Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic at Two-Way Stop-Controlled Intersections
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 2
Abstract
Connected and automated vehicles (CAVs) hold great promise in enhancing transportation operations and roadway capacity. However, there is still limited guidance in the field of highway capacity analysis to help agencies account for the potential benefits. This paper investigates the impact of CAVs on the capacity of two-way-stop-controlled (TWSC) intersections and quantifies capacity adjustments under different CAV scenarios. An isolated TWSC intersection is considered in this research to analyze the CAV impacts on various intersection movement capacities under different CAV market penetration rates (MPRs) and conflict flow rates (CFRs). To model CAVs, cooperative adaptive cruise control (CACC) is considered because it directly impacts the vehicle’s longitudinal car-following behavior and therefore the capacity. Additionally, enhanced gap-acceptance behavior based on vehicle-to-vehicle (V2V) communication is included because it can benefit minor road movements via advanced notifications. This paper collects data from customized, well-calibrated TWSC simulation models constructed in commercially available software. The results show that with the increase in MPRs, CAVs can substantially enhance the capacity of the stop or yield controlled movements, and the improvement is more significant under low CFR scenarios. CAVs can also reduce the follow-up headway and thus improve the potential capacity based on the existing HCM TWSC capacity estimation method. Finally, based on the data collected from the simulation, a capacity adjustment factor (CAF) table is proposed for HCM implementation.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This study is supported in part by the Highway Capacity Manual Pooled Fund Study, led by the Oregon Department of Transportation. The work presented in this paper remains the sole responsibility of the authors.
References
Adebisi, A., Y. Guo, B. Schroeder, J. Ma, B. Cesme, A. Bibeka, and A. Morgan. 2022. “Highway Capacity Manual capacity adjustment factor development for connected and automated traffic at signalized intersections.” J. Transp. Eng. 148 (3): 04021121. https://doi.org/10.1061/JTEPBS.0000631.
Adebisi, A., Y. Liu, B. Schroeder, J. Ma, B. Cesme, A. Jia, and A. Morgan. 2020. “Developing Highway Capacity Manual capacity adjustment factors for connected and automated traffic on freeway segments.” Transp. Res. Rec. 2674 (10): 401–415. https://doi.org/10.1177/0361198120934797.
Buzachis, A., B. Filocamo, M. Fazio, J. A. Ruiz, M. Á. Sotelo, and M. Villari. 2019. “Distributed priority based management of road intersections using Blockchain.” In Proc., 2019 IEEE Symp. on Computers and Communications (ISCC), 1159–1164. New York: IEEE. https://doi.org/10.1109/ISCC47284.2019.8969653.
Cui, H., J. Xing, X. Li, and M. Zhu. 2018. “Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection.” Mod. Phys. Lett. B 32 (32): 1850396. https://doi.org/10.1142/S0217984918503967.
Dao, T., C. M. Clark, and J. P. Huissoon. 2008. “Distributed platoon assignment and lane selection for traffic flow optimization.” In Proc., 2008 IEEE Intelligent Vehicles Symp., 739–744. New York: IEEE.
DOT. 2018. Preparing for the future of transportation: Automated vehicles 3.0. Washington, DC: DOT.
Ghiasi, A., X. Li, and J. Ma. 2019. “A mixed traffic speed harmonization model with connected autonomous vehicles.” Transp. Res. Part C Emerging Technol. 104 (Jul): 210–233. https://doi.org/10.1016/j.trc.2019.05.005.
Guo, Y., and J. Ma. 2020. “Leveraging existing high-occupancy vehicle lanes for mixed-autonomy traffic management with emerging connected automated vehicle applications.” Transportmetrica A: Transp. Sci. 16 (3): 1375–1399. https://doi.org/10.1080/23249935.2020.1720863.
Guo, Y., J. Ma, C. Xiong, X. Li, F. Zhou, and W. Hao. 2019. “Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach.” Transp. Res. Part C Emerging Technol. 98 (Jan): 54–72. https://doi.org/10.1016/j.trc.2018.11.010.
Hartmann, M., N. Motamedidehkordi, S. Krause, S. Hoffmann, P. Vortisch, and F. Busch. 2017. “Impact of automated vehicles on capacity of the German freeway network.” In Proc., ITS World Congress. Berlin: Research Association of Automotive Technology.
HCM. 2010. Highway Capacity Manual. 5th ed. Washington, DC: Transportation Research Board.
Hu, X., and J. Sun. 2019. “Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area.” Transp. Res. Part C Emerging Technol. 101 (Apr): 111–125. https://doi.org/10.1016/j.trc.2019.02.016.
Jiang, Q., B. Schroeder, J. Ma, L. Rodegerdts, B. Cesme, A. Bibeka, and A. Morgan. 2022. “Developing Highway Capacity Manual capacity adjustment factors for connected and automated traffic on roundabouts.” J. Transp. Eng.Part A: Syst. 148 (5): 04022014.
Kiefer, R. J., M. T. Cassar, C. A. Flannagan, D. J. LeBlanc, M. D. Palmer, R. K. Deering, and M. A. Shulman. 2003. Forward collision warning requirements project: Refining the CAMP crash alert timing approach by examining “last second” braking and lane change maneuvers under various kinematic conditions, (No. DOT HS 809 574). Washington, DC: National Highway Traffic Safety Administration.
Kyte, M., et al. 1996. Capacity and level of service at unsignalized intersections. Final Report. Volume 1-two-way stop-controlled intersections. Washington, DC: Transportation Research Board.
Le Vine, S., A. Zolfaghari, and J. Polak. 2015. “Autonomous cars: The tension between occupant experience and intersection capacity.” Transp. Res. Part C Emerging Technol. 52 (Mar): 1–14. https://doi.org/10.1016/j.trc.2015.01.002.
Li, Z., M. DeAmico, M. V. Chitturi, A. R. Bill, and D. A. Noyce. 2013. Calibration of VISSIM roundabout model: A critical gap and follow-up headway approach. Washington, DC: Transportation Research Board.
Liu, H., X. Kan, S. E. Shladover, X. Lu, and R. E. Ferlis. 2018a. “Impact of cooperative adaptive cruise control on multilane freeway merge capacity.” J. Intell. Transp. Syst. 22 (3): 263–275. https://doi.org/10.1080/15472450.2018.1438275.
Liu, H., X. D. Kan, S. E. Shladover, X. Lu, and R. E. Ferlis. 2018b. “Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities.” Transp. Res. Part C Emerging Technol. 95 (Oct): 261–279. https://doi.org/10.1016/j.trc.2018.07.027.
Liu, H., X. Lu, and S. E. Shladover. 2019. “Traffic signal control by leveraging cooperative adaptive cruise control (CACC) vehicle platooning capabilities.” Transp. Res. Part C Emerging Technol. 104 (Jul): 390–407. https://doi.org/10.1016/j.trc.2019.05.027.
Liu, T., X. Mu, B. Huang, X. Tang, F. Zhao, X. Wang, and D. Cao. 2020. “Decision-making at unsignalized intersection for autonomous vehicles: Left-turn maneuver with deep reinforcement learning.” Preprint, submitted August 14, 2015. http://arxiv.org/abs/2008.06595.
Ma, J., J. Hu, E. Leslie, F. Zhou, P. Huang, and J. Bared. 2019. “An eco-drive experiment on rolling terrains for fuel consumption optimization with connected automated vehicles.” Transp. Res. Part C Emerging Technol. 100 (Mar): 125–141. https://doi.org/10.1016/j.trc.2019.01.010.
Maryam Mousavi, S., D. Lord, B. Dadashova, and S. Reza Mousavi. 2020. “Can autonomous vehicles enhance traffic safety at unsignalized intersections?” In Proc., Int. Conf. on Transportation and Development 2020, 194–206. Reston, VA: ASCE.
McGowen, P., and L. Stanley. 2012. “Alternative methodology for determining gap acceptance for two-way stop-controlled intersections.” J. Transp. Eng. 138 (5): 495–501. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000358.
Milanés, V., and S. E. Shladover. 2014. “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data.” Transp. Res. Part C Emerging Technol. 48 (Nov): 285–300. https://doi.org/10.1016/j.trc.2014.09.001.
Nowakowski, C., S. E. Shladover, D. Cody, F. Bu, J. O’Connell, J. Spring, S. Dickey, and D. Nelson. 2011. Cooperative adaptive cruise control: Testing drivers’ choices of following distances. UCB-ITS-PRR-2011-01. Berkeley, CA: Univ. of California, Berkeley.
ORAD (On-Road Automated Driving). 2020. Taxonomy and definitions for terms related to cooperative driving automation for on-road motor vehicles. SAE J3216. Chicago: SAE International.
PTV Group. 2018. PTV VISSIM 11 user manual. Karlsruhe, Germany: PTV Group.
Raff, M. S. 1950. A volume warrant for urban stop signs. Washington, DC: Eno Foundation for Highway Traffic Control.
Stephens, T. S., J. Gonder, Y. Chen, Z. Lin, C. Liu, and D. Gohlke. 2016. Estimated bounds and important factors for fuel use and consumer costs of connected and automated vehicles. Alexandria, VA: US Department of Commerce National Technical Information Service.
Transportation Research Board. 2016. Highway capacity manual: A guide for multimodal mobility analysis. 6th ed. Washington, DC: National Academies Press.
Troutbeck, R. 1992. “Estimating the critical assessment gap from traffic movements.” Res. Rep. 23 (92-5).
Troutbeck, R. J. 2016. “Revised raff’s method for estimating critical gaps.” Transp. Res. Rec. 2553 (1): 1–9. https://doi.org/10.3141/2553-01.
Wang, Q., B. Li, Z. Li, and L. Li. 2017. “Effect of connected automated driving on traffic capacity.” In Proc., 2017 Chinese Automation Congress (CAC), 633–637. Beijing: National Natural Science Foundation of China.
Wang, Z., G. Wu, and M. Barth. 2018. Distributed consensus-based cooperative highway on-ramp merging using V2X communications. Chicago: SAE International.
Xu, B., S. E. Li, Y. Bian, S. Li, X. J. Ban, J. Wang, and K. Li. 2018. “Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections.” Transp. Res. Part C Emerging Technol. 93 (Aug): 322–334. https://doi.org/10.1016/j.trc.2018.06.004.
Zhan, W. 2019. Interactive prediction and planning for autonomous driving: From algorithms to fundamental aspects. Berkeley, CA: Univ. of California.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 22, 2021
Accepted: Aug 31, 2022
Published online: Nov 28, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 28, 2023
ASCE Technical Topics:
- Business management
- Data collection
- Engineering fundamentals
- Flow (fluid dynamics)
- Flow rates
- Fluid dynamics
- Fluid mechanics
- Highway and road management
- Highway transportation
- Highways and roads
- Hydrologic engineering
- Infrastructure
- Intelligent transportation systems
- Intersections
- Methodology (by type)
- Practice and Profession
- Research methods (by type)
- Traffic capacity
- Traffic engineering
- Traffic management
- Transportation engineering
- Transportation management
- Water and water resources
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.