Practical Applications and Systemic Bottlenecks of Intelligent Transportation Systems in Urban Traffic Management: A Case-Based Analysis
The sharp increase in urban vehicle ownership has triggered severe traffic congestion and low road network operational efficiency, making intelligent transportation systems (ITS) a core technical means to optimize urban traffic management. Nevertheless, existing studies have obvious deficiencies: there is a lack of systematic collation and comparative analysis of ITS practical applications across diverse urban scenarios, and the exploration of developmental constraints is fragmented without integrating inherent correlations among industrial standards, equipment compatibility and operational costs. This paper adopts literature research, case analysis and inductive methods to select typical urban cases in Indonesia, Poland and Malaysia, exploring the practical effects of ITS on signal timing optimization, short-term traffic flow prediction and multi-intersection collaborative control under traffic disturbances. The results indicate that simulation modeling, regression algorithms and deep reinforcement learning can effectively reduce traffic queue length and travel delay, and improve traffic prediction accuracy and road network operation efficiency. This study further identifies three systemic bottlenecks restricting ITS large-scale promotion: poor adaptability to extreme weather and unexpected traffic incidents, insufficient standardization and scalability for city-wide deployment, as well as data privacy risks and user psychological resistance. It also clarifies research limitations and proposes future research directions, aiming to provide theoretical support and practical references for urban intelligent traffic governance and bottleneck breakthroughs.