Modified K-means clustering for travel time prediction based on historical traffic data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6276 LNAI, Issue PART 1, 2010, Pages 511-521

  Deb Nath, Rudra Pratap ^a   Lee, Hyun Jo ^b   Chowdhury, Nihad Karim ^a   Chang, Jae Woo ^b  

^a UNIVERSITY OF CHITTAGONG   (Bangladesh)

^b Chonbuk National University   (South Korea)

Author keywords

Chain Average (CA); Intelligent Transportation System (ITS); K means Clustering; Na ve Bayesian Classification (NBC); Successive Moving Average (SMA)

Indexed keywords

BAYESIAN CLASSIFICATION; CLUSTERING STRATEGY; HIDDEN KNOWLEDGE; HISTORICAL DATA; INTELLIGENT TRANSPORTATION SYSTEMS; K-MEANS CLUSTERING; MOVING AVERAGES; RELATIVE ERRORS; RESEARCH DOMAINS; ROAD SEGMENTS; TRAFFIC DATA; TRAVEL TIME; TRAVEL TIME PREDICTION;

BAYESIAN NETWORKS; INTELLIGENT SYSTEMS; KNOWLEDGE BASED SYSTEMS; VEHICLE LOCATING SYSTEMS;

FORECASTING;

EID: 78449255254     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15387-7_55     Document Type: Conference Paper

References (13)

1. Chen, M., Chien, S.: 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)
2. Wei, C.H., Lee, Y.: Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data. IEEE Transactions on Vehicular Technology 56 (2007)
3. Chun-Hsin, W., Chia-Chen, W., Da-Chun, S., Ming-Hua, C., Jan-Ming, H.: Travel Time Prediction with Support Vector Regression. In: IEEE Intelligent Transportation Systems Conference (2003)
4. Kwon, J., Petty, K.: A travel time prediction algorithm scalable to freeway networks with many nodes with arbitrary travel routes. In: Transportation Research Board 84th Annual Meeting, Washington, D.C. (2005)
5. Park, D., Rilett, L.: Forecasting multiple-period freeway link travel times using modular neural networks. J. of Transportation Research Record 1617, 163-170 (1998)
6. Park, D., Rilett, L.: Spectral basis neural networks for real-time travel time forecasting. J. of Transport Engineering 125(6), 515-523 (1999)
7. Lint, J.W.C.V., Hoogenoorn, S.P., Zuylen, H.J.v.: Towards a Robust Framework for Freeway Travel Time Prediction: Experiments with Simple Imputation and State-Space Neural Networks. In: Presented at 82 Annual Meeting of the Transportation Research Board, Washington, D.C (2003)
8. Lint, J.W.C.V., Hoogenoorn, S.P., Zuylen, H.J.v.: Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks. Transportation Research Record: Journal of the Transportation Research Board, No. 1811, TRB, National Research Council, Washington, D.C, 30-39 (2002)
9. Kwon, J., Coifman, B., Bickel, P.J.: 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., 120-129 (2000)
10. Rice, J., Van Zwet, E.: A simple and effective method for predicting travel times on freeways. IEEE Trans. Intelligent Transport Systems 5(3), 200-207 (2004)
11. Schmitt Erick, J., Jula, H.: On the Limitations of Linear Models in Predicting Travel Times. In: IEEE Intelligent Transportation Systems Conference (2007)
12. Lee, H., Chowdhury, N.K., Chang, J.: A New Travel Time Prediction Method for Intelligent Transportation System. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part I. LNCS (LNAI), vol. 5177, pp. 473-483. Springer, Heidelberg (2008)
13. Chowdhury, N.K., Nath, R.P.D., Lee, H., Chang, J.: Development of an Effective Travel Time Prediction Method using Modified Moving Average Approach. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009, Part I. LNCS, vol. 5711, pp. 130-138. Springer, Heidelberg (2009)