Stability and Environmental Analysis of Mixed Traffic Flow – Using the Markov Probabilistic Theory
Abstract
The rapid growth of CAV (Connected and Automated Vehicle) market penetration highlights the need to gain insight into the overall stability of mixed traffic flows in order to better deploy CAVs. Several studies have examined the modelling process and stability analysis of traffic flow in a mixed traffic environment without considering its inner spatial distribution. In this paper, an innovative Markov chain-based model is established for integrating the spatial distribution of mixed traffic flow in the model process of car-following behaviour. Then the linear stability analysis of the mixed traffic flow is conducted for different CAV market penetration rates, different CAV platoon strength and different cooperation efficiency between two continuous vehicles. Moreover, several simulations under open boundary conditions in multiple scenarios are performed to explicate how CAV market penetration rate, platoon strength and cooperation efficiency jointly influence the stability performance of the mixed traffic flow. The results reveal that the performance of this mixed traffic flow stability could be strengthened in these three factors. In addition to stability, an investigation of the fuel consumption and emission reduction under different market penetration rates and the platoon strength of CAVs are explored, suggesting that substantial potential fuel consumption and emission could be reduced under certain scenarios.
References
Ran B, Cheng Y, Li S, Ding F, Jin J, Chen X, et al. Connected automated vehicle highway systems and methods. Google Patents; 2019.
Wu J, Wang Y, Wang L, Shen Z, Yin C. Consensus-Based Platoon Forming for Connected Autonomous Vehicles. IFAC-PapersOnLine. 2018;51(31): 801-6.
Seraj M, Li J, Qiu Z. Modelling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making. Journal of Advanced Transportation. 2018;2018.
Bang S, Ahn S. Platooning strategy for connected and autonomous vehicles: Transition from light traffic. Transportation Research Record. 2017;2623(1): 73-81.
Gong S, Du L. Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles. Transportation Research Part B: Methodological. 2018;116: 25-61.
Shi Y, He Q, Huang Z. Capacity Analysis and Cooperative Lane Changing for Connected and Automated Vehicles: Entropy-Based Assessment Method. Transportation Research Record. 2019;0361198119843474.
Virdi N, Grzybowska H, Waller ST, Dixit V. A safety assessment of mixed fleets with Connected and Autonomous Vehicles using the Surrogate Safety Assessment Module. Accident Analysis & Prevention. 2019;131: 95-111.
Lu C, Dong J, Hu L. Energy-Efficient Adaptive Cruise Control for Electric Connected and Autonomous Vehicles. IEEE Intelligent Transportation Systems Magazine. 2019;11(3): 42-55.
Lu C, Aakre A. A new adaptive cruise control strategy and its stabilization effect on traffic flow. European Transport Research Review. 2018;10(2): 49.
Zhou Y, Wang M, Ahn S. Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability. Transportation Research Part B: Methodological. 2019;128: 69-86.
Qin Y, Wang H, Ran B. Impact of Connected and Automated Vehicles on Passenger Comfort of Traffic Flow with Vehicle-to-vehicle Communications. KSCE Journal of Civil Engineering. 2019;23(2): 821-32.
Wang M. Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings. Transportation Research Part C: Emerging Technologies. 2018;91(April 2017): 276-95.
Alonso MR, Peralta I, Monti D, Martino R, Anesini C. Stability of an Aqueous Extract of Larrea divaricata Cav. during a Simulated Digestion Process. Phytotherapy Research. 2017;31(11): 1708-14.
Ngoduy D. Analytical studies on the instabilities of heterogeneous intelligent traffic flow. Communications in Nonlinear Science and Numerical Simulation. 2013;18(10): 2699-706. Available from: doi:10.1016/j.cnsns.2013.02.018
Seiler P, Pant A, Hedrick K. Disturbance Propagation in Vehicle Strings. IEEE Transactions on Automatic Control. 2004;49(10): 1835-41.
Ran B, Tsao HJ. Traffic Flow Analysis for An Automated Highway System. 75th TRB Annual Meeting. 1995;(960232).
Ngoduy D. Effect of the car-following combinations on the instability of heterogeneous traffic flow. Transportmetrica B. 2015;3(1): 44-58.
Talebpour A, Mahmassani HS. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies. 2016;71: 143-63. Available from: doi:10.1016/j.trc.2016.07.007
Darbha S, Rajagopal KR. Intelligent cruise control systems and traffic flow stability. Transportation Research Part C: Emerging Technologies. 1998;7(6): 329-52.
Gu H, Zhang J, Jin PJ, Ran B. Stability analysis of lead- vehicle control model in cooperative adaptive cruise control platoon within heterogeneous traffic flow. Journal of Southeast University. 2018;34: 386-93.
Lu C, Dong J, Hu L, Liu C. An Ecological Adaptive Cruise Control for Mixed Traffic and Its Stabilization Effect. IEEE Access. 2019;7: 81246-56.
Wang J, Peeta S, He X. Multiclass traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles. Transportation Research Part B: Methodological. 2019;126: 139-68.
Ghiasi A, Li X, Ma J. A mixed traffic speed harmonization model with connected autonomous vehicles. Transportation Research Part C: Emerging Technologies. 2019;104:210-33.
McConky K, Rungta V. Don’t pass the automated vehicles!: System level impacts of multi-vehicle CAV control strategies. Transportation Research Part C: Emerging Technologies. 2019;100: 289-305.
Ghiasi A, Hussain O, Sean Z, Li X. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method. Transportation Research Part B. 2017;106: 266-92. Available from: doi:10.1016/j.trb.2017.09.022
Li Z, Li W, Xu S, Qian Y. Stability analysis of an extended intelligent driver model and its simulations under open boundary condition. Physica A: Statistical Mechanics and its Applications. 2015;419: 526-36. Available from: doi:10.1016/j.physa.2014.10.063
Al Alam A, Gattami A, Johansson KH. An experimental study on the fuel reduction potential of heavy duty vehicle platooning. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; 2010. p. 306-11.
Tsugawa S, Kato S, Aoki K. An automated truck platoon for energy saving. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2011. p. 4109-14. Available from: http://ieeexplore.ieee.org/document/6094549/
Lammert MP, Duran A, Diez J, Burton K, Nicholson A. Effect of Platooning on Fuel Consumption of Class 8 Vehicles Over a Range of Speeds, Following Distances, and Mass. SAE International Journal of Commercial Vehicles. 2014;7(2): 626-639. Available from: http://papers.sae.org/2014-01-2438/
Treiber M, Hennecke A, Helbing D. Microscopic simulation of congested traffic. Traffic and Granular Flow ’99; 2000. p. 365-76.
Milanés V, Shladover SE. Modelling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transportation Research Part C: Emerging Technologies. 2014;48: 285-300.
Wilson RE. Mechanisms for spatio-temporal pattern formation in highway traffic models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2008;366(1872): 2017-32.
Ye L, Yamamoto T. Modelling connected and autonomous vehicles in heterogeneous traffic flow. Physica A. 2018;490: 269-77. Available from: doi:10.1016/j.physa.2017.08.015
Ahn K, Rakha H, Trani A, Van Aerde M. Estimating Vehicle Fuel Consumption and Emissions based on Instantaneous Speed and Acceleration Levels. Journal of Transportation Engineering. 2002;128(2): 182-90. Available from: doi:10.1061/%28ASCE%290733-947X%282002%29128%3A2%28182%29
Hausberger S, Rodler J, Sturm P, Rexeis M. Emission factors for heavy-duty vehicles and validation by tunnel measurements. Atmospheric Environment. 2003;37(37): 5237-45.
Barth M, An F, Younglove T, Scora G, Levine C, Ross M, et al. Development of a Comprehensive Modal Emissions Model. National Cooperative Highway Research Program NCHRP; 2000. Available from: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w122.pdf
Li X, Cui J, An S, Parsafard M. Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation. Transportation Research Part B: Methodological. 2014;70(1): 319-39. Available from: doi:10.1016/j.trb.2014.09.014
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