Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs

Abstract

With the development of intelligent connected vehicles (ICVs) and communication technology, collaborative operation among vehicles will become the trend of the future. Thus, traffic flow will be mixed with manual driving vehicles and ICVs. A mixed traffic flow is a traffic flow state lying between autonomous and manual traffic flows. In order to describe the car-following characteristics in a mixed traffic flow, the cooperative adaptive cruise control (CACC) car-following model and the intelligent driver model (IDM) were adopted. The car-following characteristics of different platoons from these two car-following models were analyzed. The CACC mixing ratio was used to describe the mixed traffic flow. The fixed states and disturbance states of the car-following platoons were simulated. The fixed states can be divided into three categories: the steady state, acceleration state, and deceleration state. The effects of different car-following cases and different mixing ratios on mixed traffic flow in different states were discussed. The results show that (1) in the steady state with a smaller mixing ratio, the operating speed and traffic volume of the mixed traffic flow were positively correlated. The overall traffic volume decreased with the increase in the mixing ratio, and the gap gradually narrowed. At a larger mixing ratio, the operating speed and traffic volume were negatively correlated. The overall traffic volume increased with the increase in the mixing ratio. (2) In the acceleration state, the maximum traffic volume in the platoon and the optimal mixing ratio were linearly related to the acceleration. (3) In the deceleration state with a fixed mixing ratio, the traffic volume decreased with the increase in the deceleration, with slight differences in the changing trend of the volume of the mixed flow. Under disturbances, the mixed traffic volume was positively correlated with the mixing ratio, i.e., at a larger mixing ratio, the anti-interference ability of the mixed traffic flow was higher.

Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection

“…The input of the model is the speed of the car in front, the distance between the car and the car in front and the speed of the car. The output is the acceleration of the car [34]. The IDM is suitable to be applied to the research of this paper, and it is expressed as follows:…”

“…The channelization diagram of the Jinjiling-Jiefang intersection is shown in Figure 4, the phase scheme of the signal control is shown in Table 1 and the traffic volume and the number of lanes at the intersection are shown in Table 2. The parameters of the CAV simulation set in this study refer to those from existing studies [33,34], and the main parameters of the simulation calculation are shown in Table 3.…”

“…The chan the Jinjiling-Jiefang intersection is shown in Figure 4, the phase sch trol is shown in Table 1 and the traffic volume and the number of la are shown in Table 2. The parameters of the CAV simulation set in t from existing studies [33,34], and the main parameters of the simu shown in Table 3. The ratio of a tram full load 0.4-0.9 The maximum acceleration of CAV (m/s 2 ) 2.5…”

Research on the characteristics of mixed traffic flow in an intelligent connected environment

“…Chen Jie et al [3] found that the following stability of traffic flow is affected by the values of the number of communicating vehicles and sensitivity of the networked vehicles, and the higher the penetration rate of the networked vehicles, the greater the improvement of the accessibility. Song Chengju et al [4] analyzed the following characteristics of different car-following models and carried out simulation studies using the CACC model for the stationary and perturbed states of the following queue, and the results showed that, in the case of a higher penetration rate of CAVs, the speed of traffic flow is negatively correlated with the volume of traffic and with the increase in penetration rate, the traffic volume in general is on the rise. Hou Peiguo et al [5] based on the Nasch cellular automata traffic flow model, considering the psychological factors of traditional drivers and driving behavior differences, to establish a manual driving-CACC automatic driving mixed traffic flow model, and concluded that with the increase in the proportion of automatic driving vehicles, traffic flow volume, average speed increases, and other conclusions.…”