How Did You Like This Ride? An Analysis of User Preferences in Ridesharing Assignments

Schleibaum, Sören; Greve, Maike; Lembcke, Tim-Benjamin; Azaria, Amos; Fiošina, Jeļena; Hazon, Noam; Kolbe, Lutz M.; Kraus, Sarit; Müller, Jörg P.; Vollrath, Mark

doi:10.5220/0009324401570168

Cited by 2 publications

(1 citation statement)

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“…It includes vulnerability and safety, reliability and dependability (which are well-researched concepts in Software Engineering), as well as the role of formal verification and validation. In addition, relevant topics are trust in sensors and actuators, transparency, interpretability, and explainability of AI systems and algorithms, but also notions of value alignment [3] between humans and AI systems, touching upon issues of preference elicitation, and on optimality, utility, and fairness of decisions made by algorithms [5].…”

Section: Introductionmentioning

confidence: 99%

Engineering Human–Machine Teams for Trusted Collaboration

Alhaji

Beecken

Ehlers

et al. 2020

BDCC

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The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

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Section: Introductionmentioning

confidence: 99%

Engineering Human–Machine Teams for Trusted Collaboration

Alhaji

Beecken

Ehlers

et al. 2020

BDCC

View full text Add to dashboard Cite

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Exploring Sustainable Urban Transportation: Insights from Shared Mobility Services and Their Environmental Impact

Garus,

Mourtzouchou,

Suarez

et al. 2024

Smart Cities

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The transportation landscape is witnessing profound changes due to technological advancements, necessitating proactive policy responses to harness innovation and avert urban mobility disruption. The sharing economy has already transformed ridesharing, bicycle-sharing, and electric scooters, with shared autonomous vehicles (SAVs) poised to reshape car ownership. This study pursues two objectives: firstly, to establish a market segmentation for shared ride services and secondly, to evaluate the environmental impact of ridesharing in different contexts. To mitigate potential biases linked to stated preference data, we analysed the navette service, utilized by a research institute in Europe, closely resembling future SAVs. The market segmentation relied on hierarchical cluster analysis using employee survey responses, while the environmental analysis was grounded in the 2019 navette service data. Our analysis revealed four unique employee clusters: Cluster 1, emphasizing active transportation and environmental awareness; Cluster 2, showing openness towards SAVs given reliable alternatives are available; Cluster 3, the largest segment, highlighting a demand for policy support and superior service quality; and Cluster 4, which places a premium on time, suggesting a potential need for strategies to make the service more efficient and, consequently, discourage private car use. These findings highlight a general willingness to adopt shared transport modes, signalling a promising transition to shared vehicle ownership with significant environmental benefits achievable through service design and policy measures.

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How Did You Like This Ride? An Analysis of User Preferences in Ridesharing Assignments

Cited by 2 publications

References 0 publications

Engineering Human–Machine Teams for Trusted Collaboration

Engineering Human–Machine Teams for Trusted Collaboration

Exploring Sustainable Urban Transportation: Insights from Shared Mobility Services and Their Environmental Impact

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