Cycle-Level Delay Estimation at Partially Connected Intersections with Dynamic Signal Control

Abstract

Cycle-level delay estimation plays an important role in the performance evaluation for dynamic signal control at intersections. With the popularity of connected vehicles (CVs), CV trajectory data have been investigated in delay estimation. However, CV trajectory-based studies mainly focus on fixed-time signal control, with low CV penetration rates remaining a challenge for dynamic signal control. This study proposes a cycle-by-cycle delay estimation method using CV trajectory data at an isolated intersection with dynamic signal control in undersaturated conditions. Historical CV trajectories within the same time-of-day period across days are transformed into Newellian coordinates and then aggregated to cope with low penetration rates. Vehicle arrival distribution under a Bernoulli process is estimated. The evolution of the queued vehicle number within each cycle is analytically modeled using the stochastic point-queue model. A probabilistic delay estimation model is built for cycle-level delay estimation, making full use of observed trajectory data. To cope with computational burden, the evolution of queued vehicles’ arrival in the red time is approximated based on the deterministic point-queue model. The simulation experiments validate the advantages of the proposed model over benchmarks for estimation accuracy and error variance. The performance of the proposed model remains relatively stable even with low CV penetration rates. The sensitivity analyses show that the proposed model is robust to penetration estimation errors, traffic demand levels, and arrival patterns with undersaturated traffic.