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Formalizing the heterogeneity of the vehicle-driver system to reproduce traffic oscillations

机译:正式地形成车辆驱动系统的异质性以再现交通振荡

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Road traffic congestion is the result of various phenomena often of random nature and not directly observable with empirical experiments. This makes it difficult to clearly understand the empirically observed traffic instabilities. The vehicles' acceleration/deceleration patterns are known to trigger instabilities in the traffic flow under congestion. It has been empirically observed that free-flow pockets or voids may arise when there is a difference in the speeds and the spacing between the follower and the leader increases. During these moments, the trajectory is dictated mainly by the characteristics of the vehicle and the behaviour of the driver and not by the interactions with the leader. Voids have been identified as triggers for instabilities in both macro and micro level, which influence traffic externalities such as fuel consumption and emis-sions. In the literature, such behaviour is usually reproduced by injecting noise to the results of car-following models in order to create fluctuations in the instantaneous vehicles' acceleration.This paper proposes a novel car-following approach that takes as input the driver and the vehicle characteristics and explicitly reproduces the impact of the vehicle dynamics and the driver's behaviour by adopting the Microsimulation Free-flow aCceleration (MFC) model. The congested part of the model corresponds to the Lagrangian discretization of the LWR model and guarantees a full consistency at the macroscopic scale with congested waves propagating accordingly to the first-order traffic flow theory.By introducing naturalistic variation in the driving styles (timid and aggressive drivers) and the vehicle characteristics (specification from different vehicle models), the proposed model can reproduce realistic traffic flow oscillations, similar to those observed empirically. An advantage of the proposed model is that it does not require the injection of any noise in the instantaneous vehicle accelerations.The proposed methodology has been tested by studying a) the traffic flow oscillations produced by the model in a one-lane road uphill simulation scenario, b) the ability of the model to reproduce car-following instabilities observed in three car-following trajectory datasets and c) the ability of the model to produce realistic fuel consumption estimates. The results prove the robustness of the proposed model and the ability to describe traffic flow oscillations as a consequence of the combination of driving style and vehicle's technical specifications.
机译:道路交通挤塞是各种现象的结果,通常是随机性的,而不是直接观察到实证实验。这使得很难清楚地了解经验观察到的交通不稳定。已知车辆的加速/减速模式触发在拥塞下的交通流量中的不稳定性。已经经验观察到,当速度差和从动件和领导者之间的间距增加时,可以出现自由流动口袋或空隙。在这些时刻,轨迹主要由车辆的特征和驾驶员的行为而不是与与领导者的相互作用决定。已经将空隙识别为宏观和微观水平的不稳定性的触发,这影响了燃料消耗和EMIS-SION等交通外部性。在文献中,这种行为通常通过向汽车跟踪模型的结果注入噪声来复制,以便在瞬时车辆加速器中产生波动。本文提出了一种新的汽车之后的方法,其采用输入驾驶员和车辆。通过采用微仿自动流量加速(MFC)模型,特征和明确地再现车辆动态和驾驶员行为的影响。该模型的拥塞部分对应于LAWRANGIAN对LWR模型的离散化,并保证与相应的拥挤波相应传播到一阶交通流理论的宏观尺度的完全一致性。在驾驶风格中引入自然变化(胆小和侵略性司机)和车辆特性(来自不同车型的规格),所提出的模型可以再现现实的交通流量振荡,类似于经验观察的人。所提出的模型的优点是它不需要在瞬时车辆加速器中注入任何噪声。通过研究A)通过在一条车道道路上坡仿真方案中产生的交通流振荡来测试所提出的方法B)模型在三个车次轨迹数据集中逐步销售汽车之后不稳定性的能力,以及C)模型产生现实燃料消耗估计的能力。结果证明了所提出的模型的鲁棒性以及描述交通流量振荡的能力,因为驾驶风格和车辆技术规范的组合。

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