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Signal Control Priority and Alternative Intersections in Connected Vehicle Environments
(April 22, 2021)

Development and Evaluation of SCP in CV Environment
Presenter: Zorica Cvijovic, M.S.
Presenter’s Org: Department of Civil and Architectural Engineering, University of Wyoming

T3 and T3e webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation (USDOT)’s ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.

[The slides in this presentation contain the logo of the University of Wyoming.]

Slide 1: Development and Evaluation of SCP in CV Environment

Zorica Cvijovic, M.S.

[This slide contains two images: (1) logo of the University of Wyoming’s College of Engineering and Applied Science and (2) USDOT triskelion.]

Slide 2: Outline

  • Connected Vehicles
  • Signal Control Priority
  • CV in VISSIM microsimulation
  • CV-SCP algorithms
  • SCP Strategies
  • Test Cases
  • Results and Conclusions

[This slide contains a diagram of a multi-lane road running top to bottom with three smaller roads intersecting it perpendicularly.]

Slide 3: Connected Vehicle Applications

  • Connected Vehicles (CV) technologies enable safe and interoperable wireless communication between:
    • vehicle-to-vehicle (V2V)
    • vehicle-to-infrastructure (V2I)
    • vehicle-to-device or vice versa (V2D/D2V)
  • Great potential to enhance safety, operations, and mobility
  • Close to 50 developed applications
  • UW’s research focused on:
    • Intelligent Traffic Signal System (ISIG)
    • Queue Warning (Q-ARN)
    • Dynamic Speed Harmonization (SPD-HARM)
    • Signal Control Priority (SCP)

[This slide contains a car with concentric circles around it and arrows indicating the direction the car is moving.]

Slide 4: Signal Control Priority (SCP)

  • Traffic signal operational strategy
  • Makes transit system more reliable
  • Transit or freight vehicles
  • Implemented as passive, active, or adaptive
  • SCP strategies:
    • green extension (GE)
    • early green (GE)
    • phase rotation (PH)

[This slide contains six diagrams of the same intersection, each showing a large vehicle in a different position in relation to the intersection.]

Slide 5: SCP Communication

Signal Control Priority in CV environment needs:

  • BSM (Basic Safety Message)
  • RSU (Road Side Unit)
  • OBE (On-board Equipment)

Between OBE and RSU will be exchanged:

  • SRM (Signal Request Message)
  • SSM (Signal Status Message)

[This slide contains a drawing of a bus and a traffic signal. There is an arrow from the traffic signal to the bus, and then another from the bus to the traffic signal. The arrow from the traffic signal to the bus has “SSM” in the middle of it; the arrow from the bus to the traffic signal has “SRM” in the middle of it. Below the bus and traffic signal are two arrows pointing toward each other. The one pointing to the right contains “TSP” in it. The one pointing to the left contains “FSP.”]

Slide 6: Microsimulation Setup

  • PTV VISSIM microsimulation software
  • ECONOLIT ASC/3 Software in the Loop (SIL) controllers embedded in VISSIM
  • VISSIM’s Component Object Model (COM)
  • Python programming language

[This slide contains three logos: (1) logo of PTV VISSIM, (2) logo of Econolite, and (3) logo of Python.]

Slide 7: External Controller (ASC/3) Settings

Signal controllers → ASC/3 Each controller should be set separately
SCP logic is programmed directly in the controller The same procedure can be performed on a field

[This slide contains a screenshot of the external controller (ASC/3) interface.]

Slide 8: V2I Communication-Detection Range

[This slide contains a diagram showing a road intersected by another road with two on and off ramps. There is a smaller road intersection diagram in the bottom left corner.]

Slide 9: Distance Computation

  • Models converting current VISSIM into world coordinates
  • BSM contains actual distance, transmitted in each time step
  • High fidelity of models and applied algorithms
  • Updated arrival time of vehicles in each time step

[This slide contains three images: (1) a drawing of a globe with an arc spanning from the western United States to Northern Europe, (2) a screenshot of the Haversine formula, and (3) a screenshot of some lines of computer code.]

Slide 10: Priority Algorithm for SCP

  • Priority granted for different conditions:
    • Distance between vehicle and signal
    • Estimated intersection arrival time
    • Queue conditions
  • Information contained in BSM
  • Updated at each time step

[This slide contains one image and one diagram: (1) an aerial image of two trucks on a road with a car between them and (2) a flow chart of decision phases.]

Slide 11: Conditional Priority Algorithm for SCP

  • Number of conditions extended to:
    • Expected arrival time
    • Number of passenger on board
    • Schedule adherence
  • Conditional priority
  • Priority levels

[This slide contains two images: (1) a flow chart of decisions and (2) an image of an aerial view of an urban street containing a truck, cars, and parked cars. The truck has concentric circles around it.]

Slide 12: SCP Strategies

No Priority → No strategy

Low Priority → GE / EG

High Priority → GE / EG / PR

[This slide contains a computer-generated image of a multi-lane road with traffic signals.]

Slide 13: FSP Test Case-Isolated Intersection (Laramie, WY)

  • Vehicle composition: 54% passenger cars, 46% freight vehicles
  • Intersection part of access/egress ramp to I-80
  • Test period: PM peak hour (4:00-5:00 PM)
  • FSP algorithm criteria:
    • Time for vehicle to reach intersection
    • Queue conditions on approach

[This slide contains two images: (1) a diagram showing an intersection of two roads and (2) an aerial photograph of an intersection of two roads with truck parking adjacent to the intersection.]

Slide 14: Results

  • Do Nothing plus five scenarios with different market penetration rate (MPR): 10% 25% 50% 75% 100%
  • Unconditional FSP - all trucks within detection zone receive priority
  • Simulation outputs: phase green time distributions, intersection performance (delays and stops for all vehicles and trucks)
  • T-test with 95% confidence level

[This slide contains a digital drawing of a bar graph, pie chart, and pieces of paper containing charts and graphs.]

Slide 15: Results

Phases (mov.) Parameter FSP Implementation/ MPR for CV trucks
Base FSP/CV 10% FSP/CV 25% FSP/CV 50% FSP/CV 75% FSP/CV 100%
1 (WBL) Avg. Green Time (s) 88.1 100.9 101.6 97.0 99.9 112.7
SD 178.2 216.0 187.9 187.7 208.8 226.0
P-value 0.365 0.305 0.497 0.398 0.098
2 (EBT, EBR) Avg. Green Time (s) 18.8 19.5 20.0 20.6 20.8 20.7
SD 15.2 16.0 17.0 17.5 18.2 18.2
P-value 0.151 0.018 0.001 0 0
4 (SBT, SBL) Avg. Green Time (s) 19.3 20.3 20.9 21.7 21.9 21.7
SD 20.7 22.0 23.2 23.8 24.7 24.3
P-value 0.16 0.028 0.001 0.001 0.001
5 (EBL) Avg. Green Time (s) 227.0 287.2 250.0 282.2 299.5 382.7
SD 461.1 600.7 547.3 522.8 551.1 728.9
P-value 0.351 0.696 0.37 0.265 0.046
6 (WBT, WBR) Avg. Green Time (s) 18.9 19.9 20.3 21.1 21.4 21.2
SD 16.2 17.5 18.1 19.1 20.3 19.7
P-value 0.072 0.018 0 0 0
8 (NBT, NBL) Avg. Green Time (s) 19.3 20.3 20.9 21.7 21.9 21.7
SD 20.7 22.0 23.2 23.8 24.7 24.3
P-value 0.016 0.028 0.001 0.001 0.001

Slide 16: Results

1 2 3 4 5
For MPR 25% or higher, difference in green time between base scenario and all scenarios with FSP is significant A reduction in truck delays notable for MPR as low as 10% With increase of MPR to 25% delay reduction significantly increases, while for MPR 50% or higher, delays decrease slightly MPR of 50% or higher is borderline for significant drop in number of stops for all vehicles Reduction in delays for CV-equipped trucks varies 35%-48% depends on MPR; for passenger cars, reduction varies 14%-21%

Slide 17: Conclusions

  • CV-based FSP can significantly reduce truck delays and stops through GE or EG using proposed algorithm
  • Quality of improvement directly depends on percentage of vehicles equipped with CV technologies
  • MPR of CV-equipped trucks needs to be 50% or more for significant effects
  • Delays for CV-equipped trucks can be reduced 35% to 48% for movements with FSP
  • Unconditional FSP can lead to negative impacts on side streets
  • Used methodology for simulations in this study transferable to field conditions

Slide 18: TSP Test Case - Corridor (Salt Lake City, UT)

  • State Street corridor consisting of ten signalized intersections
  • Multi-modal (cars, transit vehicles, trucks, bicycle, pedestrians)
  • PM peak hour (4:00-6:00 PM)
  • Major bus route - Route 200
  • Future center running BRT

[This slide contains one diagram and one image: (1) an aerial photograph of an urban area. A road running north to south is highlighted and shorter segments of roads intersect the main road are also highlighted. (2) an aerial photograph is a diagram of the highlighted roads with the aerial photograph removed from the background.]

Slide 19: Results

Six simulation models to accommodate base conditions, traffic projections for 20 years, conventional TSP, CV-based TSP, BRT, and CV-based BRT

Simulation outputs include intersection performance measures, vehicle speeds, and phase split durations

T-test with 95% confidence level for statistically significance testing

[This slide contains a graph titled “Average delay per Vehicle (s).”]

Slide 20: Results

  • CV-based TSP reduce delays up to 33%
  • Speed increase for CV vehicles up to 11%
  • The three-level conditional CV TSP reduces buses delays by 12%
  • Delays for other traffic up 7% lower than for unconditional TSP

[This slide contains a bar graph with the title “Bus Speeds (NB/SB) (mph).”]

Slide 21: Conclusions

  • Flexibility in using information from CV-equipped vehicles
  • Different forms of conditional priority
  • BSM contains: current queue conditions, vehicle speed, distance between transit vehicle and intersection, expected arrival time, number of passengers on board, schedule adherence
  • Developed algorithm capable of introducing different levels of TSP while considering several criteria to grant priority
  • Possibility of using different TSP strategies
  • TSP enhanced with CV technologies performs better

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Jonathan Walker, P.E., Ph.D.
Chief Policy, Architecture, and Knowledge Transfer, ITS Joint Program Office
U.S. Department of Transportation
(202) 366-2199

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