Latest revision as of 19:43, 3 February 2021

Abstract

Unlike traditional bus fleets, autonomous mobility services are naturally amenable to dynamic, demand-responsive adaptation of itinerary. Accurate prediction of demand for such services can thus improve their utilization and decrease their operational costs. Although demand for transit services is inherently stochastic, models of demand often reduce its distribution to point estimates, thus losing useful information for subsequent decision making. In this paper, we advocate for preserving the full predictive distribution through quantile regression, so that the structure of uncertainty in future demand is preserved. To demonstrate our approach, we present a real-world case study of an autonomous shuttle service in a Danish university campus, for which we have several weeks of crowd movement counts, as reconstructed from campus WiFi records. We devise several types of quantile regression models for demand prediction, analyze their performance, and discuss their applicability to the case study. Our modeling methodology can be extended to autonomous fleets of higher scale, thus promoting sustainable shared mobility.

Original document

The different versions of the original document can be found in:

• http://arxiv.org/pdf/1902.09745

• http://xplorestaging.ieee.org/ielx7/8907344/8916833/08916878.pdf?arnumber=8916878,

http://dx.doi.org/10.1109/itsc.2019.8916878

• https://dblp.uni-trier.de/db/conf/itsc/itsc2019.html#PeledLJDP19,

https://export.arxiv.org/pdf/1902.09745,

https://arxiv.org/abs/1902.09745,

https://ui.adsabs.harvard.edu/abs/2019arXiv190209745P/abstract,

https://arxiv.org/pdf/1902.09745,

https://orbit.dtu.dk/en/publications/preserving-uncertainty-in-demand-prediction-for-autonomous-mobili,

https://export.arxiv.org/abs/1902.09745,

https://staging.orbit.dtic.dk/en/publications/preserving-uncertainty-in-demand-prediction-for-autonomous-mobili,

https://academic.microsoft.com/#/detail/2990458016