Problem
Research questions and friction points this paper is trying to address.
autonomous vehicles
sharing economy
on-demand mobility
crowdsourcing
spatiotemporal heterogeneity
Sharing economy in the era of full automation: Evidence from autonomous vehicle on-demand mobility services
🤖 AI Summary
This study addresses the integration of privately owned autonomous vehicles (AVs) into mobility-on-demand (MoD) systems during non-personal-use periods to reduce operational costs and enhance service efficiency. It introduces an “AV crowdsourcing” paradigm that conceptualizes private AVs as shareable robotic assets and formulates a time-expanded network flow model to capture the spatiotemporal heterogeneity and complementarity between vehicle owners and passengers. A centralized scheduling algorithm is employed to elucidate how key factors—such as reserved idle time and dispatch distance—affect system performance. Case studies based on Chicago demonstrate that this approach substantially lowers operational costs while simultaneously maintaining high service quality in both high-demand downtown areas and peripheral zones, highlighting the synergistic impact of supply heterogeneity and market conditions on overall system efficacy.
📝 Abstract
The digital age has facilitated the sharing of underutilized assets. This paper focuses on privately owned autonomous vehicles (AVs), a unique class of robots that can move independently and provide transportation services. When not in personal use, private AV owners can lease their vehicles to a platform that operates an on-demand mobility service (MoD). We refer to this service as AV crowdsourcing, and develop a time-expanded network flow model that captures temporal and spatial heterogeneity in AV usage of both owners and passengers while preserving analytical tractability. We analyze the conditions under which AV crowdsourcing reduces MoD operating costs and identify their key factors, namely, the complementarity of the mobility pattern between AV owners and MoD passengers, the slack time reserved by vehicle owners, and the vehicle repositioning distance. A case study of Chicago further reveals substantial spatiotemporal heterogeneity in optimal prices and service quality. The results demonstrate how centralized dispatching can simultaneously fulfill the high demand in downtown areas while maintaining relatively high service quality in peripheral regions. Our findings provide insights into how supply heterogeneity and market conditions jointly shape the performance of AV crowdsourcing systems that leverage the underutilized private robotic assets.
Innovation
Methods, ideas, or system contributions that make the work stand out.
autonomous vehicle crowdsourcing
time-expanded network flow
spatiotemporal heterogeneity
on-demand mobility
shared robotic assets
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