Abstract
Pedestrians are at a significantly higher risk of suffering serious injuries or death in road traffic incidents. In 2021 alone there were 7,388 pedestrian fatalities and 60,577 injuries. Addressing this critical issue, our study introduced a novel methodology for predicting the severity of pedestrian crashes. This method leveraged advanced deep learning models such as Inception-ResNet-v2, Inception-v3, and Xception applied to synthetic data generated by Generative Adversarial Networks (GANs). This study analyzed data related to pedestrian crash severity in Louisiana spanning five years (2016–2021), encompassing forty variables that include pedestrian demographics, accident location, and vehicle specifics. The severity of crashes was categorized into three categories: injury, no injury, and fatal. To combat the challenge of data imbalance, our study implemented a novel method that combined traditional sampling methods with GANs. This integrated methodology facilitated the generation of synthetic data utilizing the Conditional Tabular GAN (CTGAN) model and the attainment of balanced datasets by employing under-sampling via the Random Under Sampler (RUS) technique and over-sampling through the Synthetic Minority Over-sampling Technique (SMOTE). Thereafter, the DeepInsight technique was employed to transform numerical and categorical crash data into image format, making it compatible with the deep learning models utilized. The findings reveal that the models demonstrated improved predictive capabilities when applied to the over sampled dataset, which was achieved by increasing the number of instances in the minority class to balance the distribution of classes, as evidenced by various performance metrics including accuracy, precision, recall, and F1 score. Specifically, the Inception-ResNet-v2, Inception-v3, and Xception models recorded predictive accuracies of 82.73%, 84.75%, and 69.07% respectively, with the over sampled dataset, which was the highest among the three sampling categories of data considering all the metrics. The insights derived from this research have practical applications for urban planners, city engineers, safety professionals, transportation authorities, emergency service providers, vehicle manufacturers, and traffic management centers.