Autonomous driving paper index
Understanding driver acceptance of red-light cameras in Thailand: testing an integrated HBM-TPB model using structural equation modeling
One-line summary
Background Red-light cameras (RLCs) have demonstrated effectiveness in reducing traffic violations and intersection crashes; however, their successful implementation depends substantially on public cooperation.
Engineering notes
Attitude (β = 0.251, p = 0.002), perceived behavioral control (β = 0.219, p = 0.003), subjective norms (β = 0.154, p = 0.034), and cues to action (β = 0.253, p = 0.020) significantly predicted intention to support RLCs.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Background Red-light cameras (RLCs) have demonstrated effectiveness in reducing traffic violations and intersection crashes; however, their successful implementation depends substantially on public cooperation. In Thailand, despite 68% policy support, inconsistent compliance persists, indicating a discrepancy between acceptance and behavioral outcomes that undermines potential safety benefits. Objective This study aimed to develop and validate an integrated model combining the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB) to identify the determinants of driver acceptance of RLC systems in Thailand. Methods A cross-sectional survey was administered to 485 licensed drivers who regularly commute through RLC-equipped intersections in Thailand. The questionnaire assessed nine constructs: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, attitude toward behavior, subjective norms, perceived behavioral control, and intention to accept. Structural equation modeling (SEM) was conducted using Mplus. Results The structural model confirmed the sequential integration of the HBM and TPB frameworks, explaining 67% of the variance in intention, with health-risk appraisals functioning as cognitive antecedents of planned behavior constructs. Cues to action were the strongest predictor of attitude toward RLC support (β = 0.432, p < 0.001), followed by perceived severity (β = 0.420, p = 0.002). Perceived barriers negatively influenced both attitude (β = −0.236, p = 0.002) and perceived behavioral control (β = −0.360, p = 0.002), suggesting that perceived unfairness, privacy concerns, enforcement uncertainty, and institutional distrust reduce drivers’ favorable evaluations and perceived capacity to comply with RLC systems. Attitude (β = 0.251, p = 0.002), perceived behavioral control (β = 0.219, p = 0.003), subjective norms (β = 0.154, p = 0.034), and cues to action (β = 0.253, p = 0.020) significantly predicted intention to support RLCs. Conclusion Driver acceptance of RLCs is fundamentally contingent upon mitigating perceived barriers and institutional distrust—not solely promoting safety benefits. Perceived barriers were the strongest suppressors of both attitude and perceived behavioral control, indicating that psychological friction must be actively dismantled. Policymakers should therefore prioritize transparent governance, standardized enforcement procedures, and community-led engagement strategies to convert driver resistance into genuine public compliance with automated enforcement systems.
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