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TDGL and mKdV equations for an extended car-following model with the consideration of driver's memory

机译:TDGL和MKDV方程为延长汽车跟踪模型,考虑到驾驶员的内存

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This paper presents an extended car-following model by taking driver's memory and the full velocity difference into the original optimal velocity model (OVM). The stability condition of this model is obtained by using linear stability theory. The TDGL equation and the mKdV equation are derived from nonlinear analysis. The energy consumption of new car-following models considering the driver's memory is discussed. Furthermore, the new car-following model is investigated in detail by numerical methods. Both analytical and simulation results show that the extended following car-following model will not only suppress the traffic congestion but also reduce energy consumption. (C) 2018 Elsevier B.V. All rights reserved.
机译:本文通过将驾驶员的内存和完全速度差呈现为原始最佳速度模型(OVM)来介绍一个扩展的汽车跟踪模型。 通过使用线性稳定性理论获得该模型的稳定性条件。 TDGL方程和MKDV方程源自非线性分析。 讨论了考虑驾驶员记忆的新车之后模型的能耗。 此外,通过数值方法详细研究了新的汽车之后模型。 分析和仿真结果表明,延长后的汽车之后的模型将不仅抑制交通拥堵,还可以降低能耗。 (c)2018年elestvier b.v.保留所有权利。

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