Optimized feature selection approaches for accident classification to enhance road safety - Sobhana, Mummaneni; Venkatesh Mendu, Gnana Siva Sai; Vemulapalli, Nihitha; Kumar Chintakayala, Kushal (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
In the modern era, the issue of road accidents has become an increasingly critical global concern, requiring urgent attention and innovative solutions. This investigation has compiled an extensive dataset of 10,356 accident occurrences that occurred between the years 2018 and 2022 in Ernakulam district. By utilizing advanced feature selection methodologies, such as genetic algorithm and coyote optimization, this research has identified pivotal accident determinants. The study harnesses the potential of deep learning techniques, encompassing recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and multilayer perceptron (MLP) for classifying accidents according to severity (categorized as fatal, grievous, and severe). Eight predictive models are trained using the dataset, and the top two are ensembled. Integrating deep learning and optimization strategies, this research aims to create a robust accident classification system. The system will help in developing proactive policies that can reduce the frequency and severity of accidents in Ernakulam district.