Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Abstract

Many agencies use Automated Traffic Signal Performance Measures (ATSPMs), which require vehicle detection and communication equipment, to evaluate traffic signal efficiency. ATSPMs usually rely on projections based on spatially limited vehicle trajectory samples to estimate performance. Recently, connected vehicle (CV) data has become available that provides entire vehicle trajectories that can be used to generate accurate signal performance measures without the need for projections or infrastructure upgrades. This paper analyzes over 50 intersections in Utah to evaluate how traditional detector-based arrivals on green (AOG) calculations compare with their CV-based calculations. The effects that saturation and queue- lengths have on estimations are analyzed. In general, there is close correlation between computations when queues are short and undersaturated conditions exist. However, if queues extend past the advance detector, detector-based calculations tend to overestimate AOG since vehicles are detected during green but may stop before. This impact is particularly large when the approach is oversaturated, which led to overestimations of around 40% in this study. In addition, detector-based estimations can underestimate AOG in undersaturated scenarios with short queues if vehicles reach the advance detector on red but reduce their speed afterwards, allowing them to not stop. In some cases, the detector-based technique underestimated AOG by over 50%. The findings can help practitioners understand how detector-based estimations vary by traffic conditions. This is particularly important as the industry moves toward a hybrid blend of detector- and CV-based signal performance measures. It is recommended that CV trajectories be used to measure AOG during periods with long queues, oversaturated conditions, or both.