Equivalent Accident Number (EAN) and Upper Control Limit (UCL) Relationship to Multi-criteria Model Analyzed by Idalin et al. on Several National Roads

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%).