In this paper, an extended car-following model is proposed to simulate traffic flow with consideration of incorporating the effects of driver’s memory and mean expected velocity field in ITS (i.e. intelligent transportation system) environment. The neutral stability condition of the new model is derived by applying the linear stability theory. Compared with the optimal velocity model and the full velocity difference model, the stability region of the new model can be significantly enlarged on the phase diagram, and the anticipating motion information of more vehicles ahead can further enhance traffic stability. Furthermore, the mean expected velocity field effect plays a more important role than that of driver’s memory effect in improving the stability of traffic flow. Nonlinear analysis is also conducted by using the reductive perturbation method, and the mKdV equation near the critical point is obtained to describe the evolution properties of traffic density waves. Numerical simulation results show that the coupling effect of driver’s memory and the mean expected velocity field can suppress the traffic jam effectively, which is in good agreement with the analytical result.

中文翻译：

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ITS环境下驾驶员记忆和平均期望速度场影响的扩展跟驰模型

在本文中，提出了一种扩展的跟驰模型来模拟交通流，并考虑了在ITS（即智能交通系统）环境中结合驾驶员记忆和平均预期速度场的影响。新模型的中性稳定条件是应用线性稳定理论推导出来的。与最优速度模型和全速度差模型相比，新模型的稳定区域在相图上可以显着扩大，并且更多的前方车辆的预测运动信息可以进一步增强交通稳定性。此外，平均期望速度场效应在提高交通流稳定性方面的作用比驾驶员记忆效应更重要。非线性分析也通过使用减少扰动方法进行，并得到临界点附近的mKdV方程来描述交通密度波的演化特性。数值模拟结果表明，驾驶员记忆力与平均期望速度场的耦合作用能够有效抑制交通拥堵，与分析结果吻合较好。