Ride-pooling demand prediction: A spatiotemporal assessment in Germany

Felix Zwick and Kay W. Axhausen

Journal of Transport Geography, 2022, vol. 100, issue C

Abstract: Ride-pooling has attracted considerable attention from both academia and practitioners in recent years, promising to reduce traffic volumes and its negative impacts in urban areas. Simulation studies have shown that large-scale ride-pooling has the potential to increase vehicle utilization, thereby reducing vehicle kilometers traveled (VKT) and required fleet sizes compared to single-passenger mobility options. However, in the real world, large-scale ride-pooling services are rare and not yet widely implemented, in part due to high operating costs that are expected to decrease substantially with the advent of automated vehicles.

Keywords: Ride-sharing; On-demand mobility; Spatial regression; Open data; New mobility; Demand prediction; Ride-splitting (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:100:y:2022:i:c:s0966692322000308

DOI: 10.1016/j.jtrangeo.2022.103307

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