Real-time traffic density estimation using various connected vehicle penetration rates: application of a drone dataset of naturalistic vehicle trajectories

This study introduces an innovative approach, the Predictive Approach, employing the Temporal Convolutional Network (TCN) algorithm to estimate traffic density. We used naturalistic vehicle trajectories captured by drones at a three-way signalized intersection in Athens, Greece, as part of the pNEUMA initiative. This method calculates the densities of input approaches at intersections with non-uniform MPRs, using these predictions to estimate the target approach density. With accuracy ranged from 92% to 95%, using the Predictive Approach showed that improving traffic density predictions can be achieved through factors such as accounting for MPR variations over time and between different intersection approaches while considering practical scenarios. Results also highlighted that excluding Signal Phase and Timing (SPaT) data in certain cases can enhance model performance. It offers practical applications in optimizing traffic flow and reducing congestion in smart cities and traffic control centers, particularly when rapid and real-time computations are required.