Application of Rescaled Adjusted Partial Sums (RAPS) Method in Validation of Traffic Management Risk Analysis Data in Large Cities in Indonesia - Oktaviani, Oktaviani; Purnawan, Purnawan; Yossyafra, Yossyafra; Adji, Bayu Martanto (Universitas Negeri Padang, 2025)
This study examines the application of the Rescaled Adjusted Partial Sums (RAPS) method in validating traffic management risk analysis data in the category of large cities in Indonesia. The RAPS method is used to test the consistency and reliability of data that is the basis for traffic management risk analysis, so that it can ensure the validity of the risk assessment results. Data validation is very crucial in traffic management risk analysis to ensure the accuracy and reliability of the analysis results that are the basis for decision making. In order for the implementation of traffic management to be in accordance with the goals and objectives, traffic management analysis is needed to minimize the risk of failure of the implementation. Traffic management risk analysis in this study uses the Failure Modes, Effects and Criticality Analysis (FMECA) method. The data used are based on the results of filling out questionnaires from respondents, in the form of an assessment of three components, namely severity, occurrence, and detection for four indicators (road capacity, side obstacles, traffic conflicts, and traffic signs/markings) which have sub-indicators of the existing negative impacts. The data were validated to see the consistency of the data provided by respondents using the RAPS method. The results of the validation test using the RAPS method show that the data from the assessment of three components for four indicators that have sub-indicators of negative impacts are valid. And the test results show that the RAPS method is effective in identifying consistent and inconsistent data, thereby improving the quality of risk analysis data validation. With better validation, traffic management risk analysis can be carried out more precisely and efficiently.