Empirical Study of a Spatial Analysis for Prone Road Traffic Accident Classification based on MCDM Method

Spatial analysis techniques are widely used as an effective approach for prone road traffic accident classification. This paper will present the results of empirical behavioral testing on the spatial analysis for prone road traffic accident classification using the Multicriteria Decision Making (MCDM) method. The performance of MCDM is compared on arterial and collector road types processed with multicriteria parameters. MCDM was chosen because it can be used as a decision making based on an alternative selection with many criteria. Empirical tests of the MCDM method used include Weighted Sum Model (WSM), Weighted Product (WP), Simple Additive Weighting (SAW), Weighted Product Model (WPM), Multi-Attribute Utility Theory (MAUT), Technique for Others Reference by Similarity to Ideal Solution (TOPSIS), and Analytical Hierarchy Process (AHP). The multicriteria parameter weight values are based on expert judgment and the Fuzzy-AHP method (EJ-AHP), which comprises volume-to-capacity ratio (VCR), international roughness index (IRI), vehicle type, horizontal alignment, vertical alignment, design speed, and shoulder. Then, the performance of the models was compared to determine the value of accuracy, precision, recall, and F1-score as decision-making on the prone road traffic accident classification using Multicriteria Evaluation Techniques (MCE). The empirical test results on arterial roads show that the SAW and TOPSIS methods have the same performance and are superior to other methods, with an accuracy value of 63%. However, the results on the collector road type show that the accuracy value of the AHP method outperforms other methods with an accuracy value of 70%.

@article{Vitianingsih2022,
  title     = {Empirical Study of a Spatial Analysis for Prone Road Traffic Accident Classification based on MCDM Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Anik Vega Vitianingsih and Zahriah Othman and Safiza Suhana Kamal Baharin and Aji Suraji},
  doi       = {10.14569/IJACSA.2022.0130578},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130578}
}