Modified K-Means Clustering for Travel Time Prediction Based on Historical Traffic Data

Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Naive Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).

[1]  Hossein Jula,et al.  On the Limitations of Linear Models in Predicting Travel Times , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[2]  S. P. Hoogendoorn,et al.  Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks , 2002 .

[3]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[4]  Peter J. Bickel,et al.  Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data , 2000 .

[5]  Steven I-Jy Chien,et al.  Dynamic freeway travel time prediction using probe vehicle data: Link-based vs , 2001 .

[6]  Serge P. Hoogendoorn,et al.  Toward a Robust Framework for Freeway Travel time Prediction: Experiments with Simple Imputation and State-Space Neural Networks , 2003 .

[7]  Ying Lee,et al.  Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data , 2007, IEEE Transactions on Vehicular Technology.

[8]  Hyunjo Lee,et al.  Development of an Effective Travel Time Prediction Method Using Modified Moving Average Approach , 2009, KES.

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

[10]  Karl Petty,et al.  Travel Time Prediction Algorithm Scalable to Freeway Networks with Many Nodes with Arbitrary Travel Routes , 2005 .

[11]  Hyunjo Lee,et al.  A New Travel Time Prediction Method for Intelligent Transportation Systems , 2008, KES.

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

[13]  Laurence R. Rilett,et al.  Spectral Basis Neural Networks for Real-Time Travel Time Forecasting , 1999 .

[14]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.