Freeway Capacity in a

Connected/Automated Mixed

Traffic Environment

Sarah El-Dabaja1, Bhaven Naik2, Deborah McAvoy3

1Ph.D. Candidate, Ohio University, 037 Stocker Center, 1 Ohio University, Athens, OH, 45701, Email: se954207@ohio.edu2Assistant Professor, Ohio University, 226 Stocker Center, 1 Ohio University, Athens, OH 45701, Email: naik@ohio.edu

3Associate Professor, Ohio University, 122 Stocker Center, 1 Ohio University, Athens, OH 45701, Email: mcavoy@ohio.edu

Discussion Topics

• The Move Towards CACC

• CACC and Freeway Capacity

• Research Objectives

• Proposed Methodology

• Anticipated Results and Implications

The Problem: Congestion

6.9 billion hours lost1

3.1 billion gallons of fuel wasted1

$160 billion spent1

1Schrank, D., Eisele, B., Lomax, T, & Bak, J. (2015). TTI’s 2015 Urban Mobility Scorecard. College Station, TX: Texas Transportation Institute, A&M University.

• Judgement, reaction times, execution

Reduced Variability

• Harmonious environment

Traffic Flow Stability

• Compact clusters

Tighter Platoons

• More efficient movement of more vehicles

Increased Capacity

One Potential Solution: CACC

CACC on FreewaysPlatooning Algorithms

• Vehicles coupled wirelessly to form dense road trains

2Nowakowski, C., Shladover, S.E., Lu, X.Y., Thompson, D., & Kailas, A. (2015). Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives. California PATH

Research Report. Retrieved from: http://escholarship.org/uc/item/7jf9n5wm

[2]

CACC on FreewaysPlatooning Algorithms

• Relies on some form of gap regulation

• Enhances capacity through string stability and shorter headways4

3Amoozadeh, M., Deng, H., Chuah, C.N., Zhang, H.M, & Ghosal, D. (2015). Platoon management with cooperative adaptive cruise control enabled by VANET. Vehicular Communications, 2.

http://dx.doi.org/10.1016/j.vehcom.2015.03.004

[3]

CACC on FreewaysMerging Algorithms

• Encourages gap creation for smooth transitions and easy

accelerations and decelerations

[5]

5Scarinci, R., & Heydecker, B. (2014). Control Concepts for Facilitating Motorway On-ramp Merging using Intelligent Vehicles. Transport Reviews, 34(6), 775-797. doi:

10.1080/01441647.2014.983210

CACC on FreewaysMerging Algorithms

• Variety of techniques

– Control merging vehicle,

mainline vehicles, or both

– Virtual gap creation

– Slot-based

– Ideal driver behavior

– Mathematical optimization

• Enhances capacity by

reducing conflicts5

[6]

6Marinescu, D., Curn, J., Bourouche, M., & Cahill, V. (2012). On-ramp traffic merging using cooperative intelligent vehicles: a slot-based approach. 15th International IEEE Conference on Intelligent

Transportation Systems, Anchorage, AL.

CACC Freeway Capacity EstimatesPenetration Rate (%) Base Capacity (vph) CACC Capacity (vph) Percent Difference (%)

10 2,020 2,050 1

20 2,020 2,100 4

20 4,900 5,000 2

20 4,890 5,000

5,300 w/lane

2

8

20 6,000 6,000

8,300 w/lane

0

38

20 8,100 8,150 0.6

30 2,020 2,150 6

7Shladover, S.E., Su, D., & Lu, X.Y. (2012). Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow. Transportation Research Record Journal of the Transportation Research Board. doi:10.3141/2324-088Arnaout, G., & Bowling, S. (2011). Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp. Journal of Industrial Engineering and Management, 4(4), 699-717. http://dx.doi.org/10.3926/jiem.3449Arnaout, G.M., & Bowling, S. (2014). A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Traffic Dynamics. International Journal of Automation and Computing, 11(1). doi: 10.1007/s11633-014-0760-210van Arem, B., van Driel, C.J.G., & Visser, R. (2006). The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics. IEEE Transactions on Intelligent Transportation Systems, 7(4). doi:10.1109/TITS.2006.884615

CACC Freeway Capacity EstimatesPenetration Rate (%) Base Capacity (vph) CACC Capacity (vph) Percent Difference (%)

40 2,020 2,250 11

40 4,900 5,500 12

40 8,100 8,200

7,990 w/lane

1

-1.4

50 2,020 2,300 14

60 2,020 2,600 29

60 4,900 6,000 22

60 8,100 8,300

8,300 w/lane

2.5

2.5

70 2,020 2,700 34

7Shladover, S.E., Su, D., & Lu, X.Y. (2012). Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow. Transportation Research Record Journal of the Transportation Research Board. doi:10.3141/2324-088Arnaout, G., & Bowling, S. (2011). Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp. Journal of Industrial Engineering and Management, 4(4), 699-717. http://dx.doi.org/10.3926/jiem.34410van Arem, B., van Driel, C.J.G., & Visser, R. (2006). The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics. IEEE Transactions on Intelligent Transportation Systems, 7(4). doi:10.1109/TITS.2006.884615

CACC Freeway Capacity EstimatesPenetration Rate (%) Base Capacity (vph) CACC Capacity (vph) Percent Difference (%)

80 2,020 2,900 44

80 4,900 7,450 52

80 8,100 8,300

8,100

2.5

0

90 2,020 3,300 63

100 600 3,250 442

100 1,540 1,880 22

100 2,020 4,000 98

100 8,100 8,300 2.5

100 4,900 7,800 59

6Marinescu, D., Curn, J., Bourouche, M., & Cahill, V. (2012). On-ramp traffic merging using cooperative intelligent vehicles: a slot-based approach.15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AL.7Shladover, S.E., Su, D., & Lu, X.Y. (2012). Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow. Transportation Research Record Journal of the Transportation Research Board. doi:10.3141/2324-088Arnaout, G., & Bowling, S. (2011). Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp. Journal of Industrial Engineering and Management, 4(4), 699-717. http://dx.doi.org/10.3926/jiem.34410van Arem, B., van Driel, C.J.G., & Visser, R. (2006). The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics. IEEE Transactions on Intelligent Transportation Systems, 7(4). doi:10.1109/TITS.2006.88461511Xie, Y., Zhang, H., Gartner, N., & Arsava, T. (2014). Collaborative Merging Behaviors and Their Impacts on Freeway Ramp Operations under Connected Vehicle Environment. Symposium Celebration 50 Years of Traffic Flow Theory, Portland, OR.

Problems with Current Estimates

• Major discrepancies in estimates

• Inconsistencies in methodology

– No single approach for practitioners to apply like HCM

• Uncertainties related to manual driver behavior in a

mixed traffic environment

– What headway will drivers choose?

– Will reaction times be affected?

Research Objectives

• Investigate manual driver behavior in a mixed

traffic environment under varying penetration rates

• Derive traffic flow parameters that are statistically

significant and generalizable:

– PRTs

– Headways

– PCEs of CACC vehicles (passenger cars, heavy trucks,

buses, RVs)

• Develop new freeway capacity analysis method in

consideration of varying penetration rates

Proposed Methodology

Phase I: Driving Simulation

Phase II: Microsimulation

Phase III: Synthesis of Results into Capacity Analysis Method

Phase I – Driving Simulation

• Purpose: To obtain critical values of PRTs and

headways for manual vehicles

• DriveSafety driving simulator

• Research Tasks:

– Develop freeway driving scenarios

– Recruit participants

– Collect and analyze data

Phase I – Driving Simulation

Inputs

• Ambient traffic control

• Subject vehicle control

• Environment

• Event triggers

Outputs

• Acceleration

• Braking

• Headways

• Gaze

• Heart Rate

• PRTs

Driver Behavior Research in Progress

• Driver reaction to failure of fully autonomous system

Parameter Immediate Response Long-Term Response

Lane Position Lane departure More oscillation, lane

departures

Steering “Jerky” right to left Less controlled

Velocity Decreases approximately

10-15 mph

±10-25 mph deviations

from speed limit

Acceleration Drops, then fully depressed Harder accelerations

Braking Not applied Harder braking

Phase II - Microsimulation

• Purpose: To observe impacts of new traffic parameter

values on flow and capacity

• VISSIM package

• Research Tasks:

– Develop model calibrated with driving simulator data

– Conduct sensitivity analysis of capacity impacts related to

CACC penetration rates

Phase III – Synthesis of Results

into Capacity Analysis Method

• Purpose: To develop uniform and applicable methods

for calculating freeway capacity estimates with CACC

• Research Tasks:

– Statistical modeling to develop analysis techniques similar to

HCM methods

Anticipated Results and Implications

• Phase I

– PRTs, headways, and PCEs corresponding to penetration rates

– Replace subjective assumptions with substantive empirical values

• Phase II

– Fully calibrated microsimulation model

– Analysis of CACC algorithms (e.g., platooning, merging)

• Phase III

– Freeway capacity analysis methods for mixed traffic environment

– Consistent estimates of freeway capacity with CACC

Questions?

Thank you!

References1Schrank, D., Eisele, B., Lomax, T, & Bak, J. (2015). TTI’s 2015 Urban Mobility Scorecard. College Station, TX: Texas Transportation Institute,

A&M University. 2Nowakowski, C., Shladover, S.E., Lu, X.Y., Thompson, D., & Kailas, A. (2015). Cooperative Adaptive Cruise Control (CACC) for Truck

Platooning: Operational Concept Alternatives. California PATH Research Report. Retrieved from: http://escholarship.org/uc/item/7jf9n5wm3Amoozadeh, M., Deng, H., Chuah, C.N., Zhang, H.M, & Ghosal, D. (2015). Platoon management with cooperative adaptive cruise control enabled

by VANET. Vehicular Communications, 2. http://dx.doi.org/10.1016/j.vehcom.2015.03.0044Milanes, V., Shladover, S.E., Spring, J., Nowakowski, C., Kawazoe, H., & Nakamura, M. (2014). Cooperative Adaptive Cruise Control in Real

Traffic Situations. IEEE Transactions on Intelligent Transportation Systems, 15(1). doi:10.1109/TITS.2013.22784945Scarinci, R., & Heydecker, B. (2014). Control Concepts for Facilitating Motorway On-ramp Merging using Intelligent Vehicles. Transport Reviews,

34(6), 775-797. doi: 10.1080/01441647.2014.9832106Marinescu, D., Curn, J., Bourouche, M., & Cahill, V. (2012). On-ramp traffic merging using cooperative intelligent vehicles: a slot-based approach.

15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AL.7Shladover, S.E., Su, D., & Lu, X.Y. (2012). Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow. Transportation Research

Record Journal of the Transportation Research Board. doi:10.3141/2324-088Arnaout, G., & Bowling, S. (2011). Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp.

Journal of Industrial Engineering and Management, 4(4), 699-717. http://dx.doi.org/10.3926/jiem.3449Arnaout, G.M., & Bowling, S. (2014). A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Traffic Dynamics.

International Journal of Automation and Computing, 11(1). doi: 10.1007/s11633-014-0760-210van Arem, B., van Driel, C.J.G., & Visser, R. (2006). The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics. IEEE

Transactions on Intelligent Transportation Systems, 7(4). doi:10.1109/TITS.2006.88461511Xie, Y., Zhang, H., Gartner, N., & Arsava, T. (2014). Collaborative Merging Behaviors and Their Impacts on Freeway Ramp Operations under

Connected Vehicle Environment. Symposium Celebration 50 Years of Traffic Flow Theory, Portland, OR.

Photo Creditshttp://www.usatoday.com/story/news/nation/2014/06/04/worst-traffic-cities/9926213/

http://careerconfidential.com/i-wasted-time-job-boards/

http://www.alexsworld.co/what-does-the-nd-mean-for-me/

http://www.aopa.org/Pilot-Resources/Aircraft-Ownership/The-Pilots-Guide-to-Taxes

http://www.ohio.edu/research/communications/driving_simulation.cfm