To understand why ridesourcing markets may be prone to evolve towards potentially socially undesirable equilibrium states, we conceptualize the network effects present in ridesourcing provision. In addition, we propose an agent-based model that allows simulating the effect of market conditions and platform strategies on system performance, accounting for such network effects. This day-to-day model captures sequential decentralized processes characterizing both sides of the two-sided ridesourcing market, i.e. information diffusion, platform registration, platform participation, and learning based on experience. We apply the model on a case representing Amsterdam, the Netherlands. Our simulation results suggest that a profit-maximizing ridesourcing platform may trade-off market transaction volume for higher earnings on successful transactions, a strategy that is harmful to the interests of travellers and drivers, and possibly of (very) limited benefit to the platform. Moreover, we find that ridesourcing operations may be viable even when potential supply and demand in an area are limited.@en

This PhD dissertation examines the societal implications of newly emerged, two-sided ridesourcing platforms that connect travellers with self-employed drivers. It uses an agent-based approach to model the dynamic interaction between ridesourcing supply and demand, exploring how market outcomes are influenced by travel demand, labour market conditions, and platform strategies. The findings inform policymakers on how to enhance the welfare effects of these markets for travellers, drivers, and the broader public.@en

Limited available market share data seems to suggest that ridesourcing platforms benefit from, even thrive on, socio-economic inequality. We suspect that this is associated with high levels of socio-economic inequality allowing for cheap labour as well as increasing the share of travellers with a considerably above-average willingness to pay for travel time savings and comfort. We test the relation between inequality and system performance by means of an agent-based simulation model representing within-day and day-to-day supply-demand interaction in the ridesourcing market. The model captures travellers’ mode choice with a heterogeneous perception of relevant time components, as well as job seekers’ participation choice with heterogeneous reservation wage. Our experiments cover scenarios for the entire spectrum ranging from perfect equality to extreme inequality. For several of such scenarios, we explore alternative platform pricing strategies. Our analysis shows a strong, positive relationship between socio-economic inequality and ridesourcing market share. This is the outcome of the combination of cheap labour and time-sensitive ridesourcing users, reinforced by network effects inherent to ridesourcing markets. We find that driver earnings are minimal in urban areas with large socio-economic inequality. In such contexts, drivers are likely to face a high platform commission, and yet, fierce competition for passengers.

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Previous studies into the potential benefits of ride pooling failed to account for the trade-off that users likely make when considering a shared ride. We address this shortcoming by formulating user net benefit stemming from pooling as a compensatory function where the additional travel time and on-board discomfort need to be compensated by the price discount for a traveller to choose a pooled ride over a private ride. The proposed formulation is embedded in a method for matching travel requests and vehicles. We conduct a series of experiments investigating how the potential of ride-pooling services depends on demand characteristics, user preferences and the pricing policy adopted by the service provider. Our results suggest that the total vehicle mileage savings found by previous studies is only attainable when users are very willing to share their ride (i.e. attach low premium to private rides) and are offered a 50% discount for doing so.

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Contrary to traditional transit services, supply in ridesourcing systems emerges from individual labour decisions of gig workers. The effect of decentralisation in supply on the evolution of on-demand transit services is largely unknown. To this end, we propose a dynamic model comprising of the subsequent supply-side processes: (i) initial exposure to information about the platform, (ii) a long-term registration decision, and (iii) daily participation decisions, subject to day-to-day learning based on within-day matching outcomes. We construct a series of experiments to study the effect of supply market properties and pricing strategies. We find that labour supply in ridesourcing may be non-linear and undergo several transitions, inducing significant variations in income levels and level of service over time. Our results provide indications that the ridesourcing market may benefit from a cap in supply and regulation of the commission fee.

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