Experience and Acceptance of Autonomous Shuttles in the Brussels Capital Region

Feys, Manon; Rombaut, Evy; Vanhaverbeke, Lieselot

doi:10.3390/su12208403

Cited by 34 publications

(27 citation statements)

References 32 publications

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“…Beginning in 2040, AVs will become more affordable; by 2045, it is predicted that AVs will account for 50% of all new vehicle sales [17]. Regarding public acceptance of AVs, studies (e.g., [18][19][20]) that focused on user attitudes after pilot project experiences revealed that there are positive intentions towards the technology, despite the fact that lower operation speed, inaccessibility to mobility-impaired individuals, and interaction of AVs with pedestrians and cyclists remains the concerns for their large-scale deployment.…”

Section: Introductionmentioning

confidence: 99%

How PTV Vissim Has Been Calibrated for the Simulation of Automated Vehicles in Literature?

Beza

Zefreh

Török

et al. 2022

Advances in Civil Engineering

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Recently, in the literature, microscopic simulation is one of the most attractive methods in impact assessment of automated vehicles (AVs) on traffic flow. AVs can be divided into different categories, each having different driving characteristics. Hence, calibrating microscopic simulators for different AV categories could be challenging in AVs’ impact assessment. The PTV Vissim microscopic traffic simulation software has been calibrated for simulating diverse types of AVs in a large body of literature. There are two main streams of studies in literature adapting AVs' driving behaviors in Vissim following either internal (i.e., adjusting the parameters of the Vissim's default driving behavior models) or external (i.e., adapting AVs' behavior through external VISSIM interfaces) modeling approaches. The current paper investigates how the PTV Vissim has been internally calibrated for the simulation of different types of AVs and compares the calibrated values in the literature with default values introduced in the recent version of PTV Vissim. In the present paper, the reviewed studies are partitioned into two main categories according to the characteristics of the studied AVs, the studies focused on autonomous automated vehicles (AAVs) and the ones focused on cooperative automated vehicles (CAVs). Our findings indicate that the literature expects a lower value for parameters including standstill distance (CC0), headway time (CC1), following variation (CC2), the threshold for entering “following” (CC3), negative/positive following thresholds (CC4/CC5), speed dependency of oscillation (CC6), oscillation acceleration (CC7), safety distance reduction factor (SDRF), and minimum headway front/rear (MinHW) for AVs than conventional vehicles (CVs). Besides, the literature expects higher values for parameters including standstill acceleration (CC8), acceleration at 80 km/h (CC9), looking distances, and maximum deceleration for cooperative braking (MaxDCB) for AVs. When cautious AVs are introduced, deterring effects are expected in the literature (e.g., higher CC0). Moreover, CAVs can have higher looking distance values compared with AAVs.

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Section: Introductionmentioning

confidence: 99%

How PTV Vissim Has Been Calibrated for the Simulation of Automated Vehicles in Literature?

Beza

Zefreh

Török

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

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“…Interestingly, the absence of staff was not an issue for frequent users of autonomous shuttle services in Trikala, Greece (Portouli et al, 2017). Vehicle operational safety and technical issues (e.g., traffic crashes caused by technical errors, confusion when an unprecedented situation occurs) were the other top concerns voiced by the respondents (Chng and Cheah, 2020;Feys et al, 2020;Dong et al, 2019;Lopez-lambas and Alonso, 2019;Roche-Cerasi, 2019;Bansal et al, 2016). However, in some studies, respondents agreed that there was less risk of a traffic crash for autonomous transit than for conventional transit (Portouli et al, 2017;Salonen, 2018).…”

Section: Public Perceptionmentioning

confidence: 99%

“…However, in some studies, respondents agreed that there was less risk of a traffic crash for autonomous transit than for conventional transit (Portouli et al, 2017;Salonen, 2018). Among others, respondents expressed concerns about potential hazards ensuing from AVs sharing the roadway with pedestrians (Battistini et al, 2020) and conventional vehicles (Bansal et al, 2016;Battistini et al, 2020), particularly in complex urban situations (Feys et al, 2020), legal liabilities in case of a traffic crash (Chng and Cheah, 2020), increased investment costs for vehicles and infrastructural improvement (Lopez-Lambas and Alonso, 2019), potential reduction in funding for traditional transit (Battistini et al, 2020), job loss due to automation (Tussyadiah et al, 2017), and affordability of the service (Bansal et al, 2016).…”

Section: Public Perceptionmentioning

confidence: 99%

“…Linear regression (LR) models are used to test the relationship between behavioral intention to use with different exogenous factors including behavioral constructs (Liu et al, 2020;Wong and Rinderer, 2020;Tussyadiah et al, 2017;Chng and Cheah, 2020;Feys et al, 2020;Madigan et al, 2017;Motak et al, 2017;Madigan et al, 2016;Zoellick et al, 2019). Other decision variables investigated using LR include perceived concerns (Wong and Rinderer, 2020), and willingness to accept (Konig and Grippenkoven, 2020).…”

Section: Econometric Modeling Frameworkmentioning

confidence: 99%

“…Valence or pleasantness of service or perceived enjoyment (hedonic motivation) was found to be a strong predictor of SAMS acceptance and usage (Feys et al, 2020;Nordhoff et al, 2020a;Yuen et al, 2020;Madigan et al, 2017). It has also a moderating effect on both attitude and intention to use (Chen,2019;Herrenkind et al, 2019).…”

Section: Attitudinal Factorsmentioning

confidence: 99%

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Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

Bala¹

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The emergence of vehicle automation and its subsequent growth has led to new transport service offerings, generally known as Autonomous Mobility Services (AMS), that have the potential to facilitate or even replace human-operated vehicles. AMS contains different forms of potential mobility options which may contradict current transport modal concepts in terms of functionalities. For example, an autonomous shuttle bus which is a form of autonomous transit may serve similarly as an autonomous taxi/robo-taxi in terms of functionalities, coinciding with the concept of Shared Autonomous Mobility Services (SAMS). Even if the functionalities or operational principles are different, peoples' perceptions of sharing rides in any of these services may be alike. Apart from these confusions in functionalities mentioned above, peoples' attitudes and acceptance of AMS, once it's implemented in any form in the public road environment, remains a significant research aspect. To address these issues, this thesis tried to first clearly distinguish different types of AMS. Second, it tried to assemble the progress till now in acceptance-related research of AMS while reviewing the previous study approaches, outcomes, policy implications, and future research directions. Third, this study attempted to understand the acceptance of AMS using statistical and deep learning approaches leveraging both survey and social media data. Fourth, this study tried to present the consequent applicabilities and limitations of using both types of data sources for autonomous vehicle acceptance research. Eventually, this thesis intends to present an overall idea of the AMS acceptance research with future directions leveraging both data sources in an individualistic or combined manner.

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