User Intention on Using Multi Lane Free Flow System of Toll Gates in Indonesia

Multi-Lane Free Flow (MLFF) system aims to resolve vehicle congestion at toll gates., impacting traffic flow., fuel consumption., and air pollution. This study addresses challenges in MLFF adoption., including infrastructure readiness and user acceptance. A quantitative approach was utilized., with purposive sampling targeting 219 toll road users in Jakarta., known for its high toll gate traffic. The model examined relationships among seven variables: Perceived Behavioral Control., Initial Trust., Perceived Safety Risk., Attitude Toward Using., Cost., Performance Expectancy., and Behavioral Intention to Use. Data were collected through structured questionnaires and analyzed using SmartPLS4. Results show significant positive impacts of perceived ease of use., usefulness., and safety on user acceptance. To enhance MLFF adoption., this study integrates AI-driven solutions., including traffic prediction (neural networks)., fraud detection (anomaly detection)., and behavioral analysis (clustering). These technologies improve the efficiency and reliability of MLFF. Additionally., expanding the dataset to other regions enhances generalizability. The findings highlight the importance of collaboration among stakeholders., smart city integration., and AI-driven strategies for overcoming barriers. Public awareness campaigns., policy reforms., and infrastructure enhancements remain crucial for ensuring successful MLFF implementation.