A deep learning method of predicting hourly boarding demand of bus passengers using imbalance records from smart cards

Data from tap-on smart cards is a useful tool for predicting future travel demand and learning about customers' boarding habits. In contrast to negative instances (not boarding at that bus stop at that time), positive instances (i.e., boarding at a specific bus stop at a certain time) are uncommon when looking at the smart-card records (or instances) by boarding stops and by time of day. It has been shown that machine learning algorithms used to forecast hourly boarding numbers from a certain site are far less accurate when the data is unbalanced. Prior to using the smart card data to forecast bus boarding demand, this research resolves the problem of data imbalance. In order to supplement a synthetic training dataset with more evenly distributed travelling and non-traveling cases, we suggest using deep generative adversarial networks (Deep-GAN) to create dummy travelling instances. A deep neural network (DNN) is then trained using the synthetic dataset to forecast the travelling and non-traveling instances from a certain stop within a specified time range. The findings demonstrate that resolving the issue of data imbalance may greatly enhance the prediction model's functionality and better match the true profile of passengers. A comparison of the Deep-GAN's performance with that of other conventional resampling techniques demonstrates that the suggested approach may generate a synthetic training dataset with greater variety and similarity, and consequently, a stronger prediction capability. The importance of enhancing data quality and model performance in travel behaviour prediction and individual travel behaviour analysis is emphasised in the study, along with helpful recommendations.