Abstract
Vehicle platooning is an innovative strategy that uses automated driving technology and communication to enable a more efficient use of transportation networks. By controlling tighter gaps between vehicles, vehicle platooning will increase freeway capacity. However, it is important to quantify the extent of the increase of capacity so that highway engineers can plan for this technology. This Federal Highway Administration research develops an analytical model to predict the capacity of basic freeway segments based on the market penetration and the maximum number of vehicles allowed in a platoon. It uses these predictions to calculate passenger car equivalents, which may be required for planning purposes. Simulations show that capacity can be predicted analytically to within 2% of simulated values. In addition, for the parameters used in the analysis, the capacity of freeways that restrict platoons to no more than 5 vehicles is comparable to the capacity of freeways that allow larger platoons (e.g., 6.1% maximum difference in capacity between maximum 5 and 12 vehicle platoons); consideration of limiting platoon size is important to ensure maneuverability.
Get full access to this article
View all available purchase options and get full access to this article.
References
Arnaout, G., and S. Bowling. 2011. “Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp.” J. Ind. Eng. Manage. 4 (4): 699–717.
Arnaout, G., and S. Bowling. 2014. “A progressive deployment strategy for cooperative adaptive cruise control to improve traffic dynamics.” Int. J. Autom. Comput. 11 (1): 10–18. https://doi.org/10.1007/s11633-014-0760-2.
Bierstedt, J., A. Gooze, C. Gray, J. Peterman, L. Raykin, and J. Walters. 2014. Effects of next-generation vehicles on travel demand and highway capacity. Walnut Creek, CA: Fehr & Peers.
Chen, D., S. Ahn, M. Chitturi, and D. A. Noyce. 2017. “Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles.” Transp. Res. Part B: Methodol. 100: 196–221. https://doi.org/10.1016/j.trb.2017.01.017.
Ghiasi, A., O. Hussain, Z. S. Qian, and X. Li. 2017. “A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method.” Transp. Res. Part B: Methodol. 106 (Dec): 266–292. https://doi.org/10.1016/j.trb.2017.09.022.
Gipps, P. G. 1981. “A behavioural car-following model for computer simulation.” Transp. Res. Part B: Methodol. 15 (2): 105–111. https://doi.org/10.1016/0191-2615(81)90037-0.
Maiti, S., S. Winter, and L. Kulik. 2017. “A conceptualization of vehicle platoons and platoon operations.” Transp. Res. Part C: Emerg. Technol. 80 (Jul): 1–19. https://doi.org/10.1016/j.trc.2017.04.005.
Marsden, G., M. McDonald, and M. Brackstone. 2001. “Towards an understanding of adaptive cruise control.” Transp. Res. Part C 9 (1): 33–51.
Milanés, V., and S. E. Shladover. 2014. “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data.” Transp. Res. Part C: Emerg. Technol. 48 (Nov): 285–300. https://doi.org/10.1016/j.trc.2014.09.001.
Rahman, M. S., M. Abdel-Aty, L. Wang, and J. Lee. 2018. Understanding the highway safety benefits of different approaches of connected vehicles in reduced visibility conditions. Washington, DC: Transportation Research Board.
Schakel, W. J., B. Van Arem, and B. D. Netten. 2010. “Effects of cooperative adaptive cruise control on traffic flow stability.” In Proc., 13th Int. IEEE Conf. on Intelligent Transportation Systems, 759–764. New York: IEEE.
Shladover, S., D. Su, and X. Y. Lu. 2012. “Impacts of cooperative adaptive cruise control on freeway traffic flow.” Transp. Res. Rec. 2324: 63–70. https://doi.org/10.3141/2324-08.
Tiernan, T., N. Richardson, P. Azeredo, W. Najm, and T. Lochrane. 2017. Test and evaluation of vehicle platooning proof-of-concept based on cooperative adaptive cruise control. Boston: John A. Volpe National Transportation Systems Center.
Turri, V., B. Besselink, and K. H. Johansson. 2017. “Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning.” IEEE Trans. Control Syst. Technol. 25 (1): 12–28. https://doi.org/10.1109/TCST.2016.2542044.
Van Arem, B., C. J. Van Driel, and R. Visser. 2006. “The impact of cooperative adaptive cruise control on traffic-flow characteristics.” IEEE Trans. Intell. Transp. Syst. 7 (4): 429–436. https://doi.org/10.1109/TITS.2006.884615.
VanderWerf, J., S. Shladover, N. Kourjanskaia, M. Miller, and H. Krishnan. 2001. “Modeling effects of driver control assistance systems on traffic.” Transp. Res. Rec. 1748: 167–174. https://doi.org/10.3141/1748-21.
VanderWerf, J., S. Shladover, M. Miller, and N. Kourjanskaia. 2002. “Effects of adaptive cruise control systems on highway traffic flow capacity.” Transp. Res. Rec. 1800: 78–84.
Xiao, L., M. Wang, and B. Van Arem. 2017. “Realistic car-following models for microscopic simulation of adaptive and cooperative adaptive cruise control vehicles.” Transp. Res. Rec. 2623: 1–9. https://doi.org/10.3141/2623-01.
Ye, L., and T. Yamamoto. 2018. “Modeling connected and autonomous vehicles in heterogeneous traffic flow.” Phys. A: Stat. Mech. Appl. 490: 269–277. https://doi.org/10.1016/j.physa.2017.08.015.
Zhao, L., and J. Sun. 2013. “Simulation framework for vehicle platooning and car-following behaviors under connected-vehicle environment.” Procedia-Social Behav. Sci. 96 (Nov): 914–924. https://doi.org/10.1016/j.sbspro.2013.08.105.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 19, 2017
Accepted: May 15, 2018
Published online: Aug 13, 2018
Published in print: Oct 1, 2018
Discussion open until: Jan 13, 2019
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.