Research on Algorithm Driven Path Planning in Intelligent Transportation System

This paper investigates algorithm-driven path planning within Intelligent Transportation Systems (ITS). It focuses on the application of advanced computational techniques to enhance route optimization and traffic management. In the paper, we integrate real-time traffic data into traditional path-finding algorithms like A * and Dijkstra to address dynamic traffic conditions. The approach we use is a hybrid model that combines machine learning techniques for traffic prediction with algorithmic adjustments to enhance responsiveness and accuracy in routing decisions. We evaluate our models through simulations in diverse traffic scenarios to demonstrate significant improvements in computational efficiency and path optimality. This paper lays the groundwork for ITS solutions that leverage real-time data. It provides more reliable and efficient routing, illustrating the benefits of integrating predictive analytics into traditional transportation models.