Connected Vehicle Study based on SPaT Data and Vehicle Trajectories Using Real-world Testbed and Simulation Environment along the TH-55 Corridor in Minnesota Assessed Energy and Mobility Impacts.
Minnesota, United States
Cost/Benefit Analysis of Fuel-Efficient Speed Control Using Signal Phasing and Timing (Spat) Data: Evaluation for Future Connected Corridor Deployment
Summary Information
Stopping at signalized intersections during red light phases is a significant contributor to fuel consumption in vehicles. Therefore, Connected Vehicle (CV) technologies anticipating future signal phases create opportunities for vehicles to optimize their speeds on arterial corridors, and preemptively reduce their speeds before a red light, thereby avoiding coming to a full stop, and reducing the fuel consumption needed to re-accelerate. Given this view, this study utilized an entire corridor of Signal Phasing and Timing (SPaT) signals, with ten different CV market penetration rates ranging from ten to 90 percent, focusing on the signalized intersections along the TH-55 Corridor in Minnesota, following data collection efforts from September 2020 to March 2021.
METHODOLOGY
In this study, several test vehicles were instrumented with On-Board Units (OBU) of Dedicated Short-Range Communications (DSRC) receivers and GPS tracking to record SPaT data and the vehicle trajectories together while performing test drives with human drivers along the TH-55 Corridor. Then, vehicle speeds were optimized based on recorded SPaT data, using the recorded vehicle trajectories to identify the constraints of traffic flow. Real world testbed experiments (“living lab” consisting of a car engine) were used to measure the fuel consumption with and without speed control for the test vehicles. Later, the study used traffic flow simulations to study the impacts of higher market penetration on the overall fuel benefits and travel times, including the benefits to legacy vehicles which unintentionally use SPaT based speed controls by following CVs. In the simulation, in addition to individual vehicles, a platoon of vehicles was also considered, assuming different platoons of ten and then 20 vehicles following a lead vehicle, simulated with an Intelligent Driver Model (IDM). Finally, network models were used to predict changes in route choices based on dynamic assignment as drivers recognize the benefits of fuel savings in the route utility, again corresponding to various levels of CV market penetration rates.
FINDINGS
- Testbed results showed that the fuel benefits could be 11.35 percent for the target CV versus a traditional vehicle.
- Testbed results also revealed that a vehicle controlled by IDM following the CV could achieve 8.98 percent fuel benefit compared with the traditional vehicle.
- Simulation results showed that, with co-optimization of vehicle speed and gear position, the target vehicle achieved 12.19 percent benefits on fuel consumption.
- Simulation of the platoon of ten vehicles showed that the fuel benefit of the vehicle platoon increased with the increase of the CV market penetration rate, ranging from three to 13 percent fuel benefits.
- Simulation of the platoon of 20 vehicles showed that the fuel benefit of the vehicle platoon also increased with the increase of the CV market penetration rate, ranging from a median value of three to 11 percent fuel benefits.
- Simulation results also showed that, with a penetration rate varying from 10 to 90 percent, the corresponding reduction in average travel time for legacy vehicles ranged from around one to nearly five percent.
- Results of the dynamic assignment network models showed that, at a 40 percent CV market penetration rate for instance, 4.11 minutes of travel time savings and 38.27 gallons of fuel savings were observed compared to the ‘no CV’ (zero percent penetration) case.
