Research of car-following behavior is very important to alleviate traffic congestion and ensure traffic safety. To describe the car-following behavior of connected vehicles and restore the real driving environment, this paper presents an improved car-following model for connected vehicles by considering driver characteristic and speed information of multiple vehicles, which reflects the interaction between adjacent connected vehicles and the influence of driver characteristic on the stability of car-following. The linear stability analysis and nonlinear stability analysis prove that the improved model is effective and can promote the stability of traffic flow more than other models. In addition, the numerical simulations show that the new model performs better in eliminating disturbances than other models. Finally, the parameters of proposed model are calibrated based on NGSIM data. The results show that the new model performs well in car-following process, keeping a safe distance from the leader vehicle without sudden acceleration and deceleration. The new model also can predict the acceleration behavior of the leader vehicle after emergency brake. In summary, the model proposed in this paper can be used as active safety technology to prevent collision accidents, or as a user-defined function or model in the traffic simulation software, or as car-following strategy in driverless algorithms.
HuJLuoS. A car-following driver model capable of retaining naturalistic driving styles. J Adv Transport2020; 2020: 1–16.
2.
ZhangYLinQWangJ, et al. Car-following behavior model learning using timed automata. IFAC-PapersOnLine2017; 50(1): 2353–2358.
3.
JianMShiJ. Analysis of impact of elderly drivers on traffic safety using ANN based car-following model. Saf Sci2020; 122: 104536.
4.
MaLQuS. A sequence to sequence learning based car-following model for multi-step predictions considering reaction delay. Transport Res C-Emer2020; 120: 102785.
5.
XiaoXDuanHWenJ. A novel car-following inertia gray model and its application in forecasting short-term traffic flow. Appl Math Model2020; 87: 546–570.
6.
PipesLA. An operational analysis of traffic dynamics. J Appl Phys1953; 24(3): 274–281.
7.
NewellGF. Nonlinear effects in the dynamics of car following. Oper Res1961; 9(2): 209–229.
8.
YuSShiZ. An improved car-following model considering relative velocity fluctuation. Commun Nonlinear Sci2016; 36: 319–326.
9.
ChandlerREHermanRMontrollEW. Traffic dynamics: studies in car following. Oper Res1958; 6(2): 165–184.
BandoMHasebeKNakayamaA, et al. Dynamical model of traffic congestion and numerical simulation. Phys Rev E1995; 51(2): 1035–1042.
12.
PengGLuWHeH, et al. Nonlinear analysis of a new car-following model accounting for the optimal velocity changes with memory. Commun Nonlinear Sci2016; 40: 197–205.
13.
ZhouTSunDKangY, et al. A new car-following model with consideration of the prevision driving behavior. Commun Nonlinear Sci2014; 19(10): 3820–3826.
14.
ShiYYangLHaoS, et al. Clustered car-following strategy for improving car-following stability under cooperative vehicles infrastructure systems. IET Intell Transp Syst2016; 10(3): 141–147.
15.
PengYLiuSYuDZ. An improved car-following model with consideration of multiple preceding and following vehicles in a driver’s view. Phys A2020; 538: 122967.
16.
YuSZhaoXXuZ, et al. An improved car-following model considering the immediately ahead car’s velocity difference. Phys A2016; 461: 446–455.
17.
YaoZXuTJiangY, et al. Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time. Phys A2021; 561: 125218.
18.
YangDJinPPuY, et al. Stability analysis of the mixed traffic flow of cars and trucks using heterogeneous optimal velocity car-following model. Phys A2014; 395: 371–383.
19.
HelbingDTilchB. Generalized force model of traffic dynamics. Phys Rev E1998; 58(1): 133–138.
20.
TreiberMHelbingD. Explanation of observed features of self-organization in traffic flow. Arxiv preprint cond-mat/9901239, 1999.
21.
JiangRWuQZhuZ. Full velocity difference model for a car-following theory. Phys Rev E2001; 64: 017101.
22.
SunDZhangJZhaoM, et al. Effect of backward looking and velocity difference in an extended car following model. J Sichuan Univ2012; 49(1): 115–120.
23.
TangTQLiCYWuYH, et al. Impact of the honk effect on the stability of traffic flow. Phys A2011; 390(20): 3362–3368.
24.
YuSShiZ. An improved car-following model considering headway changes with memory. Phys A2015; 421: 1–14.
25.
YuSShiZ. Dynamics of connected cruise control systems considering velocity changes with memory feedback. Measurement2015; 64: 34–48.
26.
PengGHeHLuWZ. A new car-following model with the consideration of incorporating timid and aggressive driving behaviors. Phys A2016; 442: 197–202.
27.
PengGQingL. A new macro model of traffic flow by incorporating both timid and aggressive driving behaviors. Mod Phys Lett B2016; 30(29): 1650351.
28.
TangTShiWShangH, et al. A new car-following model with consideration of inter-vehicle communication. Nonlinear Dyn2014; 76(4): 2017–2023.
29.
SunDKangYYangS. A novel car following model considering average speed of preceding vehicles group. Phys A2015; 436: 103–109.
30.
KuangHXuZ-PLiX-L, et al. An extended car-following model accounting for the average headway effect in intelligent transportation system. Phys A2017; 471: 778–787.
31.
ZhuW-XZhangL-D. A new car-following model for autonomous vehicles flow with mean expected velocity field. Phys A2018; 492: 2154–2165.
32.
KuangHWangM-TLuFH, et al. An extended car-following model considering multi-anticipative average velocity effect under V2V environment. Phys A2019; 527: 121268.
33.
XiaoYLiuYSongX, et al. Linked vehicle model: a simple car-following model for automated vehicles. Proc IMechE, Part D: J Automobile Engineering2021; 235(2–3): 854–870.
34.
LiTNgoduyDHuiF, et al. A car-following model to assess the impact of V2V messages on traffic dynamics. Transportmetrica B2020; 8(1): 150–165.
35.
YuGLiuSShangguanQ. Optimization and evaluation of Platooning car-following models in a connected vehicle environment. Sustainability2021; 13(6): 3474.
36.
MaMMaGLiangS. Density waves in car-following model for autonomous vehicles with backward looking effect. Appl Math Model2021; 94: 1–12.
37.
LiYChenBZhaoH, et al. A car-following model for connected and automated vehicles with heterogeneous time delays under fixed and switching communication topologies. IEEE Trans Intell Transport Syst2022; 23: 14846–14858.
38.
WangSYuBWuM. MVCM car-following model for connected vehicles and simulation-based traffic analysis in mixed traffic flow. IEEE Trans Intell Transport Syst2022; 23: 5267–5274.
39.
ZhangGMaQPanD, et al. Study on the integration effect of multiple vehicles’ delayed velocities on traffic stability in intelligent transportation system environment. Eng Comput2021; 38(2): 929–940.
40.
UllebergPRundmoT. Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers. Saf Sci2003; 41(5): 427–443.
41.
SharmaS. Lattice hydrodynamic modeling of two-lane traffic flow with timid and aggressive driving behavior. Phys A2015; 421: 401–411.
42.
HossainMAKabirKMATanimotoJ. Improved car-following model considering modified backward optimal velocity and velocity difference with backward-looking effect. J Appl Math Phys2021; 9(2): 242–259.
