Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic Study

The prediction of bus travel time with accuracy is a significant step toward improving the quality of public transportation. Drawing meaningful inferences from the data and using these to aid in prediction tasks is always an area of interest. Earlier studies predicted bus travel times by identifying significant regressors, which were identified based on chronological factors. However, travel time patterns may vary depending on time and location. A related question is whether the prediction accuracy can be improved with the choice of input variables. The present study analyzes this question systematically by presenting the input data in different ways to the prediction algorithm. The prediction accuracy increased when the dataset was grouped, and separate models were trained on them, the highest accurate case being the one where the data-derived clusters were considered. This demonstrates that understanding patterns and groups within the dataset helps in improving prediction accuracy.

Automated summary

The accuracy of predicting bus travel time is an important step in improving the quality of public transportation. Previous studies have used chronological factors to identify significant regressors for predicting travel times. However, travel time patterns can vary depending on time and location. This study systematically analyzes the impact of different ways of presenting input data to the prediction algorithm. The results show that grouping the dataset and training separate models on them, particularly considering data-derived clusters, can significantly improve prediction accuracy.