Anomalous supply shortages from dynamic pricing in on-demand mobility

Abstract

Dynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalances instead of preventing them. Combining game theory and time series analysis of dynamic pricing data from on-demand ride-hailing services, we explain this apparent contradiction. We derive a phase diagram demonstrating how and under which conditions dynamic pricing incentivizes collective action of ride-hailing drivers to induce anomalous supply shortages. We identify characteristic patterns in the price dynamics reflecting these supply anomalies by disentangling different timescales in price time series of ride-hailing services at 137 locations across the globe. Our results provide systemic insights for the regulation of dynamic pricing, in particular in publicly accessible mobility systems, by unraveling under which conditions dynamic pricing schemes promote anomalous supply shortages.

Spontaneous symmetry breaking in ride-sharing adoption dynamics

“…Importantly, these complex dynamics need not be rooted in the algorithms underlying new types of mobility services, spatially distributed vehicle fleets, or the inherent heterogeneities, but may ultimately result from interactions among the users alone. These findings emphasize the importance of understanding the collective dynamics of both the supply [25] and demand sides [30] of ride-sharing, and the interactions between them, to enable more sustainable mobility [42]. Revealing the fundamental detour-mediated interactions between the users as the cause of pattern formation dynamics of ride-sharing adoption, our modeling approach may contribute to a better understanding of the (non)adoption of ride-sharing services.…”

“…The efficiency of such a service strongly depends on the complex interactions between customer requests and the positions and routes of the vehicles [21][22][23][24]. Importantly, a high degree of self-organization of the drivers and the routes of the vehicle fleet may lead to collective dynamics that limit the efficiency of the ride-sharing service or the urban mobility system as a whole [25,26]. Still, the combination of multiple trips into one vehicle potentially reduces the total distance driven compared to individual mobility options and makes ride-sharing a promising option to reduce congestion and emissions with growing urban mobility demands [21,[27][28][29].…”

Incentive-driven transition to high ride-sharing adoption

“…On the other hand, the overall low fraction of shared ride requests observed in the empirical trip records, even in the high-sharing regime, suggests that an additional societal change towards acceptance of shared mobility is required 43 to make the full theoretical potential of ride-sharing accessible 9 , 17 . A carefully designed incentive structure for ride-sharing users adapted to local user preferences is essential to drive this change and to avoid curbing user adoption or stimulating unintended collective states 44 , 45 . This is particularly relevant in the light of increasing demand as urbanization progresses 1 .…”

Incentive-driven transition to high ride-sharing adoption

“…Still, the overall low fraction of shared ride requests, even in the high-sharing regime, suggests that an additional societal change towards acceptance of shared mobility is required [31] to make the full theoretical potential of ride-sharing accessible [9,12]. A carefully designed incentive structure for ride-sharing users adapted to local user preferences is essential to drive this change and to avoid curbing user adoption or stimulating unintended collective states [32,33]. This is particularly relevant in the light of increasing demand as urbanization progresses [1].…”