Institute of Transportation, MOTC

       Mobility as a service (MaaS) is an emerging concept in recent years, and it has been promoted in many cities around the world. Understanding users’ purchase behavior of MaaS packages will help the authorities and operators to promote MaaS. This study develops a package-purchasing prediction model using data mining techniques. The prediction model is built and trained using the data of membership registration, package-purchasing records and iPASS card transit-ridership records of MaaS users in Kaohsiung. Through preliminary data processing and analysis, it is found that significant imbalance exists in MaaS users’ package-purchasing records for the different plans, and the imbalance may affect the prediction results of the model. To address this issue, the study proposes an oversampling method, namely probability distribution-based over-sampling (PDB), to generate additional samples. This method is first tested and compared with the SMOTE (synthetic minority over-sampling technique) method commonly used in the literature by using datasets published online, and it is found that the proposed method is significantly better than the SMOTE method. Then the study uses the method to balance the MaaS users’ package-purchasing data, and constructs a decision tree model and a support vector machine model for MaaS users’ package-purchasing prediction. Through cross-validation test results, it is found that the models constructed by the oversampling data using the simulation method has good prediction results which shows that the prediction model has the ability to predict the users’ purchase behavior. This study also discusses the branch variables in the decision tree model, and found that the user's monthly spending on each public transportation mode will affect the package-purchasing of MaaS users. Moreover, month is also an important variable. The results can be used as a reference for MaaS operators to take appropriate actions on marketing based on the results of the users’ package-purchasing prediction.