Physics: Modelling improvements to ride-sharing adoption

Insights into the adoption of ride-sharing by users in cities are presented in a Nature Communications paper this week. The research suggests that a moderate increase in financial incentives for people to use ride-sharing services may have a substantial effect on their adoption.

Analysis and modelling of ride sharing — the combination of multiple trips into one — has demonstrated the technical efficiency, algorithmic feasibility and potential positive impacts of this system on the development of transport infrastructure. However, in practice, areas with high levels of traffic often show low levels of ride-sharing adoption. Understanding under which conditions people are willing to adopt ride-sharing is therefore needed.

David Storch, Marc Timme and Malte Schröder investigated ride-sharing scenarios within cities with large amounts of traffic. They applied a combination of game theory with data-driven methods, taking into account the perspective of users’ incentives to use ride-sharing services. Their theoretical findings are supported by an analysis of over 360 million ride requests from New York City and Chicago in 2019. The authors observe two regimes for ride-sharing coexist in cities: one where the number of shared rides increases as the overall demand for rides increases, and another with a constant level of adoption. They also uncover scenarios leading to abrupt switches between them. The findings suggest that current financial incentives for ride-sharing services are close to the boundary at which high levels of sharing would take place, and a small increase in financial incentives may have a disproportionate effect on increasing ride-sharing adoption.