The Development of a Road Safety Management Model Through the Mechanism of the District Road Safety Operation Center to Enhance the Participation of Local Administrative Organizations and Partner Networks: A Case Study of Mueang Surin District, Surin Province

Abstract

     This action research aimed to (1) analyze the context, problems, and gaps in district-level road safety management mechanisms, and (2) develop and evaluate a road safety management model using the District Road Safety Operation Center (DRSOC) mechanism, in terms of driving behavior, attitudes, DRSOC effectiveness, and network partner participation in Mueang Surin District. The study was conducted over one cycle of the Kemmis and McTaggart PAOR cycle over six months. Qualitative data were collected from 15 key informants and focus group discussions using the ORID method, together with quantitative data from target groups before and after the intervention (341 per group; 682 total) and a 40-member action group, analyzed using Independent Samples t-test, Paired t-test, and Two-proportion Z-test.

     Key gaps in the existing mechanism included the lack of an area-based information system, discontinuous operations, resource constraints among local administrative organizations (LAOs), and the absence of integrated coordination among network partners. These findings led to the development of a comprehensive Final Model comprising four components: (1) the DRSOC mechanism as the central coordinating hub linking all sectors, (2) an accident data and information system for decision-making, (3) a participation mechanism led by LAOs at the area level, and (4) a continuous monitoring and evaluation system based on the PAOR cycle, in which LAOs assumed the lead role at the sub-district level while the DRSOC shifted to a policy-support function. Following implementation, road safety behavior and attitude scores increased significantly (p < 0.001), as did DRSOC effectiveness and network participation (p < 0.001). Driving license possession increased significantly (difference 27.0%, p < 0.001), while accidents over a 6-month period (February-July 2025 vs. 2026) decreased significantly (difference 5.1%, p = 0.046).