Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering

摘要

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.