Citation

Yehia A, Wang X, Feng M, Yang X, Gong J, Zhu Z. Accid. Anal. Prev. 2021; ePub: ePub.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.aap.2021.106193

PMID

unavailable

Abstract

Safety performance functions (SPFs) are indispensable analytical tools that usually play a crucial role in estimating crash frequencies, identifying hotspots, analyzing crash contributing factors, and assessing the effectiveness of safety countermeasures. Due to the limited availability of safety data, municipalities tended to adopt SPFs from Highway Safety Manual or other neighboring jurisdictions. Recently, boosting algorithms have been frequently exploited for data analysis and statistical regression modeling problems. This research, therefore, aims to examine the efficiency of boosting calibration techniques to transfer the SPF using the limited region data in an international context. To this end, AdaBoost.R2, an adaptive boosting algorithm, Two-stage TrAdaBoost.R2, an instance-based transfer learning algorithm, and Gradient Boosting algorithm were employed to investigate their efficiencies in acquiring knowledge from the available source domain data to predict crashes in the target domain. As a comparison, the calibration factor method was adopted to transfer the traditional negative binomial (NB) regression model. Two training dataset groups were developed to train the four calibration techniques. The first group was used to examine the adaptability of the employed calibration techniques to the limited target region data. While the second group was utilized to further investigate the influence of larger vital information on the performance of transferred models. This study was conducted between two U.S. states, Florida and New York, and two Chinese cities, Shanghai and Suzhou. According to the goodness-of-fit results, boosting calibration techniques showed better prediction accuracy than the calibrated NB-based model using the limited target region data. In addition, the amount and distribution of the training dataset were considered the two significant factors that influence the proficiency of the boosting calibration techniques.

Language: en

Keywords

Boosting technique; Calibration factor; Limited available data; Model transferability; Safety performance function