A Repeated Commuting Driving Cycle Dataset With Application to Short-Term Vehicle Velocity Forecasting

Vehicle velocity forecasting plays a critical role in operation scheduling of varying systems and devices for a passenger vehicle. The forecasted information serves as an indispensable prerequisite for vehicle energy management via predictive control algorithms or vehicle ecosystem control Co-design. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. To explore the dynamic properties, these driving cycles are piecewise divided into cycle segments via intersection/stop identification. A vehicle velocity forecasting model pool is then developed for each segment, including the hidden Markov chain model, long short-term memory network, artificial neural network, support vector regression, and similarity methods. To further improve the forecasting performance, higher-level algorithms like localized model selection, ensemble approaches, and a combination of them are investigated and compared. Results show that (i) the segment-based forecast improves the forecasting accuracy by up to 20.1%, compared to the whole cycle-based forecast, and (ii) the hybrid localized model framework that combines dynamic model selection and an ensemble approach could further improve the accuracy by 9.7%. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.