@article{2918498,
  abstract     = {Road safety performance indicators are comprehensible tools that provide a better understanding of current safety conditions and can be used to monitor the effect of policy interventions. New insights can be gained in case one road safety index is composed of all risk indicators. The overall safety performance can then be evaluated, and countries ranked. In this paper, a promising structure of neural networks based on decision rules generated by rough sets—is proposed to develop an overall road safety index. This novel hybrid system integrates the ability of neural networks on self-learning and that of rough sets on automatically transforming data into knowledge. By means of simulation, optimal weights are assigned to seven road safety performance indicators. The ranking of 21 European countries in terms of their road safety index scores is compared to a ranking based on the number of road fatalities per million inhabitants. Evaluation results imply the feasibility of this intelligent decision support system and valuable predictive power for the road safety indicators context.},
  author       = {Shen, Yongjun and Li, Tianrui and Hermans, Elke and Ruan, Da and Wets, Geert and Vanhoof, Koen and Brijs, Tom},
  issn         = {1432-7643},
  journal      = {SOFT COMPUTING},
  keywords     = {Neural networks,Rough sets,Road safety output (RSO),(Composite) index,Road safety performance indicators,Hybrid system,Decision support,FEEDFORWARD NETWORKS,METHODOLOGY,SELECTION},
  language     = {eng},
  location     = {Xiamen, PR China},
  number       = {12},
  pages        = {1255--1263},
  title        = {A hybrid system of neural networks and rough sets for road safety performance indicators},
  url          = {http://dx.doi.org/10.1007/s00500-009-0492-3},
  volume       = {14},
  year         = {2010},
}

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