Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique.

ABSTRACT: Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques

SUBMITTER: Aboulola OI 

PROVIDER: S-EPMC11003624 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature