Abstract
This article explores models in Intelligent Transportation Systems for real-time traffic flow manageability, focusing on decision-making processes. It covers forecasting, planning, implementing, and controlling strategies to manage traffic flow and ease congestion. Traffic flow prediction models, like dynamic route guidance and traffic flow prediction, utilize historical data and real-time inputs for proactive decision-making. Traffic flow planning models, such as dynamic route guidance index and route efficiency factor, aid in route selection and signal timing optimization. In order to streamline the boundless complexity, the authors assume that it is effective to delineate the managerial capacity paradigm of intelligent transportation systems into the two separate scenarios of “stable and known situation” and “unstable and with large uncertainty situation”. The article proposes a hypothesis to improve the decision-making process in traffic flow. The distinction between these two situations is essential for the smooth running of the business and requires a thorough understanding of the traffic flow in real time, making decisions in intelligent transport systems in order to direct the traffic. The article focuses on data-driven decisions for smoother traffic flow.
Published Version
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