Real-time Estimation of O-d Matrices with Partial Trajectories from Etc Tag Data

The origin-destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle re-identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, due to incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. We develop a statistically sound, unbiased estimator for O-D estimation, based on the method of moments. The algorithm performs very well under simulation, compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces realistic time series of the hourly O-D matrix.