Data fusion,ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea |
| |
| Institution: | 1. Department of Civil, Structural and Environmental Engineering, Engineering Statistics and Econometrics Application Research Laboratory, University at Buffalo, The State University of New York, 204B Ketter Hall, Buffalo, NY 14260, United States;2. Department of Civil, Structural and Environmental Engineering, Stephen Still Institute for Sustainable Transportation and Logistics, University at Buffalo, The State University of New York, 241 Ketter Hall, Buffalo, NY 14260, United States;3. College of Engineering, Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620, United States;1. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2500 Campus Road, Honolulu, HI, 96822, United States;2. Center for Urban Transportation Research, University of South Florida, 4202 East Fowler Avenue, CUT100, Tampa, FL, 33620, United States;3. Department of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, 150090, China;4. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2500 Campus Road, Honolulu, HI, 96822, United States;5. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2500 Campus Road, Honolulu, HI, 96822, United States;6. Department of Civil and Environmental Engineering, University of Utah, 110 Central Campus Drive, 2137 MCE, Salt Lake City, UT, 84112, United States;7. Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213-3890, United States;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. 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;1. Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA;2. Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Florida A & M University, Tallahassee, FL 32310, USA;3. School of Engineering, University of North Florida, Jacksonville, FL 32256, USA |
| |
| Abstract: | Increasing amount of road traffic in 1990s has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving environmental factors based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we use various algorithms to improve the accuracy of individual classifiers for two categories of severity of road traffic accident. Individual classifiers used are neural network and decision tree. Mainly three different approaches are applied: classifier fusion based on the Dempster–Shafer algorithm, the Bayesian procedure and logistic model; data ensemble fusion based on arcing and bagging; and clustering based on the k-means algorithm. Our empirical study results indicate that a clustering based classification algorithm works best for road traffic accident classification in Korea. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
