Speed violation analysis of heavy vehicles on highways using spatial analysis and machine learning algorithms.

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 Mugla. 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.

Public transit usage and air quality index during the COVID-19 lockdown.

The people suffering from coronavirus have to lead unprecedented actions including limiting travel especially using public transportation. Therefore, lockdown measures and social distancing to decelerate the distribution of the COVID-19 has become the new norm. Nevertheless, improvement in the ambient air quality of the cities globally has appeared as a key advantage of this lockdown. There is a lack of research in the field of public transportation mobility and the Air Quality Index (AQI) during the COVID-19 lockdown globally. Consequently, this research aims to examine the overall impact of the public transit usage and ambient air quality, i.e. both AQI and indicatory air pollutants, during the lockdown in 12 countries. Data collections for analysis of public transportation usage and air quality status during the lockdown and one year before this period were carried out utilizing public transportation application Moovit and World's Air Pollution. The results demonstrated that the lockdowns of 12 countries led to dramatically decreased human movements and public transit usage up to -90% until the end of March and it had no major changes until the end of May. In the case of ambient air quality, the average values of AQI in the 12 countries within lockdown 2020 for classes I(AQI:0-50), II(AQI:51-100), and III(AQI:101-150) increased by 12%, 9%, and 13% while for classes IV(AQI:151-200), V(AQI:201-300) and VI(AQI:301-greater) decreased by 10%, 27%, and 3% in comparison with the identical time throughout 2019. The results also indicate that throughout lockdown 2020, in the 12 countries, the percentages of indicatory air pollutants of PM2.5, PM10, SO2, CO, and NO2 were decreased by 16%, 21%, 41%, 48%, and 35% lower than those in the same time in 2019. Mechanism analysis and comparisons highlighted that the lockdowns of 12 countries led to decreased human mobility and improvement in the AQI around the world.