Erzurum Technical University, Engineering and Architecture Faculty, Erzurum, Turkey.
Ataturk University, Engineering Faculty, Erzurum, Turkey.
Accid Anal Prev. 2021 Jun;155:106098. doi: 10.1016/j.aap.2021.106098. Epub 2021 Apr 7.
With the development of technology in the world, vehicles that reach high speeds are produced. In addition, with the increase of road width and quality, faster and more comfortable transportation can be provided. These developments also increase the speed violation rates of road vehicles. Drivers who violate speed limits can endanger both their own lives and the lives of others. Speed violations, of especially heavy vehicles, involve much greater risks than that of light vehicles. Heavy vehicles can cause more serious losses of lives and property in accidents, compared to the ones caused by light vehicles, as they can carry much more freight or passengers than light vehicles. In this study, data regarding the speed violations committed by heavy vehicles in Turkey, were used. Speed violations were divided into 10 classes according to the intensity of speed violation rates. After this process, all provinces were classified according to support vector machines (SVM), naive bayes (NB) and k-nearest neighbors (KNN) algorithms. When the accuracy values and error scales of all three algorithms are examined, it has been determined that the algorithm that gives the most accurate results is the NB algorithm. Based on the classification of this algorithm, speed violation density maps of types of heavy vehicles in Turkey were created by using spatial analysis. According to the density maps, the provinces with the highest speed violations were identified. In the results, it was determined that the rate of heavy vehicle speed violation was highest in the cities such as Erzurum, Konya, and Muğla. Later, these cities were examined in terms of heavy vehicle mobility. At the end of this study, measures were proposed to reduce these violations in cities where speeding violations are intense. Material and moral damages can be prevented, to a great extent, with the implementation of recommendations of policymakers which can reduce speed violations.
随着世界科技的发展，生产出了速度很高的车辆。此外，随着道路宽度和质量的提高，可以提供更快、更舒适的交通。这些发展也增加了道路车辆超速违规的频率。超速的司机不仅危及自己的生命，还危及他人的生命。与轻型车辆相比，超速违规，尤其是重型车辆的违规行为，涉及到更大的风险。重型车辆在事故中造成的生命和财产损失比轻型车辆严重得多，因为它们比轻型车辆能携带更多的货物或乘客。在这项研究中，使用了土耳其重型车辆超速违规的数据。根据超速违规率的强度，将超速违规分为 10 类。在这个过程之后，根据支持向量机（SVM）、朴素贝叶斯（NB）和 K 最近邻（KNN）算法对所有省份进行分类。当检查所有三种算法的准确率值和误差尺度时，确定给出最准确结果的算法是 NB 算法。基于该算法的分类，使用空间分析创建了土耳其各种重型车辆的超速违规密度图。根据这些密度图，确定了超速违规最多的省份。结果表明，在埃尔祖鲁姆、科尼亚和穆拉等城市，重型车辆超速违规率最高。后来，对这些城市的重型车辆流动性进行了检查。在这项研究的最后，提出了在超速违规严重的城市减少这些违规行为的措施。通过实施可以减少超速违规的政策制定者的建议，可以在很大程度上防止物质和道德损害。
