Integrating Rough Sets with Neural Networks for Weighting Road Safety Performance Indicators

Authors:
Tianrui Li;Yongjun Shen;Da Ruan;Elke Hermans;Geert Wets
Affiliations:
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, P.R. China 610031;Transportation Research Institute, Hasselt University, Diepenbeek, Belgium 3590;Transportation Research Institute, Hasselt University, Diepenbeek, Belgium 3590 and Belgian Nuclear Research Centre (SCK-CEN), Mol, Belgium 2400;Transportation Research Institute, Hasselt University, Diepenbeek, Belgium 3590;Transportation Research Institute, Hasselt University, Diepenbeek, Belgium 3590
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

Citing 5
Cited 0

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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.