Ride-pooling demand prediction: A spatiotemporal assessment in Germany

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.Two large-scale ride-pooling fleets are operated by MOIA in Hamburg and Hanover, Germany, and serve as testbeds for future pooled services. For this study, the authors analyze pre-pandemic demand data from both services from 2019 and 2020 and perform spatial and random forest regressions to understand the (spatial) characteristics of ride-pooling trip origins in both cities. They then examine how well findings from one study area (Hamburg in this case) can be generalized and transferred to other cities (Hanover in this case) to enable spatial predictions beyond areas with an existing service.The regression results are similar in both cities and show the strongest impact on ridepooling demand by the variables capturing the density of workplaces, gastronomy and culture. Given the regression results for Hamburg, they predict trip origins for Hanover and observe that demand is overestimated by all applied models. The spatial lag of X (SLX) model showed the most promising results with an overall overestimation of trip origins below 20%.

• Record URL:
  https://doi.org/10.1016/j.jtrangeo.2022.103307
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0966692322000308
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
• Supplemental Notes:
  • © 2022 Felix Zwick and Kay W. Axhausen. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zwick, Felix
  • Axhausen, Kay W
• Publication Date: 2022-4

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 103307
• Serial:
  • Journal of Transport Geography
  • Volume: 100
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0966-6923
  • Serial URL: http://www.elsevier.com/locate/jtrangeo

Subject/Index Terms

• TRT Terms: Fleet management; Geospatial data; Predictive models; Ridesharing; Shared mobility; Travel demand management
• Geographic Terms: Germany
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01843967
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 25 2022 5:02PM