Urban Car-Sharing Viability in the Knowledge Economy: A Monte Carlo-Based Planning Tool for Shared Mobility Policy

This study presents a Monte Carlo simulation to estimate the viability thresholds of car-sharing services in urban areas. Departing from traditional revenue-based assessments, the model uses real cost structures to identify the minimum population size required to reach critical mass. Critical mass, in this context, is defined as the number of users needed for a platform business to reach its breakeven point. The study also explains the increasing urban-rural divergence in adopting platform-based services. The findings highlight substantial urban-rural divides in service viability and offer concrete tools for policymakers to allocate resources and support infrastructure in underserved areas. This cost-based planning tool contributes to sustainable urban mobility policy by quantifying regional thresholds for service provision. To simulate these results, a multivariate model was developed that considers real market data in the car-sharing industry. The model considers several input variables to capture a broad spectrum of market conditions that feed into a final formulation providing two distinct results: an on-site service provider's critical mass and an estimation of the population required in an area for the company to reach its minimum threshold. The study provides a transparent, data-driven tool to support urban mobility planning and resource allocation. By identifying critical population thresholds for the economic viability of car-sharing services, the model enables policymakers and urban planners to make evidence-based decisions on where to facilitate or subsidise shared mobility infrastructures. The simulation was run 10,000 times to reflect a broad range of market conditions. Results from the Berlin case show a minimum viability threshold of approximately 1.12 million residents, with a probability of around 91% at Berlin’s current population of 3.71 million and near-certain viability at around 5.8 million residents. In regions falling below these thresholds, the tool helps to justify targeted interventions—such as financial grants, regulatory incentives or hybrid public-private service models—to mitigate accessibility gaps and reduce urban-rural mobility disparities. As such, the model contributes to a more efficient and equitable allocation of mobility resources and supports the strategic deployment of sustainable transport solutions in underserved areas.