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

Operational and Safety Assessment of CFIs in a CV Environment
Presenter: Mutasem Alzoubaidi
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.


Slide 1: Operational and Safety Assessment of CFIs in a CV Environment

Mutasem Alzoubaidi, MS

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

Slide 2: Alternative Intersection Designs

  • Favor arterials through movements
  • Reroute left turning movements
  • Reduce number of conflict points and separate those that remain
  • Improve overall operational and safety performance of arterials

[This slide contains two images: (1) screenshot of a cover page of a document titled “Unconventional Arterial Intersection Design, Management and Operations Strategies” and (2) screenshot of a cover page of a document titled “Alternative Intersections/Interchanges: Informational Report (AIIRI).”]

Slide 3: Continuous Flow Intersection (CFI)

  • Also known as Displaced Left Turn (DLT) intersection
  • Left turns relocated to far‐left side of intersection
  • CFI can be either partial or full
  • Its design provides fewer conflict points

[This slide contains two images: (1) a diagram of an intersection and (2) an aerial photograph of an intersection.]

Slide 4: Continuous Flow Intersection (CFI)

  • Also known as Displaced Left Turn (DLT) intersection
  • Left Turns relocated to far‐left side of intersection
  • CFI can be either partial or full
  • Its design provides fewer conflict points

[This slide contains two images: (1) a diagram of an intersection and (2) an aerial photograph of an intersection.]

Slide 5: CFI Benefits

  • Increases capacity by 90% compared to its conventional counterpart
  • Reduces delays by up to 70.6%
  • Reduction of 27% in total crashes and 17% reduction in severe injury crashes

[This slide contains an aerial photograph of an intersection.]

Slide 6: Connected Vehicle (CV) Technology

  • DSRC communications allow:
    • V2V communications
    • V2I communications
    • V2X communications
  • Sharing of important information
  • Society of Automotive Engineer’s DSRC message set directory (SAE J2735)
  • Basic safety message (BSM): vehicle ID, type, longitude, latitude, speed, transmission status, suspension, wipers status, headlight status, turn signals, etc.

[This slide contains a diagram of an intersection showing cars, trees, and communications towers.]

Slide 7: CV Benefits

  • Reduced delays
  • Travel time savings
  • Savings in fuel consumption
  • Improved safety
  • Great return on investment

[This slide contains an aerial photograph of an urban intersection containing cars and buses. The cars and buses have concentric circles around them.]

Slide 8: Simulation Testbed

  • Bangerter Highway (SR‐154), major urban arterial, Salt Lake County, Utah
  • Approximately 6.6 miles in length
  • Six signalized intersections (four partial CFIs, one full CFI, one single point urban interchange)
  • Operational and safety analyses conducted for PM peak hour (4:30–5:30 p.m.)

[This slide contains an aerial photograph of an urban area. Three parallel roads running north to south are highlighted. They are intersected perpendicularly by six roads, each of which is highlighted.]

Slide 9: Modeling Methodology

  • PTV VISSM 11
  • Econolite’s ASC/3 external software‐in‐the‐loop signal controller
  • User defined attributes for coordinates
  • V2I algorithms deployed in Python for distance calculation and driver behavior modeling
  • Distance calculated using Haversine’s formula in each time step

[This slide contains an aerial photograph of an urban area. Three parallel roads running north to south are highlighted. They are intersected perpendicularly by six roads, each of which is highlighted.]

Slide 10: Scenario Development

  • Traffic conditions for 2019 and 2029 used to develop simulation models
  • Five identical models developed for each year, with varied CV market penetration rate (MPR)
  • MPRs consist of 0%, 25%, 50%, 75%, and 100% CV vehicles
  • TRJ output files analyzed using SSAM

[This slide contains an aerial photograph of an urban area. Three parallel roads running north to south are highlighted. They are each intersected perpendicularly by six roads, each of which is highlighted.]

Slide 11: Vehicular Travel Times Results

  • Savings of approximately 6.4% to 24% for 2019
  • While that was 6.4% to 28% for 2029
  • Statistically significant at 95% confidence interval

[This slide contains a line graph representing vehicular travel time (y‐axis) over market penetration rate (x‐axis). It contains one line representing 2019 and another line representing 2029. Both lines show vehicular travel time decreasing as market penetration rate increase.]

Slide 12: Vehicular Delays Results

  • Reductions of 5% to 20% in vehicular delays
  • Statistically significant at 95% confidence interval

[This slide contains two graphs: (1) a line graph titled “2019” representing vehicular travel time (y‐axis) over market penetration rate (x‐axis). The line show vehicular travel time decreasing as market penetration rate increase. (2) a line graph titled “2029” representing vehicular travel time (y‐axis) over market penetration rate (x‐axis). The line shows vehicular travel time decreasing as market penetration rate increase.]

Slide 13: Queue Lengths Results

  • Reductions of 8% to 47% in queue lengths
  • Statistically significant at 95% confidence interval

[This slide contains two graphs: (1) a line graph titled “2019” representing vehicular travel time (y‐axis) over market penetration rate (x‐axis). The line show vehicular travel time decreasing as market penetration rate increase. (2) a line graph titled “2029” representing vehicular travel time (y‐axis) over market penetration rate (x‐axis). The line shows vehicular travel time decreasing as market penetration rate increase.]

Slide 14: Headways Results

Full CFI Mean Headways (s)
MPR RegCar19 CVCar19 RegHGV19 CVHGV19 RegCar29 CVCar29 RegHGV29 CVHGV29
0% 7.9 8.3 8.4 8.2
25% 7.7 7.5 8.9 7.4 7.8 7.2 8.4 7.6
50% 7.7 7.1 8.3 8.5 7.8 7.3 8.8 7.3
75% 8.3 7.4 8.3 7.4 7.4 7.5 8.0 8.9
100% 7.4 7.9 7.5 8.4
Partial CFI Mean Headways (s)
MPR RegCar19 CVCar19 RegHGV19 CVHGV19 RegCar29 CVCar29 RegHGV29 CVHGV29
0% 7.3 8.1 9.1 9.7
25% 7.2 7.0 9.1 8.1 8.6 7.8 9.5 8.2
50% 6.7 6.8 7.1 7.2 7.9 7.6 8.6 8.9
75% 6.3 6.6 7.4 8.1 7.5 7.8 9.9 9.0
100% 7.0 7.9 7.9 9.3
  • Headways between CVs smaller than those between conventional vehicles
  • Shorter headways translate to increased capacity of CFIs

Slide 15: Speed Results

Full CFI Mean Headways (mph)
MPR RegCar19 CVCar19 RegHGV19 CVHGV19 RegCar29 CVCar29 RegHGV29 CVHGV29
0% 28.3 25.5 26.9 24.4
25% 28.5 27.1 25.7 23.8 27.0 25.6 24.5 24.9
50% 28.7 27.3 25.2 25.0 27.0 25.5 24.2 23.2
75% 28.9 27.5 25.7 25.2 27.4 25.7 24.5 23.4
100% 27.3 24.5 25.9 22.8
Partial CFI Mean Headways (mph)
MPR RegCar19 CVCar19 RegHGV19 CVHGV19 RegCar29 CVCar29 RegHGV29 CVHGV29
0% 30.8 27.4 28.4 24.8
25% 31.3 28.7 27.9 25.7 28.9 26.8 25.4 23.9
50% 31.4 28.8 28.0 25.3 29.6 27.5 26.3 23.8
75% 31.9 29.2 29.3 25.4 30.3 27.8 26.6 24.1
100% 29.4 25.7 28.3 24.5
  • CV speeds lower than conventional vehicles’ speeds
  • Less variation in speeds for CVs
  • Better speed harmonization, leading to improved safety
  • Improved operational performance

Slide 16: Surrogate Safety Assessment Model (SSAM) Results

2019 Results

MPR TTC MaxS Total
0% 1.05 13.57 6935
25% 1.04 13.51 7230
50% 1.02 13.47 7571
75% 1.01 13.42 8066
100% 1.01 13.39 8395

2029 Results

MPR TTC MaxS Total
0% 1.04 13.29 7215
25% 1.03 13.25 7396
50% 1.02 13.24 7601
75% 1.00 13.20 7948
100% 1.01 13.18 8232
  • Time‐to‐collision (TTC) values decrease with MPR increase, resulting in higher crash risk
  • Maximum speed of either vehicle (MaxS) would decrease, indicating lower crash severities with increase of MPR
  • Total number of simulated conflicts increases with increase in MPR
  • No V2V communication was used in study

Slide 17: Operational Results and Conclusions

  • Delays reduced by 20%
  • Travel time savings up to 28%
  • Queue length reductions up to 47%
  • Higher MPR leads to greater benefits
  • Shorter headways and spacings, leading to increased capacity
  • CVs increase speeds of all vehicle types

Slide 18: SSAM Results and Conclusions

  • TTC values drop with higher MPRs (higher crash risk)
  • Number of simulated conflicts increases with higher MPRs
  • MaxS decreases with higher MPRs (less severe crashes)

These results, while concerning, are expected, because no V2V algorithm that would allow vehicles to adjust their behavior based on the preceding vehicles’ was used. This is recommended for future work.

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