Rear-end collisions account for a significant proportion of traffic accidents, highlighting the importance of analyzing drivers’ car-following (CF) behavior for traffic safety research. Previous studies, whether based on modeling or data analysis, have primarily focused on the influence of the lead vehicle’s (LV) state on a driver’s CF behavior. However, in real-world driving, the information perceived by drivers is not limited to the LV; drivers also receive information about the following vehicle (FV), and their behavior may be influenced accordingly. Thus, this paper investigates the influence of FV states on the CF behavior of the ego-vehicle, specifically the impact of tailgating (where the time headway between the FV and the ego-vehicle is less than 2 s) on the behavior of the ego-vehicle. The results indicate that when being tailgated, ego-vehicles tend to keep a smaller time headway to their LVs, confirming the nudging effects of the FV in CF events.
AhmedH. U.HuangY.LuP. (2021). A review of car-following models and modeling tools for human and autonomous-ready driving behaviors in micro-simulation. Smart Cities, 4(1), 314–335.
3.
AlmutairiM. S.AlmutairiK.ChiromaH. (2023). Hybrid of deep recurrent network and long short term memory for rear-end collision detection in fog based internet of vehicles. Expert Systems with Applications, 213, 119033.
4.
Alonso-MoraJ.BreitenmoserA.BeardsleyP.SiegwartR. (2012, May). Reciprocal collision avoidance for multiple car-like robots. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (pp. 360–366). IEEE.
5.
BellaF.RussoR. (2011). A collision warning system for rear-end collision: A driving simulator study. Procedia-Social and Behavioral Sciences, 20, 676–686.
FlannaganM. J. (2005). Distance perception with a camera-based rear vision system in actual driving (UMTRI-2005-38). University of Michigan Transportation Research Institute.
8.
GunawanF. E. (2012). Two-vehicle dynamics of the car-following models on realistic driving condition. Journal of Transportation Systems Engineering and Information Technology, 12(2), 67–75.
9.
HuangC.WenX.HeD. (2024). Characteristics of rear-end collisions: a comparison between automated driving system-involved crashes and advanced driving assistance system-involved crashes. Transportation Research Record, 2678(7), 771–782.
10.
JiangR.WuQ.ZhuZ. (2001). Full velocity difference model for a car-following theory. Physical Review E, 64(1), 017101.
LeeD. K.InJ.LeeS. (2015). Standard deviation and standard error of the mean. Korean Journal of Anesthesiology, 68(3), 220–223.
13.
MaM.WangW.LiangS.XiaoJ.WuC. (2023). Improved car-following model for connected vehicles considering backward-looking effect and motion information of multiple vehicles. Journal of Transportation Engineering, Part A: Systems, 149(2), 04022148.
NakajimaJ. (2011). Time-varying parameter VAR model with stochastic volatility: An overview of methodology and empirical applications. Monetary and Economic Studies, 29, 107–142.
16.
PoudelB.LiW. (2023). Beyond simulated drivers: Evaluating the impact of real-world car-following in mixed traffic control. arXiv Preprint arXiv:2311.12261.
17.
StephensA.StephanK.CrottyR.O'HernS.BjörklundG. (2023). Too close for comfort: A mixed methods study to understand self-reported tailgating using the theory of planned behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 93, 11–22.
18.
SwaroopD.RajagopalK. (2001). A review of constant time headway policy for automatic vehicle following. In Proceedings of the 2001 IEEE Intelligent Transportation Systems Conference (ITSC) (Cat. No. 01TH8585, pp. 65–69). IEEE.
19.
TreiberM.HenneckeA.HelbingD. (2000). Congested traffic states in empirical observations and microscopic simulations. Physical Review E, 62(2), 1805.
20.
WangZ.HuangH.TangJ.MengX.HuL. (2022). Velocity control in car-following behavior with autonomous vehicles using reinforcement learning. Accident Analysis & Prevention, 174, 106729.
21.
WenX.CuiZ.JianS. (2022). Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset. Accident Analysis & Prevention, 172, 106689.
22.
WenX.JianS.HeD. (2023). Modeling the effects of autonomous vehicles on human driver car-following behaviors using inverse reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 24(12), 13903–13915.
23.
YanS.HuangC.XieW.HeD. (2023). HMI design for chain-braking event based on V2V communication. In Adjunct proceedings of the 15th international conference on automotive user interfaces and interactive vehicular applications (AutomotiveUI ’23 Adjunct) (pp. 105–110). Association for Computing Machinery.
24.
Zadka-PeerS.RosenbloomT. (2024). Targeted nudging for speeding behavior: The influence of interpersonal characteristics on responses to in-vehicle road nudges. Accident Analysis & Prevention, 204, 107638.
25.
ZhuM.WangX.WangY. (2018). Human-like autonomous car-following model with deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 97, 348–368.
