Computing Arterial Travel Time Distributions From Loop Detector and Probe Datasets

Estimating Arterial Travel Time distributions from high-resolution loop detector data (signal events and vehicle detection events) is a challenging task. Even though high-resolution loop detector data for several years may be available, the lack of other data modes and ground-truth labels, hinders approaches that rely on additional information. Among the approaches relying exclusively on loop detector data, are deterministic physics-based approaches (which use the mechanics of a virtual probe moving down a signalized arterial) and cost-minimization approaches (which estimate travel times from detector counts directly). In this work, we propose and evaluate a hybrid model that uses virtual probe trajectories to indicate probable arrival and departure windows, within which we apply a sequence alignment algorithm on high-resolution loop detector data to match platoons. We generate a broad range of traffic conditions and signal timing plans using SUMO traffic simulator and evaluate our approaches. We also verify our approach using real-world data collected along a 5-intersection signalized corridor. We show that virtual probe trajectories can be replaced by data collected from real probes (when available), thereby improving accuracy. Results show that our approaches enable us to calculate a good estimate of the arterial travel time distribution and are robust to noise. Thus, our methods can be used both with standalone archived loop detector data and in conjunction with data from connected vehicles.