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Schematic representation of the research questions. Dependent variable is represented as a rectangle, predictors and mediators as ellipses.
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Empirical studies show that autonomous vehicles can contribute to sustainability goals when rides are shared. However, sharing rides with strangers in shared autonomous mobility-on-demand systems (SAMODSs) might impede the adoption of these systems. The present study addresses the research question whether a comprehensive information provision abou...
Contexts in source publication
Context 1
... RQ4: Does the length of the trip influence the relevance of provided information about fellow passengers for travellers' willingness to share the trip? Figure 1 provides a schematic overview over the research questions and the expected relationships between the variables. The study at hand thereby focuses on a specific potential user group-Generation Y, which comprises persons born between 1981 and 1999, also called millennials [44]. ...
Context 2
... RQ4: Does the length of the trip influence the relevance of provided information about fellow passengers for travellers' willingness to share the trip? Figure 1 provides a schematic overview over the research questions and the expected relationships between the variables. The study at hand thereby focuses on a specific potential user group-Generation Y, which comprises persons born between 1981 and 1999, also called millennials [44]. ...
Context 3
... of information had five levels: (A) bus stop sign, (B) name, (C) picture, (D) rating and E) full profile information as a combination of the levels name, picture and rating ( Figure 2). Further independent variables were (1) gender: information on the gender of the further traveller, (2) travel time: the length of the ride with two levels: 14 min vs. 25 min and (3) automation: the level of vehicle automation: with driver vs. driverless (Figure 1). The profile pictures of the female and male fellow travellers were used from the CHICAGO FACE DATABASE with a medium rated attractiveness [50]. ...
Context 4
... of information had five levels: (A) bus stop sign, (B) name, (C) picture, (D) rating and E) full profile information as a combination of the levels name, picture and rating ( Figure 2). Further independent variables were (1) gender: information on the gender of the further traveller, (2) travel time: the length of the ride with two levels: 14 min vs. 25 min and (3) automation: the level of vehicle automation: with driver vs. driverless (Figure 1). The profile pictures of the female and male fellow travellers were used from the CHICAGO FACE DATABASE with a medium rated attractiveness [50]. ...
Context 5
... the presentation of full profile information, a significant interaction with own gender was shown for the 25-min ride (F(1,1424) = 5.306, p = 0.006) but not for the shorter ride of 14 min (F(1,150) = 0.848, p = 0.430). As shown in Figure 6 (top right), for the longer ride, women showed higher WTA values when presented to a male full profile (M = 72.98, ...
Context 6
... the presentation of full profile information, a significant interaction with own gender was shown for the 25-min ride (F(1,1424) = 5.306, p = 0.006) but not for the shorter ride of 14 min (F(1,150) = 0.848, p = 0.430). As shown in Figure 6 (top right), for the longer ride, women showed higher WTA values when presented to a male full profile (M = 72.98, ...
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While it is widely agreed that automated and autonomous vehicles may provide safety benefits over vehicles with lower level or no automation, due to other road users there will still likely be situations where a collision is unavoidable. What should a vehicle that is operating autonomously do when it has no choice but to have a collision? And who s...
Citations
... Academic and industry perspectives alike increasingly argue that sharing AVs will increase the adoption of carsharing services, which in turn can lead to the anticipated beneficial effects of automation for mobility like a decrease in the number of vehicles on the roads, minimised total travel distances of vehicles, and reduced carbon emission (Hanna et al. 2016;Lokhandwala and Cai 2018). Research spans from investigating the impact of users' willingness to share on shared autonomous mobility (Dolins et al. 2021) to more concrete factors, such as how different amounts of information about fellow passengers influence acceptance of shared AVs (König, Wirth, and Grippenkoven 2021). Other examples concern how aspects like vehicle speed and the direction of the passenger's face impact comfort and trust while sharing AVs with strangers (Paddeu, Parkhurst, and Shergold. ...
This paper investigates elements of trust in autonomous vehicles (AVs). We contextualise autonomous vehicles as part of people's everyday settings to extend previous understandings of trust and explore trust in autonomous vehicles in concrete social contexts. We conducted online co-creation workshops with 22 participants, using design probes to explore trust and AVs in relation to people's everyday lives. Using a socio-technical perspective, we show how trust and acceptance depend not only on the underlying AV technology but also – if not more so – on human-to-human relationships and real-life social circumstances. We argue that when investigating issues of trust and automation, the scope of analysis needs to be broadened to include a more complex socio-technical set of (human and non-human) agents, to extend from momentary human-computer interactions to a wider timescale, and be situated in concrete spaces, social networks, and situations.
... Since there is no human authority (e.g., a bus driver) present anymore in AVs, users' acceptance is likely to be influenced by the presence of co-passengers [10,11]. Information about fellow travelers prior to and during the ride seems to have the potential to affect user acceptance positively [12]. The question arises whether these findings of are further influenced by gender and time of the day as, e.g., [11] found that especially women are feeling anxious to share rides at night times. ...
... Previous research suggests that combining these data reduces overall compensation demands for sharing a ride with a stranger [12]. We did not include a rating of fellow passengers, as rating systems hold discriminating characteristics [13,57]. ...
... Women and men alike explained in the interviews that they prefer sharing rides with women. This is in line with the findings of [12], who found people have higher refusal rates towards men as co-passengers. On the other hand, Polydoropoulou et. ...
Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence - especially at night - will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. As related work points out that information about fellow travelers might mitigate passengers' concerns, we designed two user interface variants to investigate the role of this information in an exploratory within-subjects user study (N = 24). Participants experienced four automated day and night rides with varying personal information about co-passengers in a simulated environment. The results of the mixed-method study indicate that having information about other passengers (e.g., photo, gender, and name) positively affects user experience at night. In contrast, it is less necessary during the day. Considering participants' simultaneously raised privacy demands poses a substantial challenge for resilient system design.
... The "attractability" is emphasized by safety (i.e., at night, with an operator) (Lenz et al. 2019). The presence of "fellow travelers" (from the opposite sex) could also positively influence the acceptability of SAVs (König et al. 2021). However, other research (Polydoropoulou et al. 2021) present a more nuanced picture with and without a preference among fellow travelers based on their sex, depending on the European country. ...
The increasing world population in urban areas and intensive use of private vehicles have several negative impacts, such as congestion, air pollution, and traffic crashes, affecting public health and well-being, especially for vulnerable groups (e.g., children, elderly, women), further increasing social inequality. Current sustainable transportation options like active and public transportation are not always alternatives for these groups. One of the most promising sustainable transportation options is shared autonomous transportation. However, it is rather unclear if vulnerable groups accept shared autonomous vehicles or SAVs, questioning their social sustainability. Based on the definitions for “social acceptability” and “social acceptance” of transportation options, the Transport Acceptancy-Vulnerability or TAV model is presented. The model combines the “acceptancy,” i.e., “acceptability,” “acceptance,” with the “vulnerability” referring to the four conditions to be met or the 4As, i.e., “availability,” “accessibility,” “affordability,” “attractability,” toward transportation options. This model can be used to address the “social acceptability” or “social acceptance” of sustainable transportation options by vulnerable groups. It allows the structuring and evaluation of literature as well as data helping to determine whether the factors of “acceptancy” or the conditions of “vulnerability” for a transportation option need to be improved. The usability is illustrated by examining the scientific literature on the acceptability of SAVs by elderly, women, households with children, and people with disabilities, and by an example of the social acceptability of SAVs by different potentially vulnerable groups. The model can help transportation authorities, operators, and practitioners to improve socially sustainable urban transportation and overall social inclusion.
... They are far more inclined to interact and meet new people than older people, they are a lot more aware of climate change, and their economies are not overly strong (Arnauda, 2022). Their daily mobility needs, which are Going to university or commuting to work (König et al., 2021), are another reason that makes young people a potential target group. Because they understand that carpooling is one of the least expensive and most comfortable forms of transportation, young people are particularly drawn to it (Xiao et al., 2021). ...
Carpooling is emerging as a more appealing “sharing economy” form with promising benefits in reducing carbon emissions, traveling costs, and traffic congestion. However, a thorough understanding of carpooling adoption is lacking for policymakers and transport planners in developing countries due to limited scientific research, specifically in Southeast Asia. Therefore, the present study aimed to understand the behavioral influences of carpool adoption in Thailand by conducting a multivariate analysis on a dataset of 307 observations gathered at Thammasat University, Pathum Thani, Thailand. First, a conceptual model was developed to assess the influence of effort expectancy, perceived safety, hedonic motivation, and social influence on carpool behavior intention. Additionally, two constructs related to COVID-19 and time credits were added to assess their impacts. Then, the sample data were analyzed using Structural Equation Modelling (SEM). It was found that hedonic motivation, social influence, and time credits as payment method factors play statistically significant direct roles in the carpool behavior intention, whereas effort expectancy, perceived safety, and perception towards compliance with COVID-19 guidelines for carpooling did not. However, significant indirect impacts of effort expectancy and social influence through hedonic motivation were discovered. Upon analysis of the findings, policy implications are presented.
... Poveda- Reyes et al. (2021) share their insights on the acceptability of bike-sharing and SAVs by women from Europe at large, while Lenz et al. (2019), Stark and Galich (2020), and König et al. (2021) collected data in Germany and Polydoropoulou et al. (2021) in Cyprus, Greece, Hungary, Israel, Iceland, Finland, and the UK on SAVs. Poveda-Reyes et al. (2021) determined the needs and barriers of women in general and different female profiles (based on residential location, income, travel companion dependency, and transport discrimination) regarding the acceptability of bike-sharing and SAVs. ...
... However, Lenz et al. (2019) discovered that women in Germany expect to feel quite safe at night in an SAV, preferably with a driver, certainly compared to walking home or waiting for the next bus alone. According to a study of König et al. (2021), young (18-39 years old) women in Germany are even more willing to share an AV for long trips with male instead of female fellow travellers if men provide more information about themselves, such as a picture and rating. This could indicate that there is no difference between men and women, which is confirmed by Stark and Galich (2020). ...
It is believed that shared transport services (STSs) can reduce transport poverty and social exclusion. This paper proposes a definition of “social acceptability” and “social acceptance” and examines whether vulnerable groups accept STSs. The notions “acceptability” and “acceptance” were distinguished and four necessary conditions, especially for vulnerable groups, or the 4As were identified: “availability”, “accessibility”, “affordability”, and “attractability”. In the context of STSs, “social acceptability” is defined as the degree to which an individual intends to use a STS before experiencing it in everyday travel based on the expected availability, accessibility, affordability, and attractability of the service, while “social acceptance” also incorporates the use of a STS after experiencing it in everyday travel based on a minimum level of perceived availability, accessibility, affordability, and attractability. This paper further reviews the scientific literature in transport research regarding the “acceptability” or “acceptance” of STSs by vulnerable groups. While several studies include socio-economic and demographic variables (e.g. age, gender) to explain the “acceptability” of STSs, only a few studies specifically focus on vulnerable groups. More research on the “social acceptance” of STSs, especially shared scooters, ride-sharing, and apps and Mobility as a Service (MaaS), by vulnerable groups is needed.
... Generation Y's Information Needs Concerning Sharing Rides in Autonomous Mobility on Demand Systems [11] Conclusion security concerns [3,4] acceptability [1,3] information needs [5] monetary incentives [4,6] detours & inreliability [5,7] trust [8] 1) Is the willingness to use SAMODS affected by travel time changes and detours due to dynamic routing? 2) To what extent could a pricing system be used to incentivize sharing rides? 3) Which personal information about fellow travellers is suitable to increase the acceptability of sharing rides? ...
The penetration of autonomous vehicles of level 5 will be a turning point for mobility. Depending on the scenario traffic will either dramatically rise or significantly decrease. Raising the occupancy rate of autonomous mobility-on-demand-systems by ridesharing is an essential prerequisite for future sustainable mobility. Yet, sharing rides with strangers in driverless vehicles might cause acceptance problems concerning privacy and safety issues, especially due to flexible routing. In how far do travel time changes and a detour due to fellow travellers affect the willingness to use shared autonomous mobility-on-demand-systems? Could a pricing system be used to incentivize sharing rides? Which information about fellow travellers is suitable to increase the acceptability of sharing rides? The contribution summarizes the findings of two stated choice experiments (N = 150 and N = 154). The results highlight the importance of a pricing system that is adjustable to travel time and detour. The willingness to share rides increases when more information about fellow passengers is presented. The contribution adds to the limited empirical findings by a first explorative step to understand human choice behaviour in these complex and dynamic systems and provides concrete recommendations for the user-centred design.
Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence – especially at night – will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. Prior research points out that having information about fellow travelers could alleviate the concerns of passengers and we designed two user interface variants to investigate the role of this information in an exploratory withinsubjects user study (𝑁 = 24). Participants experienced four automated day and night rides with varying personal information about co-passengers in a simulated environment. The results of the mixed-method study indicate that having information about other passengers (e.g., photo, gender, and name) positively affects user experience at night. In contrast, it is less necessary during the day. Considering participants’ simultaneously raised privacy concerns, balancing security and privacy demands poses a substantial challenge for resilient system design.
This article gives a scientometric analysis of research advances in mobility-on-demand (MoD)systems. By reviewing 191 articles with diverse perspectives from the past 10 years, a scientometric study is conducted using CiteSpace, giving an integrated knowledge mind mapping of MoD systems. The results indicate that the major contributions in MoD systems field are mainly from the United States, Singapore, China, and Germany. Meanwhile, pivotal institutions, journals, and article keywords are discussed. MoD system knowledge domains are concentrated in the categories of demand and service, system optimization, impacts and factors, and response and choice. The development of MoD systems is evolving from taxis and minibuses to autonomous vehicles. In particular, research interest in autonomous MoD systems is rapidly growing. This analysis gives a comprehensive and integral knowledge mind mapping, which contributes to the MoD system information network and provides valuable insights into future research directions and developments for investigators and software developers in the field of vehicle-sharing and traffic scheduling.
