Heuristic Modelling of traffic accident characteristics

Tercan E., BEŞDOK E., Tapkin S.

TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH, vol.13, no.7, pp.522-530, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 7
  • Publication Date: 2021
  • Doi Number: 10.1080/19427867.2020.1734273
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Civil Engineering Abstracts
  • Page Numbers: pp.522-530
  • Keywords: Traffic accident, traffic safety, data clustering, resilient neural networks, evolutionary computation, optimization, differential search algorithm, Calinski-Harabasz index, INJURY SEVERITY, GENETIC ALGORITHM, CRASH-FREQUENCY, ROAD, CLASSIFICATION, RISK, PREDICTION, DRIVER, LEGISLATION, PERFORMANCE
  • Erciyes University Affiliated: Yes


Due to the complex structure of observation based traffic accident data and the absence of an analytic model to define their characteristics, different models are required. Accident characteristics have been modeled for different road segments with two different methods: evolutionary data clustering method and resilient neural networks. In the first method, observation data was clustered using an evolutionary search-based clustering algorithm. The first method is based on determining whether observation based test data have the conditions of a possible death or injury accident based on the distance to the cluster centers obtained. The second one is a regression method that predicts whether an accident will cause death or injury according to observation based traffic data in test road segments by using resilient neural networks. Experiment results show that data analysis methods are very effective in determining the existence of the conditions that may cause accidents resulting in death or injury.