System Dynamics as a Tool for Data Driven Business Model Design in the Context of Autonomous Ride Hailing

Despite the rapid growth of mobility services, high uncertainty remains about how business models should be composed in the future. Several pilot projects and multiple ride hailing services have already explored the opportunities of chauffeured ride hailing and thereby produced large amounts of data. However, it is unclear, whether the introduction of autonomous vehicles will change business models. In today's business design process, well established frameworks such as the business model canvas and business plan fail to adequately incorporate big amounts of data. This applies especially, when there is interaction between (opposing or self-amplifying) effects, so called causal loops. System Dynamics has been proven to be a useful tool to master complex problems mostly within the field of operations research and is increasingly used to solve macroeconomic problems since the early 2000s. To date, the methodology has not been applied to the business model strategy of mobility service providers. In this study, a model of the city of San Francisco has been created and calibrated to ride hailing providers Uber's and Lyft's transport statistics data. The simulation reproduces recorded data at an accuracy better than 10%. The model is subsequently extrapolated to explore business characteristics in an autonomous driving scenario. Key findings within the given scenario are an optimal vehicle range of 200 km, optimal charging speed of >25 kW. Further analysis shows the reduced importance of fleet utilization when autonomous driving is introduced. Consequently, the opportunities of market segmentation are explored. In this model, a mass market offering, addressing 50% of inner city mobility use cases, would yield a profit of 17 Mn EUR per year at a passenger price of 0,266 EUR/km. In comparison, an offering aimed only at the 10% of well-paying inner-city mobility use cases and users can make the same profit by selling to passengers at 0,755 EUR/km, even if this requires vehicles priced three times as high as in the former scenario.