Freeway travel time forecast using artifical neural networks with cluster method

This paper develops a novel travel time forecasting model using artificial neural network with cluster method. The core logic of the model is based on a functional relation between real-time traffic data as the input variables and actual travel time data as the output variable. Cluster method is employed to reduce the data features with fewer input variables while still preserving the original traffic characteristics. The forecasted travel time is then obtained by plugging in real-time traffic data into the functional relation. Our results show that the mean absolute percentage errors of the predicted travel time are mostly less than 22%, indicating a good forecasting performance. The proposed travel time forecasting model has shed some light on the practical applications in the intelligent transportation systems context.

[1]  Mark Dougherty.  Applications of neural networks in transportation , 1997 .

[2]  Peter C. Nelson,et al.  Application of fuzzy logic and neural networks for dynamic travel time estimation , 1999 .

[3]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[4]  Ying Lee,et al.  APPLYING DATA FUSION TECHNIQUES TO TRAVELER INFORMATION SERVICES IN HIGHWAY NETWORK , 2005 .

[5]  Lily Elefteriadou,et al.  Travel time estimation on a freeway using Discrete Time Markov Chains , 2008 .

[6]  Ying Lee,et al.  DATA FUSION AND FEATURE COMPOSITION APPROACH TO SEQUENTIAL ACCIDENT DURATION FORECASTING , 2007 .

[7]  A. Roddam,et al.  Cluster analysis of schistosome-specific antibody responses partitions the population into distinct epidemiological groups. , 2005, Immunology letters.

[8]  M. Nakip.  Segmenting the Global Market by Usage Rate of Industrial Products , 1999 .

[9]  Dongjoo Park,et al.  Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks , 1998 .

[10]  H. J. Van Zuylen,et al.  Accurate freeway travel time prediction with state-space neural networks under missing data , 2005 .

[11]  Hojjat Adeli,et al.  Neural network model for rapid forecasting of freeway link travel time , 2003 .

[12]  Xiaoyan Zhang,et al.  Short-term travel time prediction , 2003 .

[13]  Erik van Zwet,et al.  A simple and effective method for predicting travel times on freeways , 2004, IEEE Transactions on Intelligent Transportation Systems.

[14]  R Krikke.  SHORT-RANGE TRAVEL TIME PREDICTION USING AN ARTIFICIAL NEURAL NETWORK , 2002 .

[15]  Steven I-Jy Chien,et al.  Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based , 2001 .