Ridepooling and Public Transit: How Pricing Schemes Reveal the Tradeoff between Intermodality and On-Demand Efficiency

Abstract

The largest on-demand ridepooling (DRT; demand-responsive transport) service in a single European city has officially been part of Hamburg’s public transit system since 2023. Policy makers, practitioners, and planners aim to provide a holistic mobility offering and by doing so, reduce the dependency on private car usage. Against this background, an agent-based simulation is presented and deployed to investigate how various pricing schemes influence the ridership of DRT service, with a particular focus on connections to/from traditional public transit (PT). This involves a novel process that addresses a common problem of service overcrowding while simulating fixed-size DRT fleets with a low mode share in agent-based transport models. The results suggest that a DRT discount for PT season ticket holders significantly increased overall ridership, whereas the number of intermodal trips remained constant. Similar results were observed for PT-quality-dependent pricing schemes, in which DRT was surcharged if parallel PT connections were relatively good, or discounted if they were relatively poor or nonexistent. In contrast, a direct discount for intermodal trips increased the share and the absolute number of intermodal trips, which tended to replace direct DRT trips. Most importantly, the results indicated a tradeoff between operator revenue (or the need for subsidies) and the share of intermodal trips.