TY - JOUR PY - 2014// TI - Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models JO - Transportation research part C: emerging technologies A1 - Xu, Chengcheng A1 - Wang, Weixu A1 - Liu, Pan A1 - Guo, Rui A1 - Li, Zhibin SP - 167 EP - 176 VL - 38 IS - N2 - 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.

LA - en SN - 0968-090X UR - http://dx.doi.org/10.1016/j.trc.2013.11.020 ID - ref1 ER -