How Can I Find My Ride? Importance of User Assistance in Finding Virtual Stops for Shared Autonomous Mobility-on-Demand Services

Abstract

Future mobility concepts, such as Shared Autonomous Mobility-on-Demand (SAMOD) services, have the potential to contribute to sustainability goals and enhance connectivity between rural areas and urban public transport networks. The SAMOD concept relies on virtual stops, accessible via a smartphone application, where passengers are individually picked up. This study analyzed the importance of six key attributes of a SAMOD journey: travel time, price, available information, distance to the stop, navigation to the virtual stop, and identification of the virtual stop. Using a choice-based conjoint analysis (N = 461), participants were repeatedly presented with two SAMOD journey options, each varying in attributes, and were asked to indicate their preference. The findings reveal that all six attributes significantly influenced travel decisions. Subgroup analyses further indicated that the importance of these attributes varied by gender, age, travel context, and frequency of public transport use. Notably, SAMOD-specific attributes, such as navigation to and identification of the virtual stop, were rated as nearly as critical as traditional factors like travel time and cost. Based on these findings, actionable recommendations for transport planners and policymakers are proposed to facilitate the successful implementation of SAMOD services.

1. Introduction

Public transportation systems play a vital role in ensuring efficient and sustainable mobility in urban areas. Over the years, traditional fixed-route services have served as the backbone of public transportation, offering scheduled services along predefined routes. However, emerging technologies and changing mobility preferences have given rise to on-demand services (ODSs) in public transportation. ODSs are characterized by not operating with fixed routes or schedules but considering the individual demands of passengers regarding when they want to travel, where they want to be picked up, and to which destination they want to travel. For more than two decades already, ODSs have been seen as a promising way to modify people’s travel behavior and address some of the limitations of traditional transit systems [1]. Advantages include the flexibility and convenience in allowing users to request rides precisely when and where they need them. ODSs can bridge gaps in the transportation network, serving as a viable solution for remote areas and addressing the problem of first- and last-mile connectivity [2] as well as complementing public transport by providing service at off-peak hours [3].

Despite their increasing availability, ODSs still face challenges in gaining widespread popularity. Several studies showed that ODSs are rarely preferred in comparison with private cars or other public transportation modes [4,5]. These results were consistent across different rural or urban areas [6,7]. One of the reasons is the cost intensiveness. Implementing and operating ODSs requires substantial investments in technology infrastructure, fleet management, and driver compensation, which makes it particularly difficult to make the service financially viable [8]. Furthermore, the aspirational goal of replacing private car use with ODSs has been of limited success. Studies have shown that ODSs attract users who would have otherwise chosen public transportation rather than individuals who primarily rely on private cars [9]. As a consequence, traditional ODSs do not reduce but rather increase the total number of vehicles on the road, contributing to higher traffic congestion and environmental pollution [10,11].

Technological improvements could overcome these challenges if they enabled ODSs to drive autonomously and combine the trip requests of several passengers. Algorithms would compute the best possible route for the shuttle and collect the passengers one by one, guiding them to their desired destination [12]. This concept for a future public transportation system is called a Shared Autonomous Mobility-On-Demand (SAMOD) service. It represents a transformative approach to public transportation that combines the benefits of shared ODSs and autonomous vehicles. Passengers request the transportation services via a mobile application, giving them access to vehicles operating autonomously in a predefined area. These vehicles will collect the passengers at designated locations—so called virtual stops. Several advantages of SAMOD are being discussed in the literature. In a review article, Millonig and Fröhlich (2018) [13] concluded that SAMOD operation would have a beneficial impact on the availability, affordability, and accessibility of public transportation. Other studies highlighted the potential of SAMOD to reduce costs for passengers, concluding that the costs for SAMOD would be significantly lower than for traditional ODSs or even lower than for private car use [14]. In particular, the feature of shared use enables multiple individuals to be efficiently grouped together, which lowers operational costs, as shown by Hyland and Mahmassani (2020) [15] in a simulation study. Other simulations show the possibility of SAMOD decreasing the overall number of vehicles on the road [16] while optimizing fleet management by reducing idle times of vehicles [17]. All in all, SAMOD is considered a promising way of complementing mass transportation and making public transport more attractive [18].

For SAMOD to be successfully implemented in the public transportation system, the acceptance of the users is an essential precondition. This study addresses the user requirements of SAMOD with a special focus on accessing the vehicle via virtual stops. Even though empirical findings about the user perspective on the usage of SAMOD are still scarce due to the novelty of the concept, some results can be found. A survey conducted by Mantouka et al. (2022) [19] in Athens, Greece found that more than half of the respondents would use an autonomous public transportation vehicle. In a stated preference study, Krueger et al. (2016) [20] showed that the acceptance and adoption of SAMOD depend on user characteristics as well as service aspects. Among the latter, the travel time, travel costs, and waiting time play particularly crucial roles in users’ appraisal. These findings are consistent with other studies highlighting the importance of the travel time and travel costs for traditional public transport services [21] as well as ride-pooling services [22].

In König and Grippenkoven’s (2020a) [22] stated preference study, the trip context was also identified as an influential factor in the perceived importance of various attributes of a ride-pooling service. Participants exhibited greater sensitivity to the travel time and changes in departure time when placed in a scenario where time pressure was induced. Furthermore, Krueger et al. (2016) [20] concluded that, due to their multimodal travel patterns, young individuals are more likely to adopt SAMOD than older persons. Other results showed that the acceptance of sharing rides in autonomous vehicles is negatively correlated with the trip length and detours [23,24]. This effect can potentially be reduced by providing further information like the name or a picture of the fellow passenger [25]. Trust has also been shown to be a determinant of the intention to adopt autonomous shuttles [26,27]. It not only serves as a direct predictor of adoption intent but also exerts an indirect influence through perceived usefulness, perceived ease of use, and perceived safety, as shown in a review article [28]. In recent years, several studies found an increase in acceptance of self-driving vehicles [19,29,30], even though there are concerns regarding the perceived safety and performance ability, particularly in relation to driving speed [30,31]. Previous research has identified some cultural differences in attitudes toward autonomous shuttles. A review article by Jing et al. (2020) [28] revealed that elderly people in China have a more positive attitude towards autonomous shuttles compared to older persons in the United States, likely because of the fact that many older individuals in China do not possess a driving license. A study conducted in Singapore indicated a general acceptance of autonomous vehicles among survey respondents, but there are concerns regarding the technological reliability and the prevailing legal framework [32]. Furthermore, a questionnaire-based study in South Africa identified hedonistic motivation and performance expectations as significant predictors of the intention to adopt autonomous shuttles, thereby corroborating findings from previous studies [33].

Another critical factor regarding the usage of public transportation is the walking distance to the stop [34,35]. On-demand mobility systems like SAMOD will no longer need physical stops but instead rely on the concept of virtual stops. Within this framework, the term “virtual” denotes that the access point lacks a physical manifestation in contrast to conventional bus stops. As a result, the positions of virtual stops are exclusively discernible through digital media such as smartphones with suitable applications. An algorithm computes the most effective route for the shuttle and pools passengers with similar pick-up locations and destinations, providing each passenger an individual virtual stop and collecting them one by one. Empirical evidence indicates that in comparison to a door-to-door service, the implementation of virtual stops significantly improves the percentage of pooled passengers while decreasing the number of stops and the overall distance covered [36]. With the reduction in detours, leading to a reduced travel time for passengers [37] and the potential for higher occupancy rates, it can be concluded that virtual stops can lead to an improvement in overall system efficiency [38].

Czioska et al. (2017) [39] showed that the amount and distribution of virtual stops affect the performance and convenience of ride-sharing systems. In a simulation study, Armellini et al. (2021) [40] examined the optimal locations for virtual stops in an urban area concerning different aspects like accessibility, safety, walking distance, seating availability, or roofing. An expert utility analysis conducted by Harmann et al. (2022) [41] revealed that for users, virtual stops located at streetlamps are most useful, while for providers, virtual stops at intersections are most beneficial due to better routing efficiency. Other important factors to consider when positioning virtual stops are legal aspects like the permission to stop a vehicle at a designated location, as well as the impact on the traffic flow at that location [42]. Explicit communication by the vehicle like the usage of indicator lights as well a smooth driving behavior with defensive braking dynamics when approaching the virtual stop increase the trust of passengers and facilitate the positive perception and timely identification of the vehicle [43]. Some research has been performed on human-centered digital technologies guiding users to virtual stops. Hub et al. (2020) [44] found that a simple prototype using augmented reality was evaluated positively in terms of understandability and user experience. Smartphone interface design solutions were created for different use cases like navigation to the pick-up location, identifying the pick-up location, and identifying the vehicle [45]. According to the authors, a well-designed human–machine interface for mobile augmented reality significantly reduces the workload and improves user acceptance, leading to a highly assistive character of virtual stops with an adequate HMI [46].

The present study is intended to follow up on the presented results, adding insights on the effect of factors that have not been examined before on the intention to use SAMOD. The selection of the attributes that should be closer investigated in this study was based on the literature presented as well as a qualitative workshop (N = 15). Summarizing the presented literature, it can be said that essential attributes for the decision on whether a ride is taken or not are the price, the travel time, and the distance to the stop. These attributes were shown to be important determinants for the use of traditional public transport [21,47] as well as potential SAMOD use [20]. Regarding the specific aspects of autonomous shuttles and the user interaction via virtual stops, the existing research also highlighted the importance of the navigation to virtual stops and the identification of virtual stops [45]. Furthermore, participants in the qualitative workshop emphasized the importance of different kinds of available information about the trip.

As a conclusion, six attributes were included in the present study: price, travel time, distance to virtual stop, available information, navigation to the virtual stop, and identification of the virtual stop. As potential interacting factors, the person variables gender, age, and mobility behavior were included since these factors were shown to have an influencing effect on the perceived importance of public transport service attributes [20]. Finally, the effect of the context of the trip was also investigated as an interaction factor. The context of the trip primarily relates to whether the participants are exposed to time pressure in a described scenario. This can be caused, for example, by having to catch a train at the station or attend a doctor’s appointment. König and Grippenkoven (2020a) [22] found it to have an influence on the perceived importance of travel time and shift of departure when traveling with a ride-pooling services.

Accordingly, the first objective of this study was to investigate the importance of various factors when choosing a journey with SAMOD. In particular, the importance of factors that arise specifically in the context of autonomous shuttles, like the navigation to the virtual stop or the identification of the virtual stop, were to be compared with the importance of factors that are also relevant for conventional public transport, like the price, the travel time, the available information, or the distance to the stop. The second objective was to identify potential interaction effects of these factors with demographic variables as well as participants’ habitual mobility behavior. Finally, the third objective was to investigate the role of the context of the trip.

2. Materials and Methods

2.1. Choice-Based Conjoint Analysis

To address the research objectives, an online study with a stated preference (SP) paradigm was conducted. This paradigm allows for the investigation of services not yet available in the market [48]. Within transportation research, it is a frequently employed method to analyze choice behavior and the potential use of novel technologies like autonomous vehicles [49]. The applied method was a choice-based conjoint analysis (CBC). In CBC, the participant is confronted with a series of choice sets, which are characterized by a set of attributes with different levels. The relative importance of the attributes is determined by the indicated preferences of the decision maker. With the possibility to include interaction factors, CBC allows researchers to test whether individual characteristics or context variables influence the valuation of attributes [50].

2.2. Selection of Attribute Levels

For each of the attributes investigated, 3 different levels had to be defined. To derive practical implications on the basis of the results, it was important that the levels represented realistic options for the service. For the attributes price, travel time, and distance to the stop, the definition of the levels was based on previous studies that already conducted a conjoint analysis with these attributes for ODSs [51,52] or conventional public transportation [53,54]. The levels of the remaining attributes were defined on the basis of the results of the qualitative workshop. An overview of all attributes with their different levels is shown in

Table 1. Attributes included in the conjoint analysis with their different levels.

Attribute	Levels
Price	EUR 2.50 EUR 3.00 EUR 3.50
Travel time	10 min 20 min 30 min
Distance to the stop	100 m 200 m 300 m
Available information	Departure time Departure time + arrival time Departure time + arrival time + route + delay
Navigation to the stop	Address Map Map + navigation
Identification of the stop	No feature Photo Augmented reality

.

The attribute navigation to stop is characterized by three levels, each representing varying degrees of navigational assistance. At the first level, only the address is provided. At the second level, a map is additionally supplied, while at the third level, a full navigation aid is included. Figure 1 illustrates the visualization of these different levels. The visuals were provided to the study participants.

The attribute identification of the stop encompasses three levels of assistance for identifying the stopping point. At the first level, no additional aid is provided for precise identification. At the second level, a photograph of the stop is supplied, while at the third level, an augmented reality feature is integrated, virtually displayed within the app. Figure 2 depicts visualizations of levels 2 and 3, which were provided to participants during this study.

2.3. Survey

To start the survey, participants had to indicate their minimum age of 16 years. After this, the questionnaire started with questions about demographic characteristics like the participants’ age, gender, educational qualification, and frequency of use of different means of transportation. In the next section, participants were provided with a detailed description of the functionality of a SAMOD, consisting of information about the booking process, the pick-up procedure, and routing calculation (see Figure 3). To consider the effect of the context of the trip, participants were randomly assigned to one of two scenarios in the beginning of the questionnaire. In the first scenario, participants were asked to imagine that the shuttle is used for a ride to a park five kilometers away, while the second scenario described a ride to a train station five kilometers away.

The main part of the questionnaire concerned the influence of the 6 SAMOD characteristics on the decision on whether a ride is taken or not. This was investigated with a choice-based conjoint analysis. Participants were confronted with 18 decision tasks in which they were to choose one of two suggested options of a trip with the SAMOD. Figure 4 shows an example of the decision scenario. The two trip profiles consisted of one specific level of each of the six attributes investigated (see below). Participants also had the option to indicate they would choose neither of the presented trip profiles.

The six attributes with three levels each enable the creation of 729 different trip profiles. To make the survey feasible in a reasonable time frame, the number of possible trip profiles was reduced with a fractional factorial design according to Aizaki and Nishimura (2008) [55]. Several fractional factorial designs with different numbers of trip profiles were created and compared regarding their efficiency (Ge). The highest efficiency (Ge = 1) resulted in a design with 18 trip profiles. To create the profile pairs, the 18 trip profiles were duplicated and randomly assigned to each other while making sure that no similar profiles were matched. These 18 pairs of trip profiles constituted the 18 decision tasks that every participant was presented with during the conjoint analysis. The tasks as well as the factors within the tasks were presented in a randomized order to prevent a potential confounding effect of presentation order.

2.4. Data Analysis

To investigate the influence of the six attributes price, travel time, distance to the stop, available information, navigation to the stop, and identification of the stop on the decision on which ride is taken and to assess the relative importance of these attributes, the stated preference data were analyzed with a logistic regression according to Aizaki and Nishimura (2008) [55]. To identify interaction effects with demographic and trip-specific factors, subgroup analyses were conducted. For each of the potential interaction factors gender, age, mobility behavior, and trip purpose, two subgroups were created. Gender was divided into the subgroups male and female. Due to the small group size (N = 5), data from participants who indicated a diverse gender were not included in this part of the analysis. For the factor age, a median split was conducted (Mdn = 30 years). The information provided about participants’ mobility behavior was grouped into the frequency of use of individual motorized transportation (e.g., car and motorbike), public transportation, and non-motorized individual transportation (e.g., bike or walking). For each of these modes of transportation, participants were divided into frequent users (usage at least 2 times a week) and non-frequent users (usage less than 2 times a week).

2.5. Sample

The data collection was conducted between July and September 2021. Participants were recruited via social media and the participant pool of the Institute of Transportation Systems of the German Aerospace Center. Furthermore, the link to this study was distributed via certain websites specialized in participant recruitment for empirical studies. All in all, 468 persons completed the questionnaire. Four were excluded because they finished the survey in an unrealistically short time of less than five minutes, which led to concerns regarding the conscientious answering of the questions. Another three were excluded because they indicated they could not gain a sufficient understanding of the shuttle service based on the given description. Thus, a total number of 461 participants were included in the analysis. The sample consisted of 57.7% (n = 266) women, 41.2% (n = 190) men, and 1.1% (n = 5) persons of non-binary gender. The mean age was 34.2 years (SD = 12.46). The majority of the sample (36.9%) lived in a large city with 100,000 to 1,000,000 inhabitants and was rather educated, with 51.6% (n = 238) having a university degree. A detailed description of the sample information can be found in

Table 2. Sample information.

Demographic Variable	Categories	%	N
Gender	Male	57.7	266
	Female	41.2	190
	Non-binary	1.1	5
Age	<21 years	5.2	24
	21–30 years	48.2	222
	31–40 years	19.1	88
	41–50 years	10.8	50
	>50 years	16.7	77
Size of city of residence (number of inhabitants)	<2000	6.3	29
	2000–5000	6.3	29
	5000–20,000	12.4	57
	20,000–100,000	18.7	86
	100,000–1,000,000	36.9	170
	>1,000,000	19.5	90
Highest educational qualification	No educational qualification/still in education	17.8	82
	Secondary school certificate	5.2	24
	High school graduation	17.1	79
	Completed vocational training	8.2	38
	University degree	51.6	238
Frequency of usage of the following:
Individual motorized transport	<2 times a week	53.8	248
	At least 2 times a week	46.2	213
Public transportation	<2 times a week	62.9	290
	At least 2 times a week	37.1	171
Individual non-motorized transport	<2 times a week	30.6	141
	At least 2 times a week	69.4	320

.

3. Results

3.1. Model Specification

For data analysis, the survival package in R was used [56]. The first step was to define the best possible logit model with all relevant main and interaction effects. For this, the Akaike Information Criterion (AIC) was used. As a starting point, the model was defined with the main effects of all levels of the six attributes. The first level of each attribute served as a baseline. Next, a stepwise extension of the model with first-order interaction effects was conducted. For every step, the interaction effect which led to the biggest improvement of the AIC was added to the model. The procedure was stopped when no more improvement in the AIC could be observed with the addition of an interaction effect. The interaction effects that were included in the final model are shown in

Table 3. Interaction effects included in the final model.

Attribute	Interaction with
Price	Gender
Travel time	Gender Frequent use of non-motorized individual transport
Distance to the stop	Frequent use of non-motorized individual transport
Available information	Age Context
Navigation to the stop	Age Frequent use of motorized individual transport
Identification of the stop	Age Frequent use of motorized individual transport Frequent use of public transportation

.

Each of the 456 participants included in this part of the analysis had to answer 18 decision tasks, which led to a total number of 8208 responses. Since each decision task contains three different options (two trip profiles and the option to choose neither of them), 24,624 decisions for or against a specific option were included in the analysis. For the logistic regression, the Maximum-Likelihood-Method was used for parameter estimation. The investigation revealed a high model fit (X² = 7363, p < 0.001).

3.2. Parameter Estimation

Participants chose one of the two trip profiles (Option A or B) in 91.4% (7505) of the cases and none of the available trips in 8.6% (703) of the cases. The results of the logistic regression are shown in

Table 4. Regression parameters of the main effects of the logit model.

Attribute	Attribute Level	β	OR	SE (β)	z	p
Price	EUR 2.50 EUR 3.00 EUR 3.50	- −0.78 −1.06	- 0.46 0.35	- 0.09 0.14	- −8.56 −7.60	- <0.001 *** <0.001 ***
Travel time	10 min 20 min 30 min	- −0.92 −1.59	- 0.40 0.20	- 0.08 0.15	- −10.88 −10.83	- <0.001 *** <0.001 ***
Available information	Departure time Departure time + arrival time Departure time + arrival time + route + delay	- 0.68 1.78	- 1.97 5.91	- 0.10 0.16	- 6.98 11.43	- <0.001 *** <0.001 ***
Distance to the stop	100 m 300 m 500 m	- −0.39 −0.77	- 0.68 0.46	- 0.06 0.07	- −6.58 −10.87	- <0.001 *** <0.001 ***
Navigation to the stop	Address Address + map Address + map + navigation	- 0.23 0.82	- 1.26 2.27	- 0.07 0.09	- 3.31 9.25	- <0.001 *** <0.001 ***
Identification of the stop	No feature Photo Augmented reality	- 0.69 0.93	- 1.69 2.53	- 0.06 0.10	- 10.72 9.58	- <0.001 *** <0.001 ***

*** p < 0.001.

. The main effects need to be interpreted in comparison to a reference value. Here, the lowest level of each attribute was used as the respective reference value.

Regarding the first research objective, for each of the attributes, a strongly significant effect (p < 0.001) for every level could be observed. The odds ratio (OR) as an effect size can be interpreted as an indicator of the change in the probability of taking a ride resulting from a unit change in the attribute. For the attributes price, travel time, and distance to the stop, the odds ratio decreases for every increase in attribute level, whereas for the attributes available information, navigation to the stop, and identification of the stop, the probability of taking a ride increases for every increase in attribute level. When comparing the attributes in the change in odds from the lowest to the highest level, the available information and travel time show the strongest change, with a nearly six times higher probability (OR = 5.91) of taking a trip when the information given is most comprehensive and a five times lower probability (OR = 0.20) of taking a trip with the longest duration.

Additionally, the relative importance of the attributes was assessed, which indicates the proportional contribution of a factor within the trip decision and is calculated by dividing the range of the regression coefficient β of all levels of one attribute by the sum of the ranges of all attributes.

Table 5. Relative importance of each attribute.

Attribute	Range of Regression Coefficients	Relative Importance (%)
Available information	1.78	25.6
Travel time	1.59	22.9
Price	1.06	15.2
Identification of the stop	0.93	13.4
Navigation to the stop	0.82	11.8
Distance to the stop	0.77	11.1

shows the relative importance of each attribute. Consistent with the analysis of the odds ratio, available information with 25.6% and travel time with 22.9% are rated as more important than the other attributes, with importance values ranging between 15.1% and 11.1%.

3.3. Interaction Effects

Research objectives two and three concerned possible interaction effects of the six attributes with person- as well as trip-specific factors. The results of the analysis of the interaction effects that were included in the model are shown in

Table 6. Interaction effects of person- and trip-specific factors with the attributes.

Attribute	Interaction Factor	β	OR	SE (β)	Z	p
Price	Gender	−0.13	0.88	0.05	−2.83	0.005 **
Travel time	Gender Non-motorized individual transport	−0.11 −0.08	0.90 0.93	0.05 0.05	−2.34 −1.59	0.019 * 0.111
Distance to the stop	Motorized individual transport	−0.07	0.93	0.04	−1.66	0.096
Available information	Age Context	−0.18 −0.30	0.83 0.74	0.05 0.04	−3.83 −6.80	<0.001 *** <0.001 ***
Navigation to the stop	Age Motorized individual transport	−0.26 −0.06	0.77 0.94	0.05 0.05	−5.56 −1.35	<0.001 *** 0.176
Identification of the stop	Age Motorized individual transport Public transportation	−0.23 −0.05 −0.10	0.79 0.95 0.91	0.04 0.05 0.05	−5.41 −1.08 −2.12	<0.001 *** 0.281 0.034 *

* p < 0.05, ** p < 0.01, *** p < 0.001.

.

Like the main effects, the interaction effects can only be interpreted in relation to a reference value. In this case, the reference is a female user under 30 years of age who uses the shuttle service for a trip to the station and is a non-frequent user of individual motorized transport as well as non-motorized transport and public transportation. For the factor gender, two significant interaction effects with price (β = −0.13, p = 0.005) and travel time (β = −0.11, p = 0.019) could be observed, which shows that for female persons, these attributes have a stronger influence on the trip decision than for male persons. The factor age revealed a significant interaction with available information (β = −0.18, p < 0.001), navigation to the stop (β = −0.26, p < 0.001), and identification of the stop (β = −0.23, p < 0.001): These attributes are more important in the trip decision for younger people than for older ones. Regarding mobility behavior, a single significant interaction for the frequency of public transport use and identification of the stop (β = −0.10, p = 0.034) could be identified. This indicates that the identification of the stop is more important for people who use public transportation less frequently. Finally, the context of the trip interacted with available information (β =−0.30, p < 0.001), showing that the amount of information is more important in the trip decision for people who are on their way to a station as compared to the situation of going to a park.

Since a significant interaction effect for the factors age, gender, context, and public transportation use could be identified with at least one of the attributes, the relative importance of the attributes separated for the subgroups of the interaction factors was investigated. Figure 5 shows a multi-panel figure of the results. Statistically significant group differences are marked.

4. Discussion

Summary and Interpretation of Results

The objective of this study was to assess the significance of various factors influencing users’ preferences when using a novel mobility concept, the Shared Autonomous Mobility-On-Demand (SAMOD) service. Specifically, this study aimed to compare the importance of factors that are also relevant to conventional public transportation, like the travel time, the price, the availability of information on departure and arrival times, as well as the distance to the stop with the importance of factors that newly emerge due to technological features of SAMOD, such as navigation to the virtual stop or identification of the virtual stop. Additionally, this study examined interaction effects between these factors and various person characteristics, as well as the travel context. The results revealed that all factors considered are relevant for influencing user decisions in SAMOD journeys, even though the conventional factors of travel time, price, and available information were rated as slightly more important than the newly emerging factors of navigation and identification of virtual stops. Several notable interaction effects were observed. Regarding gender, it was shown that for female persons, the price and the travel time have a stronger influence on the trip decision. For the factor age, it was shown that the availability of information, the navigation to the stop, and the identification of the stop are more important for younger persons than for older ones. For the mobility behavior, an interaction effect with frequency of public transport use and the identification of the stop could be identified in that the identification of the stop is more important for persons who use public transport less often. Analysis of the context of the trip revealed that persons who are on their way to the station are influenced more in their trip decisions by the availability of information than persons who are on their way to the park.

5. Conclusions

5.1. Research Objective 1: Main Attributes

Consistent with prior research [20], this study reaffirms the critical importance of the travel time and travel cost in influencing public transportation usage. Previous studies with similar methods have demonstrated the significance of these factors for both traditional public transport services [21] and emerging mobility concepts such as ride-pooling services [22]. The findings of this study further corroborate that these elements serve as foundational pillars for an effective and user-accepted public transportation system and are equally vital for the successful implementation of novel mobility concepts.

A key conclusion of this study is the emphasized significance of SAMOD-specific factors, such as the navigation to and identification of virtual stops. These factors have not yet been examined in prior studies on user requirements for public transportation services, but should receive increased attention in future research, as they are crucial components of innovative, demand-driven public transport services. Although the relative importance of these factors was rated somewhat lower compared to the other factors analyzed, their relevance has to be highlighted. In contrast to the traditional factors which are known to everyone, the novel SAMOD-specific factors were not yet experienced firsthand by the participants but were only verbally described in the hypothetical scenario of this study. When accounting for the potential diminishing effect on the perceived importance of SAMOD-specific factors due to their unfamiliar nature, it can nevertheless be concluded that these factors exert a substantial influence on the decision to undertake a SAMOD journey. Prior research [41] similarly identified the virtual stop as a central component of human–system interaction, with the process of locating and identifying this interaction point playing a crucial role in the user experience of SAMOD travel. The findings of the present study also corroborate those of Hub et al. (2020) [44] and Hub and Oehl (2021) [45], confirming that a well-designed human–machine interface—providing comprehensive information for navigating to virtual stops and employing augmented reality prototypes for stop identification—is highly valued by users and should be a priority in the design of future systems for planning and facilitating SAMOD journeys.

The availability of information—such as departure and arrival times, the route of the forthcoming journey, and potential delays—was identified as the most important criterion for SAMOD travel in this study. This finding is in line with the outcomes of a field trial in which a prototype of an autonomous on-demand shuttle was tested and assessed by users [57]. Although the field trial did not evaluate precisely the same type of information as examined in the present study, participants similarly demonstrated a strong demand for information regarding the service and the forthcoming journey, as well as for real-time information provided during the shuttle ride. This underscores the necessity, particularly with innovative mobility concepts like SAMOD, of providing users with comprehensive pre-journey information to offer a clear overview of the upcoming travel experience. It can be presumed that the provision of relevant information plays a pivotal role in reducing user uncertainty and fostering long-term trust in the service.

The walking distance to the stop was also shown to have an influence on user preferences when choosing a trip with SAMOD even though it was rated as the least important factor. This is consistent with previous results on traditional public transport systems, which showed that there are other factors, like the service frequency, which are more important for users, especially in smaller cities or rural areas [30,58]. The results of the present study regarding the distance to the virtual stop are in line with the previous results, considering that the SAMOD is a concept especially viable for remote areas.

5.2. Research Objective 2: Demographic Variables and Mobility Behavior

The results regarding demographic variables and mobility behavior show that it is essential to consider the individual needs of different user groups. The present study revealed that individuals under the age of 30 exhibit a greater demand for information, both in terms of general details about the upcoming journey and with respect to navigating to the virtual stop. Moreover, younger users place greater importance on advanced technologies, such as augmented reality, for identifying the virtual stop. It can be assumed that younger individuals are more flexible in their travel decisions and therefore prefer a higher level of information. Additionally, minor gender differences were observed in the perceived importance of factors such as price and travel time. Frequent public transport users placed higher importance on a short distance to the stop. Although these differences are relatively modest, they highlight the necessity of a tailored service that takes diverse user preferences into account.

5.3. Research Objective 3: Context of the Trip

Finally, the context of the trip was found to have an influence on the amount of information required for the journey. Participants who were on their route to a train station in the scenario described demonstrated a markedly higher need for information compared to those heading to the park. This suggests that in situations with potential time pressure, users place greater value on increased situational awareness, which is facilitated by the availability of relevant information.

5.4. Recommendations for Transport Companies and City Planners

Several recommendations for transport companies and city planners for implementing a novel mobility concept like the SAMOD in a user-friendly way can be derived from the presented results. First and foremost, the fundamental requirements of public transportation concerning travel time and affordability of the service to users must be fulfilled. For the SAMOD concept, travel time is critically dependent on the optimization of algorithms to ensure efficient pooling of passengers with similar routes. Even though the pooling procedure enhances the service efficiency from an operator perspective, it must not create excessively prolonged travel times for passengers due to detours but should instead result in an optimized compromise between effective pooling of passengers and short travel times. Additionally, for such a service to remain economically viable, operational costs must be controlled to make it possible to offer affordable fares. Failure to do so could place SAMOD at a competitive disadvantage relative to the wide availability of low-cost conventional public transportation options.

A further prerequisite for the successful implementation of such a service is that the accompanying user app for SAMOD travel meets advanced technical standards. Furthermore, the digital travel guide should start as soon as the passenger departs from their residence, offering seamless navigation to the virtual stop. Essential information, including the address of the virtual stop, a map, and navigation assistance, should be provided. Moreover, beyond the detailed route description, the precise identification of the virtual stop should be facilitated by a suitable information presentation form like a visualization via augmented reality.

Finally, the app should offer customizable settings to accommodate individual preferences. This includes options for providing context-specific information about upcoming journeys, as well as preferences related to the travel time, cost, and specifications about general mobility behavior. Furthermore, it should be possible to provide information on potential mobility impairments, which could imply that the distance to the virtual stop needs to be short or that vehicles with certain forms of assistance are required. As demonstrated by König et al. (2021) [25], information about other potential passengers is also of significance. The authors suggest that details such as the passenger’s name, a photo, and a rating from previous journeys can enhance user acceptance of shared mobility services and should therefore also be implemented in applications for SAMOD services.

5.5. Limitations and Further Research Needs

One limitation of the present study is the representativeness of the sample. Over half of the participants resided in large cities with populations exceeding 100,000 inhabitants. Additionally, the average age of the sample was 34 years, which is 10 years younger than the national average age in Germany of 44 years. To facilitate comparisons across age groups with similar group sizes, a comparatively low age of 30 years was applied for a median split. As younger individuals showed a greater need for information and placed greater importance on navigating to the virtual stop and identifying the virtual stop, it can be assumed that these factors would likely have been rated as slightly less important in a more demographically balanced sample. Future research should examine whether the findings of this study also apply to people living in rural areas and older individuals. Another important consideration that was not the focus in the present study is the inclusion of perspectives from individuals with physical disabilities. A fundamental aspect of a functioning public transportation system is its accessibility to all. Therefore, in the implementation of new mobility concepts, it is essential to ensure that no groups of people are systematically excluded. Future studies on autonomous, demand-responsive mobility services should prioritize the needs of individuals with disabilities to ensure the development of sustainable and inclusive public transportation in the long term. Some practical challenges associated with the use of augmented reality (AR) for navigation to and identification of virtual stops should be considered. Environmental factors influencing visibility, such as fog or inadequate lighting, may impair the effectiveness of AR displays, thereby complicating the accurate identification of virtual stops. Additionally, individuals with limited technological proficiency may experience difficulty or confusion when interacting with AR-based systems, potentially undermining their overall user experience.

A limitation of stated preference (SP) studies, in contrast to real-world studies, is the reduced external validity. Hypothetical bias is frequently observed, as respondents may indicate preferences that do not accurately reflect their actual behavior. This discrepancy often arises when the complexity of real-world scenarios is underestimated or when individuals provide socially desirable responses rather than expressing their true intentions. Future research could explore whether the findings related to the attributes examined in this study are corroborated by evidence from real-world tests. The validity of choice-based conjoint analyses is also highly dependent on the selection of the attributes and attribute levels and the accurate depiction of the chosen scenario. This study concentrated on distinctive features of the SAMOD concept and particularly the user interaction with the SAMOD service via virtual stops. The additional interacting factors analyzed were chosen based on an extensive literature review and insights gathered from a qualitative workshop preceding this study. It is conceivable to explore further factors related to the travel experience within a SAMOD context. Following up on the work of König et al. (2021) [25], future research could focus on the shared mobility aspect by examining specific characteristics of co-passengers or the predicted occupancy of the vehicle to assess their potential impact on user acceptance of the service. Additionally, a more detailed analysis of vehicle attributes, such as cleanliness or interior design, could be conducted.

Future research could also place a greater emphasis on the concept of the virtual stop. While this study explored various methods for navigating to and identifying the virtual stop, future investigations could delve deeper into specific characteristics of the virtual stop. Armellini et al. (2021) [40] already simulated possible locations of virtual stops in an urban area concerning aspects of accessibility, safety, proper lighting, seating availability, and roofing. Future studies could assess the importance of these virtual stop characteristics from a user perspective. The aspect of the identification of the virtual stop via a smartphone application could also be examined in further detail. Following up on Hub and Oehl (2022) [46], who already evaluated an augmented reality (AR) prototype of a virtual stop, future studies could test different interface design aspects like the optimal amount of AR information presented depending on the characteristics of the specific location.