A Study On Indian Toll Management

Toll plazas play a vital role in highway infrastructure by facilitating revenue collection and traffic regulation; however, they frequently act as operational bottlenecks that contribute to congestion, increased travel delay, and environmental impact. Over the past decade, significant research efforts have been directed toward improving toll plaza efficiency through capacity and level of service (LOS) analysis, electronic toll collection (ETC), intelligent transportation systems, and artificial intelligence (AI)-based solutions. Despite these advancements, existing studies largely address isolated aspects of toll plaza operations, such as payment automation, traffic flow evaluation, or vehicle identification, without offering an integrated operational framework. This survey paper presents a comprehensive review of recent research on toll plaza management systems, covering capacity and LOS modeling, RFID- and GPS-based toll collection, AI and machine learning techniques for traffic prediction, automatic number plate recognition (ANPR), and smart traffic management approaches. The surveyed works are systematically classified and compared based on their objectives, methodologies, and technological scope. Through a detailed comparative analysis, this paper identifies critical research gaps, including the lack of real-time adaptability, limited consideration of mixed traffic conditions, insufficient integration of sensing and edge intelligence, and the absence of scalable system-level implementations. The findings of this survey aim to guide future research toward the development of unified, real-time, and intelligent toll plaza management systems.