Advancement of weather-related crash prediction model using nonparametric machine learning algorithms

Abstract

This paper evaluates the machine learning-based weather-related crash prediction model in Connecticut. Crash severity prediction has always been the principal focus of safety professionals and emergency responders for appropriate policy making and resource management. Over the years, different statistical methodologies (e.g., random forest, support vector machine) have been explored in various research efforts to develop efficient crash severity prediction models. As technology is advancing and computing has started becoming more efficient, machine learning-based models for crash severity prediction are being brought into light for more accurate data-driven prediction. However, some machine learning methodologies provide increased efficiency and better performance compared to others from the same genre. To explore different machine learning methodologies for crash severity prediction, this study considered two machine learning applications-random forest (RF) and bayesian additive regression trees (BART) for performance comparison. RF model produced higher prediction probabilities for determining crash severity compare to BART. The results were evaluated using various prediction probability analyses obtained from these two machine learning models and showed the model capabilities to generate prediction consistent with the observed data. We found the performance of the RF model to be highly promising with a higher skill score (0.73) than BART (0.61). Overall, our findings demonstrate the robust performances of the RF algorithm in predicting weather-related crashes. The analysis of this study confirms that stakeholders can use weather-related RF model with confidence to obtain a better prediction of crash severity that enables them to facilitate appropriate emergency responses and support essential preemptive measures.

Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

“…Generally, the machine learning-based model is expected not to capture very low and extremely high values successfully [14,93]. This is because the model accuracy is sensitive to sample size and the data representativeness in the training dataset [95,96]. Therefore, very large sample sizes are required for low and extremely high values to quantify the rate of convergence to the underlying cumulative distribution function.…”

Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison

“…In their study, Amiri et al [35] focused on predicting the severity of fixed object crashes among elderly drivers using ANN models and a hybrid intelligent genetic algorithm. Some studies represented that machine learning techniques have a better performance in improving safety in transport modes, including pedestrians and motorcycle crash severity, compared with ANN models [36][37][38]. Chang and Chien [39] focused on decision trees (DTs) to study crash severity as another data mining technique.…”

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

“…On the other hand, we identified the following methods when analyzing the ML approaches that show the best performance in the studies consulted. RF [56][57][58], SVM [59][60][61], Light-GBM [62], Gradient Boosting [63], AdaBoost [58], Multi-layer perceptron [64], Nearest Neighbor Classification [25], and SimpleCart model [65]. These methods were commonly used for case studies located in the USA (Connecticut, Michigan, California, Florida, Nebraska), United Kingdom, China (Kunshan), Korea (Seoul), India, and Ghana.…”