ML
		[80]	ML
		[77]	Hybrid-choice
		[74]	ML
	Multimodal systems	[81]	ML	Cost changes significantly influence car-sharing service use; MaaS models are gaining traction, with users willing to pay for bundled mobility services. First- and last-mile solutions like shared micromobility (e-scooters, bikes) enhance multimodal integration. Users prefer multimodal hubs that ensure seamless transfers between transport modes.
		[75]	MNL; ML
		[88]	ML
		[89]	NL
		[83]	Hybrid-choice
		[84]	ML
Route choice	Pedestrians	[91]	MNL	Pedestrians prioritize shorter distances, safety, and well-lit routes, especially for nighttime travel.
		[93]	MNL
		[92]	Probit
	Cyclists, e-scooters	[91]	MNL	Cyclists and e-scooter users prefer dedicated bike lanes, fewer intersections, and lower vehicle traffic volume.
		[107]	MNL
		[102]	Binary logit
		[94]	ML
		[97]	Logit
	Battery electric vehicles (BEVs)	[104]	NL	BEV users prioritize charging station availability, shorter charging times, and optimal routes based on battery levels.
		[105]	ML
Service attributes		[78]	Hybrid-choice	Key factors include travel time, frequency, cleanliness, and seating availability, while high vehicle occupancy negatively impacts user experience.
		[108]	/
		[76]	MNL
		[73]	ML
		[113]	NL
		[109]	MNL
		[110]	MNL
Pricing and fare policy	Public transport	[115]	MNL	Users prefer reliable, faster, and more comfortable public transport over free transport.
		[116]	/
	Parking	[119]	ML	Implementing parking fees can influence modal share.
	Mobility-as-a-Service	[122]	MNL	Users prefer to have bundled services, with public transport, car-sharing services, and park-and-ride as the most preferred options to pay.
		[84]	ML
		[121]	ML
Technological innovations	Autonomous vehicles	[124]	ML	Users are willing to pay a premium for AVs; younger individuals prefer shared AVs over private ones.
		[131]	MNL
		[126]	MNL
	Public transport	[128]	NL	Public transport users are open to autonomous buses, particularly as last-mile solutions. Younger males are interested in air taxi commute services.
		[129]	NL
		[130]	ML

summarizes the main attributes and insights identified across the reviewed application areas.

4. Discussion

The SP method offers unique insights into user behaviors by capturing hypothetical travel choices, enabling the exploration of trends essential for fostering sustainable and efficient transport systems. Through its application, researchers have identified critical factors that influence decisions related to route selection, multimodal integration, public transport service attributes, pricing strategies, and the adoption of emerging mobility solutions.

Among the most commonly analyzed attributes are travel time and cost, which remain consistent determinants of user choice across all transport modes. However, other specific attributes such as comfort, environmental impact, and service reliability appear to be less frequently used, suggesting a limited exploration of more delicate user preferences.

An analysis of the modeling approaches employed in the reviewed studies reveals strong reliance on well-established discrete choice frameworks. The multinomial logit (MNL) model and its extensions remain the predominant tools for analyzing stated preference data in the context of sustainable and multimodal transport, reflecting their computational simplicity and interpretability. The mixed logit (ML) model, which allows for random taste variation and captures unobserved heterogeneity among respondents, appears with comparable frequency and demonstrates the growing recognition of preference diversity in travel behavior research. Nested logit (NL) models, used to account for correlation within groups of alternatives, are less common but still present where hierarchical mode structures are relevant. Notably, only a few studies apply more advanced hybrid choice models, such as SEM-DM, which integrate latent attitudinal factors with standard utility formulations. This distribution suggests that while the field is gradually adopting richer behavioral representations, there remains considerable potential for broader use of advanced models to better capture the complexities inherent in multimodal and sustainable travel decisions.

Still, certain gaps remain, with limited research of integrating SP with real-time data, dynamic pricing strategies, inclusion of behavioral complexity, and multimodal trip chain and transfers. As technology develops, many users are increasingly relying on dynamic information such as live traffic updates, vehicle arrival predictions, seat availability, or fluctuating prices based on demand levels. All these factors play a significant role in travel decisions in real time but are often absent from static SP scenarios. As a result, traditional SP designs may not accurately reflect how travelers respond under realistic, time-sensitive conditions. One promising experimental direction is the design of real-time SP experiments, where respondents face trip alternatives that draw attribute values directly from live data feeds (e.g., waiting times, fares, or occupancy level). For example, participants could choose between a crowded bus arriving in 3 min with price of EUR 1.50, a shared bike available immediately for EUR 0.70, or a ride-hailing option with an 8 min wait time and price of EUR 3, with attribute levels updating dynamically at the moment of choice. Methodologically, this setup can be implemented by linking surveys to real-time APIs from transport providers and analyzing the data with mixed logit or hybrid models, thereby increasing realism and external validity.

Another challenge is the simplified treatment of multimodal trips. Many SP studies focus on single-mode trips and ignore the complexity of multimodal travel chains, which involve transfers, walking segments, service coordination, and synchronization between modes. A promising approach is the use of SP designs, where respondents evaluate entire trip chains such as car–walk–metro or bike–bus–car rather than isolated legs. For example, a choice task could compare a direct bus against a metro–bike combination by incorporating transfer penalties, waiting times, and coordination attributes. These designs can be structured as sequence-based choice tasks and analyzed with nested or mixed logit models to capture the interdependence of decisions across modes.

Additionally, behavioral complexity remains underrepresented in many SP applications. Standard models often assume that individuals make fully rational choices based on measurable attributes, while in reality, travel decisions are influenced by psychological, emotional, and social factors. Hybrid choice experiments that combine SP tasks with psychometric indicators (e.g., trust in technology, environmental concern, risk perception) offer a pathway to better capture these latent influences. Embedding attitudinal scales directly into SP tasks would allow for estimation of latent variables alongside utility parameters, linking psychological constructs to observed choices. Such design can be analyzed through hybrid discrete choice models, which integrate structural equation modeling with standard logit formulations. By doing so, researchers can quantify how non-rational factors, such as habit, social influence, or environmental concern, modify stated preferences, providing richer insights for both theory and policy. For instance, a longitudinal SP survey could follow the same respondents before and after the launch of a shared e-scooter program, measuring how changes in perceived safety or environmental concern translate into actual adoption.

Advancements in SP methods open up several promising opportunities to address key challenges in transportation research and planning. One significant area of potential lies in leveraging emerging technologies such as Mobility as a Service (MaaS) platforms. By examining the interaction between these technologies and user preferences, SP methods can facilitate the creation of integrated transport solutions that are seamless, efficient, and user-centric.

Another opportunity is the expansion of SP studies to encompass more diverse demographic and geographic contexts. By including underrepresented regions and populations, researchers can generate more inclusive insights, ensuring that transportation solutions are equitable and tailored to meet the needs of all users.

Additionally, advancements in hybrid modeling approaches offer the possibility of combining SP data with RP data. This integration enhances predictive accuracy by providing a more comprehensive understanding of user behavior across both hypothetical scenarios and real-world contexts.

SP methods also present valuable opportunities to promote sustainable travel behavior through the evaluation of multimodal hubs and integrated ticketing systems. These approaches can reduce barriers to multimodal transport, encourage environmentally friendly travel choices, and increase overall user convenience. By capitalizing on these advancements, SP methods can play a pivotal role in shaping innovative, inclusive, and sustainable transportation systems in the future.

To synthesize the reviewed evidence, Figure 4 represents a conceptual framework linking stated preference findings to policy levers, behavioral impacts, and broader system outcomes. This framework presents how empirical insights can inform targeted interventions and effects from individual preferences to transport system performance.

5. Conclusions

This paper presents a structured review of the use of stated preference techniques in transport research, with a particular focus on their application to sustainable and multimodal mobility systems. Drawing on studies published between 2000 and 2024, the review identifies five dominant application areas: mode choice, route choice, service attributes, pricing and fare policy, and technological innovation. Across these areas, travel time and cost remain the most influential attributes, while comfort, reliability, environmental impact, and multimodal coordination are still underrepresented. The diversity of modeling approaches also shows methodological evolution, with studies moving from standard logit structures toward mixed and hybrid models that better capture heterogeneity and behavioral complexity.

From a policy perspective, several priorities emerge. First, integrated ticketing systems and MaaS bundles should be piloted more widely, since current evidence shows that seamless payment and bundled services increase the attractiveness of multimodality. Second, dynamic pricing instruments, such as congestion-based fares and parking charges, should be combined with high-quality service provision, as SP studies confirm that they are more effective when paired with reliable and comfortable alternatives. Third, infrastructure investments should focus on multimodal hubs, segregated cycling lanes, and safe pedestrian corridors, which reduce transfer barriers and encourage sustainable mode shifts. Finally, equity-oriented measures such as targeted subsidies or discounts are essential to ensure that sustainable transport remains accessible for low-income and vulnerable groups to tackle the problem of transport poverty.

Overall, SP evidence highlights that policy effectiveness depends not only on cost levels but also on convenience and integration. By using these insights, policymakers can create urban mobility systems that are both sustainable and socially equitable, and SP research can also evolve toward a more holistic and realistic understanding of travel behavior with the ultimate goal of more inclusive and sustainable transport systems.