Predicting Public Transport User Honesty: A Machine Learning Approach to Lost Item Returns

Public transport (PT) users often experience instances of leaving items behind in the public transport system. Finders who come across these items may choose to keep them maliciously or, out of goodwill, decide to return them. This paper aims to utilize six (6) machine learning models, including LR, SVM, DT, RF, NB, and KNN, to predict the ability of finders to return found items. Nine (9) features, comprising four (4) demographic parameters (age, gender, income, and education), were used in the models’ prediction process. The study involved a total of 603 PT users in the Accra cosmopolitan area of Ghana to assess finder’s decision regarding returning found item(s). The classification success rates were obtained as follows: 86.740% (LR), 87.293% (SVM), 82.873% (DT), 85.083% (RF), 85.083% (GNB), and 87.845% (KNN) using Python codes. The RF model also performed well, considering the balance of performance with the desired precision and recall. RF, GNB, and LR achieved the highest AUC values (0.78), demonstrating strong discriminative ability in predicting user honesty.