Unexpected events such as crashes, disabled vehicles, flat tires and spilled loads cause traffic congestion or extend the duration of the traffic congestion on the roadways. It is possible to reduce the effects of such incidents by implementing intelligent transportation systems solutions that require the estimation of the incident duration to identify well-fitted strategies. This paper presents a methodology to establish incident duration estimation models by utilizing decision tree models of CHAID, CART, C4.5 and LMT. For this study, the data contained traffic incidents that occurred on the Istanbul Trans European Motorway were obtained and separated into three groups according to duration by utilizing some studies about classification of traffic incidents. By using classified data, decision tree models of CHAID, CART, C4.5 and LMT were established and validated to estimate the incident duration. According to the results, although the models used different variables, the decision tree models of CHAID, CART and C4.5 have nearly the same prediction accuracy which is approximately 74%. On the other hand, the prediction accuracy of decision tree model of LMT is 75.4% which is somewhat better than the others. However, C4.5 model required less number of parameters than the others, while its accuracy is the same with others.

意外事件，例如撞车，伤残车辆，轮胎漏气和溢出的货物，会导致交通拥堵或延长道路上交通拥堵的持续时间。通过实施智能交通系统解决方案，可以减少此类事故的影响，这些解决方案需要估算事故持续时间，以找出适合的策略。本文提出了一种利用CHAID，CART，C4.5和LMT的决策树模型建立事件持续时间估计模型的方法。对于本研究，通过利用一些关于交通事故分类的研究，获得了包含在伊斯坦布尔跨欧洲高速公路上发生的交通事故的数据，并根据持续时间将其分为三类。通过使用分类数据，CHAID，CART，C4的决策树模型。建立并验证了图5和LMT以估计事件持续时间。根据结果​​，尽管模型使用了不同的变量，但是CHAID，CART和C4.5的决策树模型几乎具有相同的预测精度，约为74％。另一方面，LMT的决策树模型的预测精度为75.4％，比其他方法要好一些。但是，C4.5模型所需的参数数量比其他模型少，而其准确性与其他模型相同。4％，比其他人要好一些。但是，C4.5模型所需的参数数量比其他模型少，而其准确性与其他模型相同。4％，比其他人要好一些。但是，C4.5模型所需的参数数量比其他模型少，而其准确性与其他模型相同。