Exploration of highway accidents temporal changes using traffic and climate big data

Abstract

Anthropogenic emissions of greenhouse gases accelerate global warming and contribute to further temperature increases. Global warming increases the likelihood of a shift towards more warm days and seasons and fewer cold days and seasons. Additionally, it causes changes in precipitation patterns. In earlier research, as ambient temperatures increase, cognitive performance decreases and the risk of crashing increases. Earlier, crash-frequency models were developed using various methodologies, but time-series crash-frequency prediction studies considering the effects of climate change are scarce. Therefore, the purpose of this study is to identify the correlation between crashes and climate change using big data and to develop crash-frequency models using an econometric model and a deep-learning model. Econometric models use autoregressive-integrated moving average and autoregressive-integrated moving average with exogenous variable that are traditional time-series methodologies. Deep-learning models use long short-term memory. This study approached crash occurrence by comprehensively considering climate change and traffic factors. Also, it differs from earlier studies in detailing the influence of independent variables on crashes. Through the results, the impact of climate change on accidents can be identified and it can contribute as an engineering basis for improving traffic safety.