Abstract
Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobility.
Highlights
Over the past decade, the sharing economy has fundamentally affected a variety of industry and service sectors, such as transport, finance, entertainment, and education
Focusing on the concept of shared, autonomous electric vehicles (SAEVs), we investigate the effect of driverless vehicles on these challenges and analyze, in turn, to which degree constraints imposed by vehicle sharing and electrification shape the impact of autonomous vehicles on urban transportation
The time required for refueling the vehicle with gasoline is negligible, as it amounts to approximately 300 min per day for the entire fleet, assuming that a range of 500 km can be refueled in 5 min
Summary
The sharing economy has fundamentally affected a variety of industry and service sectors, such as transport, finance, entertainment, and education. Focusing on the concept of shared, autonomous electric vehicles (SAEVs), we investigate the effect of driverless vehicles on these challenges and analyze, in turn, to which degree constraints imposed by vehicle sharing and electrification shape the impact of autonomous vehicles on urban transportation.3 For this purpose, we combine data analytics and an agentbased simulation model, leveraging real-world data on both carsharing trips and charging locations. The effect persists when electrification comes into play, but, more interestingly, satisfying the entire current carsharing trip demand of the investigated operator for Berlin – approximately 5700 trips per day – only requires about 30 charge points Overall, these results emphasize the relevance of data-driven decision support systems for environmental sustainability, providing valuable insights for current discussions on sustainable urban transportation, investments into EV charging infrastructure, and shared mobility systems.
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