Arterial Signal Timing Based on Probe Vehicle Trajectories Under Cyclic Stochastic Demand

As an emerging data source, the trajectories of probe vehicles can compensate for the deficiencies of high maintenance costs and low coverage ranges of infrastructure-based detectors (e.g., loop detectors). However, existing arterial signal coordination studies typically assume high-penetration-rate trajectories, which are difficult to achieve in reality. Utilizing low-penetration-rate vehicle trajectories for arterial signal timing with cyclic stochastic traffic demand remains a significant challenge. To address this issue, this study developed a nonlinear optimization model for arterial signal coordination that is applicable to low-penetration-rate vehicle trajectories. Offsets and green splits were optimized to minimize the average delay of probe vehicles on both major and minor roads. Probe vehicle trajectories across cycles were aggregated into one cycle to compensate for the low penetration rate of the trajectory data. The concepts and estimation of the sampled arrival pattern, sampled departure pattern, and transition period were proposed to capture the spatiotemporal progression of probe vehicles along the arterial with varying signal timings. A genetic algorithm (GA)-based solution algorithm was designed to solve the proposed model. Simulation studies validated the advantages of the proposed model over the models in Synchro Studio, MULTIBAND, and the simplified model without considering the transition period. The sensitivity analysis showed that: 1) number of sampled trajectories matters instead of the penetration rate; 2) required number of sampled trajectories increases approximately linearly with the number of intersections and the demand factor; and 3) proposed model is robust to the sampling interval that is no longer than 7 s.