Hybrid time series forecasting methods for travel time prediction

Providing accurate information about travel time to passengers is important in public transportation. In this aspect, the travel time of buses between two consecutive stops can be handled as time series. Then, the future travel time can be predicted using time series forecasting methods. In this study, we propose a novel method with three-layer architecture to predict bus travel time between two stops. In the first layer of the proposed method, initial prediction is made by processing measured data. In the second layer, residuals are predicted in the specified depth. In the third layer, the final prediction is made by integrating the results of two previous layers with three different approach. The experiments were performed on the data, which were obtained from public transportation of Istanbul, using various time series forecasting methods in form of traditional and proposed architecture. The results show that proposed method outperforms traditional approach with approximately MAPE of 6. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Providing accurate travel time information is crucial in public transportation. A novel three-layer architecture method is proposed to predict bus travel time between two stops, outperforming traditional approaches with an approximate MAPE of 6 in experiments using Istanbul's public transportation data.