Abstract
Short-term traffic flow prediction plays an important role in route guidance and traffic management. K-NN is considered as one of the most important methods in short-term traffic forecasting, but some disadvantages limit the widespread application. In this paper, we use four tests to find the key factors of the K-NN method, which will give inspires to the further research to improve the method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bosq, D. (1996). “Nonparametric statistics for stochastic processes.” Lecture Notes in Statist.
Oswald, R. K., W. T. Scherer, et al. (2000). “Traffic flow forecasting using approximate nearest neighbor nonparametric regression.” Final project of ITS Center project: Traffic forecasting: non-parametric regressions.
Clark, S. (2003). “Traffic prediction using multivariate nonparametric regression.” Journal of transportation engineering 129(2): 161-168.
Chang, G., Y. Zhang, et al. (2011). A summary of short - term traffic flow forecasting methods. 11th International Conference of Chinese Transportation Professionals (ICCTP 2011).
Gong, X. and F. Wang (2002). Three improvements on knn-npr for traffic flow forecasting. Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on, IEEE.
Abdulhai, B., H. Porwal, et al. (2002). “Short-term traffic flow prediction using neuro-genetic algorithms.” ITS Journal-Intelligent Transportation Systems Journal 7(1): 3-41.
Friedman, J. H., J. L. Bentley, et al. (1977). “An algorithm for finding best matches in logarithmic expected time.” ACM Transactions on Mathematical Software (TOMS) 3(3): 209-226.
Van Der Voort, M., M. Dougherty, et al. (1996). “Combining Kohonen maps with ARIMA time series models to forecast traffic flow.” Transportat ion Research Part C: Emerging Technologies 4(5): 307-318.
Zhu, J. and A. G.-O. Yeh (1998). “A self-learning short-term traffic forecasting system.” Horizons 72: 13.32.
Bentley, J. L. (1975). “Multidimensional binary search trees used for associative searching.” Communications of the ACM 18(9): 509-517.
Bentley, J. L. (1990). K-d trees for semidynamic point sets. Proceedings of the sixth annual symposium on Computational geometry, ACM.
Bentley, J. L. and J. H. Friedman (1979). “Data structures for range searching.” ACM Computing Surveys (CSUR) 11(4): 397-409.
Smith, B. L. and M. J. Demetsky (1997). “Traffic flow forecasting: comparison of modeling approaches.” Journal of transportation engineering 123(4): 261-266.
Smith, B. L., B. M. Williams, et al. (2002). “Comparison of parametric and nonparametric models for traffic flow forecasting.” Transportation Research Part C: Emerging Technologies 10(4): 303-321.
Li, L., Y. Li, et al. (2013). “Efficient missing data imputing for traffic flow by considering temporal and spatial dependence.” Transportation Research Part C: Emerging Technologies 34: 108-120.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Atlantis Press and the authors
About this paper
Cite this paper
Zhong, Jt., Ling, S. (2015). Key Factors of K-Nearest Neighbours Nonparametric Regression in Short-Time Traffic Flow Forecasting. In: Qi, E., Shen, J., Dou, R. (eds) Proceedings of the 21st International Conference on Industrial Engineering and Engineering Management 2014. Proceedings of the International Conference on Industrial Engineering and Engineering Management. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-102-4_2
Download citation
DOI: https://doi.org/10.2991/978-94-6239-102-4_2
Published:
Publisher Name: Atlantis Press, Paris
Print ISBN: 978-94-6239-101-7
Online ISBN: 978-94-6239-102-4
eBook Packages: EngineeringEngineering (R0)