Driver Acceptance of Advanced Traffic Management Systems: An Integrated TAM-TRI Analysis of M-Flow in Thailand Using Structural Equation Modeling

Engineering notes

Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.

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

This study investigates the determinants of driver acceptance of “M-Flow”, Thailand’s first Advanced Traffic Management solution utilizing Multi-Lane Free Flow (MLFF) technology. While designed to eliminate toll plaza bottlenecks through AI-driven automated billing, the system’s operational efficiency is hindered by a “trust gap” caused by a stringent ten-fold penalty for late payment compliance. By integrating the Technology Readiness Index (TRI 2.0) with the Technology Acceptance Model (TAM), this research explores how psychological readiness dictates the success of smart traffic infrastructures. Data from 485 drivers were analyzed using Structural Equation Modeling (SEM). The results reveal that while technological optimism and innovativeness act as motivators, Insecurity (β = −0.723) emerges as the dominant psychological barrier, directly suppressing the perceived ease of use and triggering behavioral resistance. The findings demonstrate that technical efficiency and diverse payment options alone are insufficient to ensure mass adoption if the regulatory climate fosters financial anxiety. To maximize system throughput, this study recommends that policymakers shift from punitive enforcement to “trust engineering.” By enhancing financial transparency, simplifying the registration-to-payment workflow, and mitigating the “penalty trap” perception, authorities can achieve the psychological seamlessness that is a strict prerequisite for a fully trusted smart transportation infrastructure in Thailand.

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