This study presents a method for optimizing the placement of dynamic wireless charging installations for battery electric buses, addressing the growing need for efficient and scalable charging infrastructure in public transport systems. The proposed optimization model is formulated as a mixed-integer linear programming problem, and identifies optimal placement of dynamic wireless charging infrastructure on specific bus routes. A key contribution of this work is the comparative analysis of results derived from timetable data and measured data. Using a comprehensive dataset from the public transport fleet in Trondheim, Norway, including measured schedule deviations, we demonstrate that relying solely on timetable data underestimates actual charging infrastructure requirements. These findings highlight the importance of integrating operational data into infrastructure planning to enhance system resilience.