Virtual Probe Approach for Time-Dependent Arterial Travel Time Estimation

Travel time estimation on arterials is a challenging task for traffic engineers due to the interrupted nature of urban traffic flows. Many research efforts have been devoted on this topic, but their successes are limited and most of them can only be used for offline purposes due to the limited availability of traffic data from signalized intersections. With the advancement of intelligent transportation systems, high-resolution detector and signal status data are available but not fully explored. In this paper, we develop an innovative algorithm for time-dependent arterial travel time estimation by tracing a virtual probe vehicle. At each time step, the virtual probe has three possible maneuvers: acceleration, deceleration and no-speed-change. The maneuver decision is determined by its own status and its surrounding traffic conditions, which can be estimated based on the availability of traffic data at intersections. An interesting property of the proposed model is that travel time estimation errors can be self-corrected with the signal status data, because the differences between a virtual probe vehicle and a real probe can be reduced when both of them meet the red signal phase. A field study at an 11-intersections arterial corridor along France Avenue in Minneapolis, MN shows the proposed model can generate accurate time-dependent travel time under various traffic conditions.