Rule extraction from an optimized neural network for traffic crash frequency modeling

This study develops a neural network (NN) model to explore the nonlinear relationship between crash frequency and risk factors. To eliminate the possibility of over-fitting and to deal with the black-box characteristic, a network structure optimization algorithm and a rule extraction method are proposed. A case study compares the performance of the trained and modified NN models with that of the traditional negative binomial (NB) model for analyzing crash frequency on road segments in Hong Kong. The results indicate that the optimized NNs have somewhat better fitting and predictive performance than the NB models. Moreover, the smaller training/testing errors in the optimized NNs with pruned input and hidden nodes demonstrate the ability of the structure optimization algorithm to identify the insignificant factors and to improve the model generalization capacity. Furthermore, the rule-set extracted from the optimized NN model can reveal the effect of each explanatory variable on the crash frequency under different conditions, and implies the existence of nonlinear relationship between factors and crash frequency. With the structure optimization algorithm and rule extraction method, the modified NN model has great potential for modeling crash frequency, and may be considered as a good alternative for road safety analysis.

• Record URL:
  • https://doi.org/10.1016/j.aap.2016.08.017
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457516303037
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Zeng, Qiang
  • Huang, Helai
  • Pei, Xin
  • Wong, S C
  • Gao, Mingyun
• Publication Date: 2016-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 87-95
• Serial:
  • Accident Analysis & Prevention
  • Volume: 97
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash rates; Highway safety; Neural networks; Traffic crashes
• Geographic Terms: Hong Kong (China)
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01619498
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 21 2016 11:34AM