Many people lose their lives in road accidents because they do not receive timely treatment after the accident from emergency medical services; providing timely emergency services can decrease the fatality rate as well as the severity of accidents. In this study, we predicted the severity of car accidents for use by trauma centers and hospitals for emergency response management. The predictions of our model could be used to decide whether an ambulance unit should be dispatched to the crash site or not. This study used histogram-based gradient boosting (HistGBDT), a modification of the gradient boosting (GBDT) classifier that accelerates the learning process and increases a model’s prediction power. The HistGBDT model was compared with seven state-of-the-art machine learning models: logistic regression, multilayer perceptron, random forest, extremely randomized trees, bagging, AdaBoost, and GBDT. The experiments were conducted on French accident data from 2005 to 2018. The HistGBDT model, with an overall accuracy of 82.5%, recall of 76.7%, and precision of 81.9%, outperformed other models. An analysis of feature importance indicated that safety equipment was the most important feature and vehicle category, department, localization, and region were other significant features. The Fβ measure (i.e., the weighted harmonic mean of recall and precision) was optimized with different weights on recall for the four best performing models to compare the tradeoff between the two crucial performance measures.
The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.
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