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首页> 外文会议>2012 international conference on future communication and computer technology >A Comparative Evaluation of Classification Methods in the Prediction of Road Traffic Accident Patterns
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A Comparative Evaluation of Classification Methods in the Prediction of Road Traffic Accident Patterns

机译：道路交通事故模式预测中分类方法的比较评价

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摘要

It is important to study the nature of the associations between road, environmental, and traffic factors and motor vehicle crashes, with the aim to understand the reasons for crashes and to better predict their occurrence, hi this research work we evaluate the performance of several kinds of decision tree algorithms viz. C4.5, ID3, REPTree, Random Tree, Decision Stump, J48 and QUEST, for predicting road traffic accident patterns. For the study the road accident training dataset of Great Britain is obtained from the STATS 19 data collection system, maintained by the government of United Kingdom (UK). The experimental results show that when various decision trees are applied in predicting the road traffic accident patterns C4.5 Tree is the best only in terms of accuracy, Decision Stump is the best only considering speed, Random Tree is the optimal choice considering both accuracy and speed. The results have been evaluated using the accuracy measures such as Recall and Precision.
机译：重要的是研究道路，环境和交通因素与机动车碰撞之间的关联性，以了解碰撞的原因并更好地预测碰撞的发生。在这项研究中，我们评估了几种性能决策树算法C4.5，ID3，REPTree，随机树，决策树桩，J48和QUEST，用于预测道路交通事故模式。为了进行研究，从英国政府维护的STATS 19数据收集系统中获取了英国的道路事故训练数据集。实验结果表明，在将各种决策树应用于道路交通事故模式的预测中，C4.5树仅在准确性方面是最佳的，决策树桩仅在考虑速度的情况下是最佳的，随机树是同时考虑准确性和精度的最佳选择。速度。已使用诸如“查全率”和“精度”之类的准确性度量来评估结果。

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