EAN and UCL Relationship to Multi-criteria Model Analyzed by Idalin et al on Several National Roads


  • Muhammad Arif Arofah Master of Civil Engineering Program, Faculty of Civil Engineering and Planning, Universitas Trisakti
  • Budi Hartanto Susilo Professor, Department of Civil Engineering, Faculty of Engineering, Universitas Kristen Maranatha




Road Safety Audit (AKJ), Equivalent Accident Number (EAN), National Roads, Accident-Prone Location (LRK), Multi Criteria Analysis, Accident Prediction, Probability, Upper Control Limit (UCL)


In Indonesia, to determine whether a road section is a crash-prone location (LRK), accident data from the local police is required, meaning that an accident needs to occur first. The Idalin et al (2018) method is a road safety audit (AKJ) method with a multi-criteria analysis model to calculate the probability (%) without using accident data, so that it can predict accidents based on 24 road geometric parameters and traffic characteristics. The purpose of the study was to determine the correlation of the Equivalent Accident Number (EAN) and Upper Control Limit (UCL) values to the Idalin et al (2018) Method. This research was conducted on 4 national roads with different characteristics in the form of function, class, area type and road terrain slope. The results of the study are that the greater the probability value, the greater the EAN and UCL values with a high positive correlation (r2> 0.82), and the weight of urban road accidents is influenced by traffic attributes (26-43%) and inter-city roads are influenced by cross-sectional conditions (23-28%).


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How to Cite

Arofah, M. A., & Susilo, B. H. (2024). EAN and UCL Relationship to Multi-criteria Model Analyzed by Idalin et al on Several National Roads. Gema Wiralodra, 15(1), 334–349. https://doi.org/10.31943/gw.v15i1.657