The development of Rwanda was accompanied by rapid growth of various modes of transport and the occurrence of road traffic crashes (RTCs). RTC severity has also increased in Rwanda, with the number of fatalities increasing progressively every year. The aim of this study was to compare the classification performance of eight supervised machine learning (ML) algorithms in order to determine the best one to predict crash severity and identify potential RTC-influencing factors in Rwanda. Quantitative data sets of RTCs, numbers of registered vehicles and annual average daily traffic (AADT) from 2010 to 2022 were used. The ML algorithms examined were logistic regression (LR), support vector machine (SVM), naive Bayes (NB), K-nearest neighbour (KNN), random forest (RF), decision table (DT), lazy Bayesian rules (LBR) and J48. Five algorithms (RF, DT, J48, LBR and KNN classifiers) were found to have an accuracy of more than 80%. The RF classifier was found to have the best performance for predicting crash severity in Rwanda, with an accuracy of more than 97%. The most influential factors were identified as AADT, number of registered vehicles, causes of crashes and the type of vehicles involved. The model results can be used to provide useful information to road safety decision makers during the planning and design of road infrastructure.

中文翻译：

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卢旺达道路交通事故预测模型的监督机器学习算法比较

卢旺达的发展伴随着各种交通方式的快速增长和道路交通事故（RTC）的发生。卢旺达的 RTC 严重程度也有所增加，死亡人数逐年增加。本研究的目的是比较八种监督机器学习 (ML) 算法的分类性能，以确定预测碰撞严重程度的最佳算法并确定卢旺达潜在的 RTC 影响因素。使用了2010年至2022年RTC、注册车辆数量和年平均日交通量（AADT）的定量数据集。检查的 ML 算法包括逻辑回归 (LR)、支持向量机 (SVM)、朴素贝叶斯 (NB)、K最近邻 (KNN)、随机森林 (RF)、决策表 (DT)、惰性贝叶斯规则 (LBR)和J48。五种算法（RF、DT、J48、LBR 和 KNN 分类器）被发现具有超过 80% 的准确率。研究发现，RF 分类器在卢旺达预测碰撞严重程度方面具有最佳性能，准确率超过 97%。最有影响力的因素被确定为AADT、注册车辆数量、事故原因和涉及车辆类型。模型结果可在道路基础设施规划和设计过程中为道路安全决策者提供有用的信息。