Accurate battery aging prediction is essential for ensuring efficient, reliable, and safe operation of battery systems in electric vehicle application. This article presents a novel battery aging assessment method based on the incremental capacity analysis (ICA) and radial basis function neural network (RBFNN) model. The RBFNN model is used to depict the relationship between battery aging level and its influencing factors based on real-world operation datasets of electric city transit buses. The ICA method together with the Gaussian window (GW) filter method is used to derive the peak values of IC curves which are utilized to represent battery aging levels, and the support vector regression (SVR) method is used in several scenarios for data preprocessing. The considered influencing factors include accumulated mileage of vehicles and initial charging state-of-charge (SOC), average charging temperature, average charging current, and average operating temperature of battery systems. The datasets collected from real-world electric city buses are used for RBFNN model training, validation, and test. The results show that an average prediction error of 4.00% is reached, and the derived model has a confidential interval of 92% with the prediction accuracy of 90%. This work provides insights for battery aging prediction based on massive real-time operation data.