Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models |
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| Institution: | 1. Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China;2. Department of Civil and Environmental Engineering, University of South Florida, 4202 E. Fowler Ave., Tampa, FL 33620, United States;1. Department of Computational Science & Engineering, North Carolina Agricultural & Technical State University, United States;2. Department of Civil & Environmental Engineering, University of Maryland, College Park, United States;3. Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst, United States;4. Department of Civil & Environmental Engineering, University of Massachusetts, Amherst, United States |
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| Abstract: | This study aimed to improve the spatial and temporal transferability of the real-time crash risk prediction models by using the Bayesian updating approach. Data from California’s I-880N freeway in 2002 and 2009 and the I-5N freeway in 2009 were used. The crash risk models for these three datasets are quite different from each other. The model parameters do not remain stable over time or space. The transferability evaluation results show that the crash risk models cannot be directly transferred across time and space. The updating results indicate that the Bayesian updating approach is effective in improving both spatial and temporal transferability even when new data are limited. The predictive performance of the updated model increases with an increase in the sample size of the new data. In addition, when limited new data are available, updating an existing model is better than developing a model using the limited new data. |
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| Keywords: | Real-time crash risk assessment Spatial and temporal transferability Bayesian updating Freeway Safety |
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