Improving the transferability of the crash prediction model using the TrAdaBoost.R2 algorithm.

Improving the transferability of the crash prediction model using the TrAdaBoost.R2 algorithm. Accid Anal Prev. 2020 Apr 23;141:105551 Authors: Tang D, Yang X, Wang X Abstract The crash prediction model is a useful tool for traffic administrators to identify significant risk factors, estimate crash frequency, and screen hazardous locations, but some jurisdictions interested in traffic safety analysis can collect only limited or low-quality data. Existing crash prediction models can be transferred if calibrated, but the current aggregate calibration method limits prediction accuracy and the disaggregate method is resource-consuming. Transfer learning is another approach to calibration that acquires knowledge from old data domains to solve problems in new data domains. An instance-based transfer learning technique, TrAdaBoost.R2, is adopted in this study since it meets the requirement of site-based crash prediction model transfer. TrAdaBoost.R2 was compared with AdaBoost.R2 using a simply pooled data set to examine the efficiency in extracting knowledge from a spatially outdated source data domain (old data domain). The target data domain (new data domain) was sampled to test the technique's adaptability to small sample size. The calibration factor method based on a negative binomial model was employed to compare its predictive performance with that of the transfer learning technique. Mean square error was calculated to evaluate the pr...
Source: Accident; Analysis and Prevention. - Category: Accident Prevention Authors: Tags: Accid Anal Prev Source Type: research