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Real-time prediction of secondary incident occurrences using vehicle probe data
Institution:1. School of Civil Engineering, The University of Queensland, Australia;2. Faculty of Built Environment and Engineering, Queensland University of Technology, Australia;3. School of ICT, Griffith University, Brisbane, QLD 4111, Australia;1. School of Transportation Engineering, Tongji University, 4800, Cao’an Highway, Shanghai, China;2. School of Public Health, UC Berkeley, Safe Transportation Research and Education Center (SafeTREC), University of California, Berkeley, United States;1. Department of Transportation and Logistics Engineering, Hanyang University at Ansan, 1271, Sa-3 dong, Sangnokgu, Ansan-si, Gyeonggi-do 426-791, Republic of Korea;2. The Ohio State University, Department of Civil, Environmental, and Geodetic Engineering, Department of Electrical and Computer Engineering, Hitchcock Hall 470, 2070 Neil Ave, Columbus, OH 43210, United States;1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China
Abstract:
Keywords:Secondary Incident  Bayesian neural network  Rule extraction  Incident duration  Adjusted boxplot  Clustering
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