State Study Assessed Benefits to Vehicular Delays from the Deployment of LiDAR Pedestrian Detection and Signal Optimization Algorithm.
The goals of this study were to extend the scope of FDOT's Real-time Intersection Optimizer (RIO) to:
- incorporate pedestrians
- investigate how connected and automated vehicle (CAV) communications could enhance the safety and efficiency of CAVs at intersections
- use RIO for real-time anonymous tracking of vehicles and pedestrians; and
- implement a new optimization algorithm and evaluate its performance.
The research team designed and deployed a LiDAR-based pedestrian detection system into RIO, as well as pedestrian phasing capabilities. A simulated intersection which was modeled based on a real intersection in Gainesville, Florida was developed to evaluate how vehicle and pedestrian delays were impacted by RIO’s upgraded SigPT solver under different demand patterns for both pedestrians and vehicles when compared to an actuated signal. Field tests were also conducted at FDOT’s Traffic Engineering and Research Laboratory (TERL) to evaluate the hardware and software system.
For the simulation case study, the average network delay improvement (the actuated delay minus the RIO Signal Solver delay) was assessed for 12 scenarios with four demand patterns, each at various CAV penetration rates (zero, 25, 50, 75, and 100 percent).
Under each scenario, the vehicle delay difference, pedestrian delay difference, and user delay difference were all calculated. For the field tests, various scenarios were conducted in TERL, where different numbers of vehicles and pedestrians were detected and served when they approached the signalized intersection under different circumstances to verify and evaluate the modifications made to the system.
- SigPT solver decreased vehicle delay with relatively little impact to pedestrian delay. Vehicle delay was reduced by between 7.7 seconds and 14.1 seconds while increasing pedestrian delay between 1.6 and 3.9 seconds. Under all scenarios, the largest improvement to vehicular delay occurred when vehicular volume was half of the pedestrian volume.
- Pedestrians incurred less delay (0.9 to 1.9 seconds) in scenarios with simultaneously high pedestrian demand and low vehicle demand. These scenarios were less affected by RIO Signal Solver optimization as the CAV penetration rate increased.
- RIO could successfully provide trajectory suggestions for all network vehicles regardless of sensing errors that introduced false positive vehicle tracks.