A New Car Following Model： Comprehensive Optimal Velocity Model

In this paper, we present a new car-following model, i.e. comprehensive optimal velocity model (COVM), whose optimal velocity function not only depends on the following distance of the preceding vehicle, but also depends on the velocity difference with preceding vehicle. Simulation results show that COVM is an improvement over the previous ones theoretically. Then, the stability condition of the model is obtained by the linear stability analysis, which has shown that the model could obtain a bigger stable region thanprevious models in the phase diagram. Through the nonlinear analysis, the Burgers, Korteweg-de Vries (KdV) and modified KdV (mKdV) equations are derived for the triangular shock wave, the soliton wave, and the kink-antikink soliton wave. At the same time, numerical simulations are also carried out to show that the model could simulate these density waves.     

基于改进格子气模型的对向行人流分层现象的随机性研究

在考虑行人视野范围的随机偏走格子气模型基础上, 引入行人对前方开阔区域的移动偏好特性, 提出改进的格子气模型, 对通道内对向行人流进行仿真研究. 模型再现了对向行人流在不同密度下出现的3种演化过程, 发现了行人密度与对向行人流分层现象的形成具有随机性, 以及统计了概率的变化趋势, 同时分析了分层现象形成概率与系统几何尺寸参数、移动强度参数、右行人流比例参数和视野范围参数等的关系. 分析结果表明, 改进的模型能够再现实际低密度下对向行人流不会出现分层现象的特性. 根据分层形成的概率, 可将对向行人流的密度分为5个区间, 不同区间的行人流演化过程各有差异. 模型和分析结果对理解对向行人流的动态演化过程, 提高通道内对向行人流的走行效率有一定帮助.     

A new car-following model considering velocity anticipation

The full velocity difference model proposed by Jiang et al. [2001 Phys. Rev. E 64 017101] has been improved by introducing velocity anticipation. Velocity anticipation means the follower estimates the future velocity of the leader. The stability condition of the new model is obtained by using the linear stability theory. Theoretical results show that the stability region increases when we increase the anticipation time interval. The mKdV equation is derived to describe the kink--antikink soliton wave and obtain the coexisting stability line. The delay time of car motion and kinematic wave speed at jam density are obtained in this model. Numerical simulations exhibit that when we increase the anticipation time interval enough, the new model could avoid accidents under urgent braking cases. Also, the traffic jam could be suppressed by considering the anticipation velocity. All results demonstrate that this model is an improvement on the full velocity difference model.     

Flow difference effect in the two-lane lattice hydrodynamic model

By introducing a flow difference effect, a modified lattice two-lane traffic flow model is proposed, which is proved to be capable of improving the stability of traffic flow. Both the linear stability condition and the kink-antikink solution derived from the modified Korteweg-de Vries (mKdV) equation are analyzed. Numerical simulations verify the theoretical analysis. Furthermore, the evolution laws under different disturbances in the metastable region are studied.     

Flow difference effect in the lattice hydrodynamic model

In this paper, a new lattice hydrodynamic model based on Nagatani's model [Nagatani T 1998 Physica A 261 599] is presented by introducing the flow difference effect. The stability condition for the new model is obtained by using the linear stability theory. The result shows that considering the flow difference effect leads to stabilization of the system compared with the original lattice hydrodynamic model. The jamming transitions among the freely moving phase, the coexisting phase, and the uniform congested phase are studied by nonlinear analysis. The modified KdV equation near the critical point is derived to describe the traffic jam, and kink--antikink soliton solutions related to the traffic density waves are obtained. The simulation results are consistent with the theoretical analysis for the new model.     

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm (OCA) with that of a heuristic rules-based algorithm (HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that (i) capacity under the HRA (denoted as C_H) is smaller than capacity under the OCA; (ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA; (iii) when the inflow rate is smaller than C_H, the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost; (iv) when the inflow rate is larger than C_H, the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.