Using Road Characteristics Database to Support Protocol-Specific Track Identification for Vehicle Safety Testing

Abstract

The growing demand for a structured database to support vehicle safety testing necessitates the systematic identification of road tracks that align with ASEAN NCAP protocols. This study aims to develop a Road Characteristics Database underpinned by a standardized data model and data dictionary, addressing a critical gap in harmonizing real-world road environments for testing applications. The methodology followed a structured five-step process. First, a thematic literature review, document analysis, and on-site inspections were
conducted to extract attributes from ASEAN NCAP and other testing frameworks. Second, these attributes were analyzed and validated through pattern recognition and statistical testing, leading to the construction of a harmonized data model aligned with international NCAP standards. Third, survey instruments were designed based on the validated attributes, and in the fourth step, these instruments were applied to real-world road segments, resulting in the development of a data model and confirmation of protocol suitability. Finally, a PHP-based user interface was implemented and linked to a MySQL database, enabling structured data entry, validation, and query functions. The results produced a validated set of 32 road infrastructure attributes, subsequently expanded into a standardized data
dictionary containing 52 harmonized parameters. Seven candidate tracks were identified, supporting up to 25 NCAP test protocol scenarios, with each track accommodating two to five test types depending on its characteristics. Work is ongoing to expand the database to 50–60 tracks. The user interface was tested successfully, with statistical validation confirming strong reliability (average score = 0.85; standard deviation = 0.26). Future enhancements will integrate advanced analytics and intelligent search tools, enabling users to identify road segments by specific testing criteria and thereby improving efficiency, interoperability, and decision-making in vehicle safety assessments.