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Electric vehicles (EVs) play a pivotal role in sustainable ride-hailingservices primarily due to their potential in reducing carbon emissions andenhancing environmental protection. Despite their significance, currentresearch in the realm of EV batched matching frequently overlooks criticalaspects such as rider demand uncertainty and charging station (CS) selection,leading to inefficiencies like decreased matching rates and prolonged waitingtimes for both riders and EV drivers. To fill the research gap, we propose adata-driven optimization framework that incorporates two inter-connectedstochastic optimization models to address the challenges. The first model aimsto relocate the idle EVs under satisfied conditions to the designated regionsbased on the probabilistic rider demand forecasting result before the realrider demand is revealed. Taking the solutions of the first model as the input,the second model optimizes the batched matching results by minimizing therider's average waiting time and EV charging waiting time at CS. Thisintegrated framework not only elevates the matching rate through theincorporation of rider demand uncertainties in the guidance module but alsosubstantially curtails both rider and EV charging waiting times by synergizingguidance with CS selection choices. Empirical validation of our framework wasconducted through an extensive case study in New York City, utilizingreal-world data sets. The validation results demonstrate that the proposeddata-driven optimization framework outperforms the benchmark models in terms ofthe proposed evaluation metrics. Most importantly, when deploying ourframework, the charging waiting time of the EVs with low SOC can be reduced upto 73.6% compared to the benchmark model without CS selection.

Coordinating Guidance, Matching, and Charging Station Selection for  Electric Vehicle Ride-Hailing Services through Data-Driven Stochastic  Optimization