Home   >   CSC-OpenAccess Library   >    Manuscript Information
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
MORE INFORMATION
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).
CITED BY (1)  
1 Rupnik, J., Davies, J., Fortuna, B., Duke, A., & Clarke, S. S. (2015, October). Travel Time Prediction on Highways. In Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on (pp. 1435-1442).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
C. H. Wei and Y. Lee. “Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data”. IEEE Transactions on Vehicular Technology. Vol. 56, 2007
D. Park and L. Rilett. “Forecasting multiple-period freeway link travel times using modular neural networks”. J. of Transportation Research Record, vol. 1617, pp.163-170. 1998
D. Park and L. Rilett. “Spectral basis neural networks for real-time travel time forecasting”. J. of Transport Engineering, vol. 125(6), pp.515-523, (1999)
H. Kitaoka, T. Shiga, H. Mori, E. Teramoto and T. Inoguchi. “Development of a Travel Time Prediction Method for the TOYOTA G-BOOK Telematics service”. R & D Review of TOYOTA CRDL vol. 41 no.4 ,2006
H. Lee, N. K. Chowdhury and J. Chang. “A New Travel Time Prediction Method for Intelligent Transportation System”. In: International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, LNAI 5177, pp: 473-483, 2008
J. Chang, N. K. Chowdhury and H. Lee. “New travel time prediction algorithms for intelligent transportation systems”. Journal of intelligent and fuzzy systems, vol.21, pp: 5-7, 2010.
J. Kwon and K. Petty. “A travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes”. Transportation Research Board 84th Annual Meeting, Washington, D.C. 2005
J. Kwon, B. Coifman and P. J. Bickel. “Day-to-day travel time trends and travel time prediction from loop detector data”. J. of Transportation Research Record, No. 1717, TRB, National Research Council, Washington, D.C., pp. 120-129. 2000
J. Rice and E. Van Zwet. “A simple and effective method for predicting travel times on freeways”. In: IEEE Trans. Intelligent Transport Systems, vol. 5, no. 3, pp. 200-207, 2004
J. Schmitt Erick and H. Jula. “On the Limitations of Linear Models in Predicting Travel Times”. In: IEEE Intelligent Transportation Systems Conference, 2007
J. W. C. V. Lint, S. P. Hoogenoorn and H. J. V. Zuylen. “Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks”. In Transportation Research Record: Journal of the Transportation Research Board, No. 1811, TRB, National Research Council, Washington, D.C., pp. 30-39. 2002
J. W. C. V. Lint, S. P. Hoogenoorn and H. J. V. Zuylen. “Towards a Robust Framework for Freeway Travel Time Prediction: Experiments with Simple Imputation and State-Space Neural Networks”. Presented at 82 Annual Meeting of the Transportation Research Board, Washington ,D.C., 2003
M. Chen and S. Chien. “Dynamic freeway travel time prediction using probe vehicle data: Link-based vs. Path-based”. J. of Transportation Research Record, TRB Paper No. 01- 2887, Washington, D.C. 2001
N. K. Chowdhury, R. P. D. Nath, H. Lee and J. Chang. “Development of an Effective Travel Time Prediction Method using Modified Moving Average Approach”. 13th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. Part 1. LNAI 5711, pp: 130-138 2009
R. P. D. Nath, H. Lee, N. K. Chowdhury and J. Chang. “Modified K-means Clustering for Travel Time Prediction Based on Historical Traffic Data”. 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. Part 1. LNAI 6276, pp: 511-521, 2010.
S. UI, I. Bajwa and M. Kuwahara, “A Travel Time Prediction Method Based on Pattern Matching Technique”. In proceedings of the 21st ARRB and 11th REAAA Conference. Transport. Vermont South, Victoria 3133, ZZ N/A Australia.2003.
W. Chun-Hsin, W. Chia-Chen, S. Da-Chun, C, Ming-Hua and H. Jan-Ming. “Travel Time Prediction with Support Vector Regression”. IEEE Intelligent Transportation Systems Conference, 2003
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


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS