Analysing Factors Influencing Readiness of Adoption of AI Driven IOT based Traffic Light for Traffic Management

Traffic congestion remains a persistent challenge in major Indonesian cities, largely due to static traffic light systems that fail to respond to real-time road conditions. This study aims to analyze the factors influencing the readiness of road users in Indonesia to adopt an AI-driven IoT-based traffic light system as a smarter alternative for traffic management. The research was conducted from April to May, targeting Indonesian road users who hold a valid driving license, with data collected via Google Forms using purposive sampling. Respondents were drawn primarily from the Jabodetabek region. This study developed a structural model grounded in the Technology Acceptance Model (TAM), incorporating seven constructs: Perceived Ease of Use, Perceived Security, Compatibility, Trust in AI, Perceived Usefulness, Attitude Toward Adoption, and Behavioral Intention to Accept. A total of ten hypotheses were formulated and tested using Structural Equation Modeling with Partial Least Squares (SEM-PLS) through SmartPLS. The results demonstrate that all ten hypotheses are supported, indicating that each proposed relationship among the constructs is statistically significant. Attitude Toward Adoption emerged as the strongest direct predictor of Behavioral Intention to Accept, followed by Perceived Usefulness. Trust in AI plays a critical mediating role in shaping users’ attitudes, and is most strongly influenced by Perceived Security. These findings suggest that successful adoption of AI-driven IoT traffic lights requires simultaneous attention to ease of use, security assurance, system compatibility, and public trust in artificial intelligence.