Application of Extreme Learning Machine on Large Scale Traffic Congestion Prediction

Abstract

Short-Term prediction aimed at the urban traffic congestion is an important goal for Intelligent Transport Systems (ITS). Short-term traffic prediction module tries to predict Traffic Congestion Index accurately and in approximately real-time. Up to date, there have been three basic methods in short-Term traffic prediction research namely the Kalman Filtering (KF) (Okutani and Stephanedes in Transport Res Part B 18B:1–11, 1984, [1]) method, Time Series models (Williams and Hoel in ASCE J Transport Eng 129:664–672, 2003, [2]) and Neural Network (NN) models (Smith and Demetsky in Transport Res Rec J Transport Res Board 1453:98–104, 1994, [3]). The Neural Networks based methods have proven to give good accuracy rate but they are time consuming in training. In our paper, we have implemented the new Neural Networks based algorithm called Extreme Learning Machine (ELM) (Huang and Siew in ICARCV, pp. 1029–1036, 2004, [4]) to design a real-time traffic index in the data of a real world city of Nanning in South China. Our experiment results show that ELM algorithm provides good generalization performance at extremely fast learning speed compared with other state-of-art algorithms. The algorithm obtains high accuracy in practical prediction application. In addition, quick training and good fitting results on our own large scale traffic data set proves ELM algorithm works well on large data sets.