TY - CHAP A1 - Fischer, Markus A1 - Michalk, Wibke A1 - Bogenberger, Klaus ED - Transportation Research Board, T1 - CDRpy: Data-Driven Decision Support for the Roll-out of public Charging Infrastructure in urban Areas T2 - Transportation Research Board Annual Meeting N2 - In this study, the data-driven simulation framework CDRpy is presented, validated and applied using the example of the city of Munich to support decision-makers in the rollout of public charging infrastructure in urban areas. The innovation of the study lies in the fact that CDRpy can be used to evaluate different concepts for public charging infrastructure at a detailed level. The Demand is mapped based on real charging events for different types of electric vehicles and pricing models, and the evaluations can be broken down to the level of individual charging stations. The developed framework was validated using charge detail records from the largest operator of public charging infrastructure in Munich for the years 2020, 2021 and 2022 based on various criteria. In a further case study, the additional demand for public charging infrastructure in the city was estimated for the year 2030. The results indicate that mixed and timebased pricing models could reduce the required number of public charging points from 7,055 to 6,374 by around 10% compared to energy-based pricing models. Determining the expansion requirements for each sub-district shows that the additional demand for public charging infrastructure varies greatly across the city. Since both technological and economic perspectives are considered, the study is relevant for all decisionmakers involved in the planning, operation and scaling of public charging infrastructure. KW - Plug-in Electric Vehicles KW - public charging infrastructure KW - roll-out of public charging stations KW - pricing policies KW - charge detail records Y1 - 2025 ER -