Developing Data-Driven Approaches for Traffic Density Estimation Using Connected Vehicle Data

This paper introduces novel approaches for the estimation of the traffic stream density. First, an artificial neural network (ANN) data-driven approach is developed to estimate the level of market penetration (LMP) of connected vehicles at two fixed locations. Then, the estimated values are used as inputs to a Kalman filter (KF) approach to estimate the vehicle count between these two locations. Second, three data-driven approaches are developed to directly estimate the vehicle count using only connected vehicle data, an ANN, a k-nearest neighbor (k-NN), and a random forest (RF). A congested signalized roadway in downtown Blacksburg, Virginia, is used to test and compare the performance of the estimation approaches. Results demonstrate that the ANN approach produces reasonable errors in estimating the LMPs; however, integrating the ANN with the KF results in larger errors than the errors produced from using the KF with a predefined fixed average value obtained from historical data. The results also demonstrate that the data-driven approaches provide accurate vehicle count estimates, with the ANN being the most accurate of the three approaches. Lastly, the paper compares the three developed data-driven approaches with model-driven approaches (i.e., KF), showing that the ANN outperforms all other approaches. However, taking into consideration that the difference is not large, the computational time needed to train the ANN, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is recommended in the estimation of traffic stream density due to its simplicity and applicability in the field.