Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles
【Abstract】In order to better simulate the car-following characteristics of connected autonomous vehicles (CAV), based on the longitudinal control model (LCM), the LCM in the connected autonomous environment (C-LCM) was constructed when the influences of speed and acceleration of multiple preceding vehicles in V2V environment were considered. The stabilities of LCM and C-LCM were analyzed. The stability regions of two models were compared, and the influence of C-LCM on the traffic flow stability region at different communication distances was determined. Numerical simulation was designed to simulate the common traffic scenarios including acceleration and deceleration, and the car-following behavior characteristics of CAV in V2V environment were analyzed. The traffic flow safety levels at different communication distances and penetration rates of CAV were analyzed via simulation. A fundamental diagram model of mixed traffic flows at different penetration rates of CAV was constructed. Analysis result shows that the traffic flow stability region increases as the number of preceding vehicles rises. When only one preceding vehicle is considered, longer distance between the preceding vehicle and ego vehicle results in higher influence of velocity coefficient on the C-LCM stability region. The C-LCM can respond to the behaviors of multiple preceding vehicles in advance and simulate the dynamics characteristics of connected autonomous vehicles better. In the deceleration scenario, the speed overshoot decreases from 0.15 to 0.08, and the maximum speed delay decreases from 7.5 s to 4.9 s. In the acceleration scenario, the speed overshoot decreases from 0.07 to 0.04 and the minimum speed delay decreases from 3.5 s to 2.6 s. With the increase in CAV penetration rate, the safety level of traffic flow is enhanced. The highest safety level is achieved with four CAVs in communication distance, and TIT and TETdrop by 57.22% and 59.08%, respectively. As the CAV penetration rate goes up, the traffic capacity rises from 1 281 veh·h−1 to 3 204 veh·h−1. So the proposed C-LCM can describe the car-following characteristics of different vehicles to achieve the modeling of mixed traffic flow, decrease the complexity of mixed traffic flow, and provide a reference for the impact analysis of CAV on traffic flow.
【Keywords】 traffic safety; connected autonomous vehicles; numerical simulation; car-following model; traffic flow stability;
 GE J I, OROSZ G. Dynamics of connected vehicle systems with delayed acceleration feedback [J]. Transportation Research Part C: Emerging Technologies, 2014, 46: 46–64.
 MILANÉSV, SHLADOVER S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data [J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 285–300.
 QIN Yan-yan, WANG Hao, WANG Wei, et al. Fundamental diagram model of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles and adaptive cruise control vehicles [J]. China Journal of Highway and Transport, 2017, 30 (10): 127–136 (in Chinese).
 RAJAMANI R, SHLADOVER S E. An experimental comparative study of autonomous and cooperative vehicle follower control systems [J]. Transportation Research Part C: Emerging Technologies, 2001, 9 (1): 15–31.
 VAN NUNEN E, KWAKKERNAAT M R J A E, PLOEG J, et al. Cooperative competition for future mobility [J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (3): 1018–1025.
 YU Shao-wei, SHI Zhong-ke. An extended car-following model at signalized intersections [J]. Physica A: Statistical Mechanics and its Applications, 2014, 407: 152–159.
 YU Shao-wei, SHI Zhong-ke. The effects of vehicular gap changes with memory on traffic flow in cooperative adaptive cruise control strategy [J]. Physica A: Statistical Mechanics and its Applications, 2015, 428: 206–223.
 YU Shao-wei, SHI Zhong-ke. An improved car-following model considering relative velocity fluctuation [J]. Communications in Nonlinear Science and Numerical Simulation, 2016, 36: 319–326.
 QIN Yan-yan, WNAG Hao, RAN Bin. Car-following model of connected and autonomous vehicles considering multiple feedbacks [J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18 (3): 48–54 (in Chinese).
 TANG Tie-qiao, SHI Wei-fang, SHANG Hua-yan, et al. A new car-following model with consideration of inter-vehicle communication [J]. Nonlinear Dynamics, 2014, 76 (4): 2017–2023.
 GUO Lan-tian, ZHAO Xiang-mo, YU Shao-wei, et al. An improved car-following model with multiple preceding cars velocity fluctuation feedback [J]. Physica A: Statistical Mechanics and its Applications, 2017, 471: 436–444.
 LI Zhi-peng, LI Wen-zhong, XU Shang-zhi, et al. Stability analysis of an extended intelligent driver model and its simulations under open boundary condition [J]. Physica A: Statistical Mechanics and its Applications, 2015, 419: 526–536.
 JIN P J, YANG Da, RAN Bin, et al. Bidirectional control characteristics of general motors and optimal velocity car-following models [J]. Transportation Research Record, 2013 (2381): 110119.
 TALEBPOUR A, MAHMASSANI H S. Influence of connected and autonomous vehicles on traffic flow stability and throughput [J]. Transportation Research Part C: Emerging Technologies, 2016, 71: 143–163.
 JIN P J, ZHANG Guo-hui, WALTON C M, et al. Analyzing the impact of false accident cyber attacks on traffic flow stability in connected vehicle environment [C]//IEEE. 2013 International Conference on Connected Vehicles and Expo. New York: IEEE, 2013: 616–621.
 YAO Zhi-hong, HU Rong, WANG Yi, et al. Stability analysis and the fundamental diagram for mixed connected automated and human-driven vehicles [J]. Physica A: Statistical Mechanics and its Applications, 2019, 533: 121931-1–16.
 QIN Yan-yan, WANG Hao, RAN Bin. Car-following modeling for CACC vehicles and mixed traffic flow analysis [J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18 (2): 60–65. (in Chinese)
 WNAG Qi, XIE Na, HOU De-zao, et al. Effects of adaptive cruise control and cooperative adaptive cruise control on traffic flow [J]. China Journal of Highway and Transport, 2019, 32 (6): 188–197, 205 (in Chinese).
 YUAN Yao-ming, JIANG Rui, HU Mao-bin, et al. Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles: a hybrid modelling approach [J]. Physica A: Statistical Mechanics and its Applications, 2009, 388 (12): 2483–2491.
 QIN Yan-yan, WANG Hao, RAN Bin. Stability analysis of connected and automated vehicles to reduce fuel consumption and emissions [J]. Journal of Transportation Engineering, Part A: Systems, 2018, 144 (11): 04018068-1–9.
 ZHU Wen-xing, ZHANG H M. Analysis of mixed traffic flow with human driving and autonomous cars based on car-following model [J]. Physica A: Statistical Mechanics and its Applications, 2018, 496: 274–285.
 YE Lan-hang, YAMAMOTO T. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput [J]. Physica A: Statistical Mechanics and its Applications, 2018, 512: 588–597.
 NGODUY D. Analytical studies on the instabilities of heterogeneous intelligent traffic flow [J]. Communications in Nonlinear Science and Numerical Simulation, 2013, 18 (10): 2699–2706.
 SUN Jie, ZHENG Zu-duo, SUN Jian. Stability analysis methods and their applicability to car-following models in conventional and connected environments [J]. Transportation Research Part B: Methodological, 2018, 109: 212–237.
 QIN Yan-yan, WANG Hao, WANG Wei, et al. Review of car-following models of adaptive cruise control [J]. Journal of Traffic and Transportation Engineering, 2017, 17 (3): 121–130 (in Chinese).
 NI Dai-heng, LEONARD J D, JIA Chao-qun, et al. Vehicle longitudinal control and traffic stream modeling [J]. Transportation Science, 2016, 50 (3): 1016–1031.
 NI Dai-heng, HSIEH H K, JIANG Tao. Modeling phase diagrams as stochastic processes with application in vehicular traffic flow [J]. Applied Mathematical Modelling, 2018, 53: 106–117.
 ZHENG Liang, HE Zheng-bing, HE Tian. A flexible traffic stream model and its three representations of traffic flow [J]. Transportation Research Part C: Emerging Technologies, 2017, 75: 136–167.
 MINDERHOUD M M, BOVY P H L. Extended time-to-collision measures for road traffic safety assessment [J]. Accident Analysis and Prevention, 2001, 33 (1): 89–97.