Abstract
This paper aims at improving two main uncertain factors in neural networks training in developing a composite road safety performance indicator. These factors are the initial value of network weights and the iteration time. More specially, rough sets theory is applied for rule induction and feature selection in decision situations, and the concepts of reduct and core are utilized to generate decision rules from the data to guide the self-training of neural networks. By means of simulation, optimal weights are assigned to seven indicators in a road safety data set for 21 European countries. Countries are ranked in terms of their composite indicator score. A comparison study shows the feasibility of this hybrid framework for road safety performance indicators.
This work is partially supported by NSFC (No.60873108,60875034), the Research Fund for the Doctoral Program of Higher Education (No.20060613007) and the Basic Science Foundation of Southwest Jiaotong University (No.2007B13), China.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
European Transport Safety Council, Transport Safety Performance Indicators, ETSC, Brussels (2001)
Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., Giovannini, E.: Handbook on Constructing Composite Indicators: Methodology and User Guide, Organisation for Economic Cooperation and Development (2005)
Hermans, E., Van den Bossche, F., Wets, G.: Combining road safety information in a performance index. Accident Analysis and Prevention 40, 1337–1344 (2008)
Shen, Y., Hermans, E., Ruan, D., Wets, G., Vanhoof, K., Brijs, T.: Development of a composite road safety performance index based on neural networks. In: Proceedings of 2008 International Conference on Intelligent Systems and Knowledge Engineering, vol. 2, pp. 901–906. IEEE Press, Los Alamitos (2008)
SafetyNet, Work Package 3, State-of-the-art Report on Road Safety Performance Indicators (2005)
Litman, T.: Developing Indicators for Comprehensive and Sustainable Transport Planning. In: The 86th annual meeting of the Transportation Research Board, Washington, DC (2007)
SARTRE 3 Report: European Drivers and Road Risk (2004)
Pawlak, Z.: Rough sets. In: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston (1991)
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)
Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177(1), 28–40 (2007)
Skowron, A.: Extracting Laws from Decision Tables: A Rough Set Approach. Computational Intelligence 11, 371–388 (1995)
Peters, J.F., Skowron, A.: A rough set approach to knowledge discovery. International Journal of Computational Intelligence System 17(2), 109–112 (2002)
Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 833–849 (2003)
Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets. Encyclopedia of Database Technologies and Applications, 575–580 (2005)
Swiniarski, R.W.: Rough set methods in feature reduction and classification. International Journal of Applied Mathematics and Computer Science 11(3), 565–582 (2001)
Li, T., Ruan, D., Wets, G., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowledge-Based Systems 20(5), 485–494 (2007)
Midelfart, H., Komorowski, H.J., Nørsett, K., Yadetie, F., Sandvik, A.K., Lægreid, A.: Learning rough set classifiers from gene expressions and clinical data. Fundamental Informaticae 53, 155–183 (2002)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Øhrn, A., Komorowski, J., Skowron, A., Synak, P.: The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets: The ROSETTA System. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodoloy and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 376–399. Physica-Verlag, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, T., Shen, Y., Ruan, D., Hermans, E., Wets, G. (2009). Integrating Rough Sets with Neural Networks for Weighting Road Safety Performance Indicators. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-02962-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02961-5
Online ISBN: 978-3-642-02962-2
eBook Packages: Computer ScienceComputer Science (R0)