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

Kuşkapan E., Çodur M. Y., ATALAY A.

ACCIDENT ANALYSIS AND PREVENTION, cilt.155, 2021 (SSCI) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 155
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.aap.2021.106098
  • Dergi Adı: ACCIDENT ANALYSIS AND PREVENTION
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, PASCAL, Abstracts in Social Gerontology, Aerospace Database, BIOSIS, Business Source Elite, Business Source Premier, Communication Abstracts, EMBASE, Environment Index, MEDLINE, Metadex, Psycinfo, Civil Engineering Abstracts
  • Anahtar Kelimeler: Heavy vehicles, Speed violation, Machine learning, Spatial analysis, EXPRESSWAY, TUTORIAL
  • Atatürk Üniversitesi Adresli: Evet

Özet

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 knearest 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 Mug?la. 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.