Abstract
It is the aim of this research to adopt the data dig technology in the respect of prediction of traffic circumstance and condition by making use of ITS information. There were study results as examples by means of the tool of inductive loop detectors, which were collected to be used as study materials, including 1-37, I-10 and 1-410 in San Antonio. It’s usual to use Support Vectors Machine that is one kind of new data dig technology to absorb different knowledge existed in concealment pattern, potential relations and tendencies between variable quantities. Then one compared analysis was made between the prediction results and another two variables, namely BP neural system and Response Surface Methodology, or say RSM which is known as a traditional means in statistics. According to the result, the SVM model is superior to BP neural system and RSM model as to the aspects of MAPE and the RMSE.
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
References
Wang, Y.: Research on the traffic flow data mining in road network. Fudan University, December 2007
Cui, Z.: Model and analysis of automatic incident detection based on support vector machine. Beijing Jiaotong University, June 2008
Li, B.: On the recursive estimation of vehicular speed using data from a single inductance loop detector: a Bayesian approach. Transp. Res. Part B 43, 391–402 (2009)
Nguyen, M.N., Shi, D., Quek, C., Ng, G.S.: Traffic prediction using Ying-Yang fuzzy cerebellar model articulation controller. In: ICPR 2006 (2006)
Xia, H., Bi, J., Li, Y.: Support vector machine approach for retained introns prediction using sequence features. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 654–659. Springer, Heidelberg (2006). https://doi.org/10.1007/11760191_96
Vert, J.-P.: Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings. Pac. Symp. Biocomput. 7, 649–660 (2002)
Smith, B.L., Williams, B.M., Oswalsd, R.K.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C 10(4), 303–321 (2002)
Agrawal, R., Kiernan, J.: Watermarking relational databases. In: Proceedings of the 28th VLDB Conference, Hong Kong, China (2002)
Sion, R., Atallah, M., Prabhakar, S.: Watermarking relational databases. CERIAS TR 2002-28 (2002)
Sion, R., Atallah, M., Prabhakar, S.: On watermarking numeric sets. In: Kim, H.J. (ed.) IWDW 2002. LNCS, vol. 2613, pp. 130–146. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36617-2_12
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lei, D., Zhong, W. (2018). Traffic Condition Assessment Based on Support Vectors Machine Using Intelligent Transportation System Data. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-1648-7_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1647-0
Online ISBN: 978-981-13-1648-7
eBook Packages: Computer ScienceComputer Science (R0)