A big data based deep learning approach for vehicle speed prediction

Abstract

Vehicle speed prediction plays an important role in Data-Driven Intelligent Transportation System (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ITS) and electric vehicle energy management. Accurately predicting vehicle speed for an individual trip is a challenging topic because vehicle speed is subjected to various factors such as route types, route curvature, driver behavior, weather and traffic condition. A big data based deep learning vehicle speed prediction algorithm featuring big data analytics and Adaptive Neuro-Fuzzy Inference System (ANFIS) is presented in this paper. Big data analytics examines copious amounts of speed related data to identify the pattern and correlation between input factors and vehicle speed. ANFIS model is constructed and configured, based on the analytics. The proposed speed prediction algorithm is trained and evaluated using the actual driving data collected by one test driver. Experiment results indicate that the proposed algorithm is capable of accurately predicting vehicle speed for both freeway and urban traffic networks.