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A Naïve Clustering Approach in Travel Time Prediction
Rudra Pratap Deb Nath, Nihad Karim Chowdhury, Masaki Aono
Pages - 62 - 74     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 2   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Travel Time Prediction, Advanced Traveler Information Systems (ATIS), Naïve Clustering Approach(NCA), Cumulative Cloning Average (CCA), Successive Moving Average (SMA), Chain Average (CA)
ABSTRACT
Travel time prediction plays an important role in the research domain of Advanced Traveler Information Systems (ATIS). Clustering approach can be acted as one of the powerful tools to discover hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our proposed Naïve Clustering Approach (NCA), we partition a set of historical traffic data into several groups (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment, day group and time group. In each cluster, data objects are similar to one another and are sufficiently different from data objects of other groups. To choose centroid of a cluster, we introduce a new method namely, Cumulative Cloning Average. For experimental evaluation, comparison is also focused to the forecasting results of other four methods namely, Rule Based method, Naïve Bayesian Classification (NBC) method, Successive Moving Average (SMA) and Chain Average (CA) by using same set of historical travel time estimates. The results depict that the travel time for the study period can be predicted by the proposed strategy with the minimum Mean Absolute Relative Errors (MARE) and Mean Absolute Errors (MAE).
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Mr. Rudra Pratap Deb Nath
Toyohashi University of Technology - Japan
prataprudracsecu@gmail.com
Mr. Nihad Karim Chowdhury
UNIVERSITY OF MANITOBA - Canada
Professor Masaki Aono
Toyohashi University of Technology - Japan