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A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method

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  • Ghiasi, Amir
  • Hussain, Omar
  • Qian, Zhen (Sean)
  • Li, Xiaopeng

Abstract

The projected rapid growth of the market penetration of connected and autonomous vehicle technologies (CAV) highlights the need for preparing sufficient highway capacity for a mixed traffic environment where a portion of vehicles are CAVs and the remaining are human-driven vehicles (HVs). This study proposes an analytical capacity model for highway mixed traffic based on a Markov chain representation of spatial distribution of heterogeneous and stochastic headways. This model captures not only the full spectrum of CAV market penetration rates but also all possible values of CAV platooning intensities that largely affect the spatial distribution of different headway types. Numerical experiments verify that this analytical model accurately quantifies the corresponding mixed traffic capacity at various settings. This analytical model allows for examination of the impact of different CAV technology scenarios on mixed traffic capacity. We identify sufficient and necessary conditions for the mixed traffic capacity to increase (or decrease) with CAV market penetration rate and platooning intensity. These theoretical results caution scholars not to take CAVs as a sure means of increasing highway capacity for granted but rather to quantitatively analyze the actual headway settings before drawing any qualitative conclusion. This analytical framework further enables us to build a compact lane management model to efficiently determine the optimal number of dedicated CAV lanes to maximize mixed traffic throughput of a multi-lane highway segment. This optimization model addresses varying demand levels, market penetration rates, platooning intensities and technology scenarios. The model structure is examined from a theoretical perspective and an analytical approach is identified to solve the optimal CAV lane number at certain common headway settings. Numerical analyses illustrate the application of this lane management model and draw insights into how the key parameters affect the optimal CAV lane solution and the corresponding optimal capacity. This model can serve as a useful and simple decision tool for near future CAV lane management.

Suggested Citation

  • Ghiasi, Amir & Hussain, Omar & Qian, Zhen (Sean) & Li, Xiaopeng, 2017. "A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 266-292.
    • Handle: RePEc:eee:transb:v:106:y:2017:i:c:p:266-292
    DOI: 10.1016/j.trb.2017.09.022
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    1. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
    2. Cherry, Christopher R. & Adelakun, Adebola A., 2012. "Truck driver perceptions and preferences: Congestion and conflict, managed lanes, and tolls," Transport Policy, Elsevier, vol. 24(C), pages 1-9.
    3. Menendez, Monica & Daganzo, Carlos F., 2007. "Effects of HOV lanes on freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 809-822, October.
    4. Geroliminis, Nikolas & Daganzo, Carlos F., 2008. "Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 759-770, November.
    5. Dahlgren, Joy, 2002. "High-occupancy/toll lanes: where should they be implemented?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(3), pages 239-255, March.
    6. Chu, Chih-Peng & Tsai, Jyh-Fa & Hu, Shou-Ren, 2012. "Optimal starting location of an HOV lane for a linear monocentric urban area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 457-466.
    7. Vincent A.C. van den Berg & Erik T. Verhoef, 2015. "Robot Cars and Dynamic Bottleneck Congestion: The Effects on Capacity, Value of Time and Preference Heterogeneity," Tinbergen Institute Discussion Papers 15-062/VIII, Tinbergen Institute, revised 11 Jul 2016.
    8. Bansal, Prateek & Kockelman, Kara M., 2017. "Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 49-63.
    9. Shladover, Steven E & Nowakowski, Christopher & Lu, Xiao-Yun & Hoogendoorn, Raymond, 2014. "Using Cooperative Adaptive Cruise Control (CACC) to Form High-Performance Vehicle Streams," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3m89p611, Institute of Transportation Studies, UC Berkeley.
    10. Shladover, Steven & VanderWerf, Joel & Miller, Mark A. & Kourjanskaia, Natalia & Krishnan, Hariharan, 2001. "Development and Performance Evaluation of AVCSS Deployment Sequences to Advance from Today's Driving Environment to Full Automation," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt33w2d55j, Institute of Transportation Studies, UC Berkeley.
    11. van den Berg, Vincent A.C. & Verhoef, Erik T., 2016. "Autonomous cars and dynamic bottleneck congestion: The effects on capacity, value of time and preference heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 43-60.
    12. Qu, Xiaobo & Wang, Shuaian & Zhang, Jin, 2015. "On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 91-102.
    13. Chen, Danjue & Ahn, Soyoung & Chitturi, Madhav & Noyce, David A., 2017. "Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 196-221.
    14. Georges M. Arnaout & Jean-Paul Arnaout, 2014. "Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(2), pages 186-199, March.
    15. (Sean) Qian, Zhen & Li, Jia & Li, Xiaopeng & Zhang, Michael & Wang, Haizhong, 2017. "Modeling heterogeneous traffic flow: A pragmatic approach," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 183-204.
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