Rule extraction from an optimized neural network for traffic crash frequency modeling |
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| Affiliation: | 1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, PR China;2. Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China;3. Department of Automation, Tsinghua University, Beijing, PR China;4. Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;5. Business School of Hunan University, Changsha, Hunan 410082, PR China;1. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA;2. School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;3. Quality Assurance and Transportation System Safety, Washington State Department of Transportation, Seattle, WA 98101, USA;4. Geospatial and Population Studies Traffic Research Unit, University of New Mexico, Albuquerque, NM 87106, USA;1. Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Korea;2. Graduate School of Convergence Science and Technology, Seoul National University, 145 Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 443-270, South Korea |
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| Abstract: | 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. |
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| Keywords: | Crash frequency Neural network Over-fitting Structure optimization Rule extraction |
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