2019
DOI: 10.3390/smartcities2020015
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Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan

Abstract: Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined a… Show more

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Cited by 11 publications
(8 citation statements)
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“…PAM starts by nding a medoid, which should be the most centrally located observation in the cluster. Following the determination of the initial set of medoids, one medoid is iteratively replaced by one non-medoid whenever the total distance of the resulting clustering is improved [22]. PAM was the preferable method over CLARA considering the study's sample size and computing time.…”
Section: Cluster Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…PAM starts by nding a medoid, which should be the most centrally located observation in the cluster. Following the determination of the initial set of medoids, one medoid is iteratively replaced by one non-medoid whenever the total distance of the resulting clustering is improved [22]. PAM was the preferable method over CLARA considering the study's sample size and computing time.…”
Section: Cluster Analysismentioning
confidence: 99%
“…The most popular partitioning clustering algorithms is the k-means, but other extensions have been implemented, namely k-modes, and Partitioning Around Medoids (PAM) or CLARA (Clustering Large Applications) algorithms, the latter being an extension of PAM to handle large sample sizes using a sampling approach. [22]. Since our dataset is only composed of categorical variables, we rst tested the k-modes algorithm, which has been widely used to e ciently cluster categorical data [23].…”
Section: Cluster Analysismentioning
confidence: 99%
“…For example, Moreno et al (2018) observe that pick-up waiting time for SAV journeys are important if users are going to a doctor appointment or running errands. This does not mean that pick-up waiting time is not also important in routine commuting, but it suggests that sharing may be less feasible for some noncommuting journeys due to their irregularity, depending on how punctual and rapid ondemand SAVs become (Hao et al, 2019). Nazari et al (2018) also observe that interest in using SAVs is less for non-commuters than commuters and suggest this may be because productive use of in-vehicle time is less urgent outside of a regular commute.…”
Section: Sharing Of Avs Is Less Likely For Non-commuting Journeysmentioning
confidence: 99%
“…Table 1). One such feature can be storage requirements, as also pointed out by Hao, Li, and Yamamoto (2019). It has long been acknowledged that car purchasing behaviours are based on hypothetical and rare usessuch as a potential desire to go camping.…”
Section: Leisure Surpasses Commuting In Av Public Interestmentioning
confidence: 99%
“…The use of gas [11] or biofuels [12] in the form of low-emission drives has developed, too. Numerous studies concern autonomous vehicles [13][14][15][16][17][18][19][20]. All of the above innovations lead to the development of new vehicles (possibly engines), but the industry is good at efficient implementations.…”
Section: Introductionmentioning
confidence: 99%