Combining analytics and simulation methods to assess the impact of shared, autonomous electric vehicles on sustainable urban mobility
以柏林为案例，展示了结合数据驱动分析和模拟技术的决策支持系统如何评估共享自动驾驶电动汽车对城市交通的影响，发现其能在保持服务水平的同时大幅减少资源投入，为利益相关者权衡经济与可持续性提供依据。
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.