Dynamic Origin–Destination Flow Estimation Using Cellular Communication System

This paper aims to develop a new approach for the estimation of dynamic origin-destination (O-D) flow using cell phones as traffic probes. The state-space model, which depends on the autoregressive dynamics of O-D flow and the time series for link volume counts, was adopted. Unlike a direct approach that uses sample O-D flows extracted from the cell-based location data as additional observations, an indirect approach is proposed wherein the assignment map in the model is derived from the passing time at observation locations and the path choice proportion. A probe phone's passing time at a certain point in a cell was approximated with its entry and exit times at cell boundaries. The average path choice proportion was also estimated using cell-based trajectories of probe phones. The simulation experiments confirmed that the approach was successfully applicable to the real-world freeway network. The results suggest that the O-D flows estimated from the present approach are promising in that the mean absolute error ratio was smaller than the case wherein only historical O-D flows are concerned. A sensitivity analysis revealed that the present approach met the requirements for an urban area encompassing a huge number of cell phone users in a microcell system.