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Closed-form multiclass cell transmission model enhanced with overtaking, lane-changing, and first-in first-out properties

机译:具有超车,变道和先进先出特性的封闭式多类小区传输模型

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摘要

A novel multiclass macroscopic model is proposed in this article. In order to enhance first in, first-out property (FIFO) and transmission function in the multiclass traffic modeling, a new multiclass cell transmission model with FIFO property (herein called FM-CTM) is extended from its prior multiclass cell transmission model (M-CTM). Also, to enhance its analytical compactness and resultant computational convenience, FM-CTM is formulated in this paper as a set of closed-form matrix equations. The objective is to improve the accuracy of traffic state estimation by enforcing FIFO property when a fast vehicle cannot overtake a slow vehicle due to a limitation of a single-lane road. Moreover, the proposed model takes into account a different priority for vehicles of each class to move forward through congested road conditions, and that makes the flow calculation independent from their free-flow speeds. Some hypothetical and real-world freeway networks with a constant or varying number of lanes are selected to verify FM-CTM by comparing with M-CTM and the conventional CTM. Observed densities of VISSIM and real-world dataset of I-80 are selected to compare with the simulated densities from the three CTMs. The numerical results show that FM-CTM outperforms the other two models by 15% of accuracy measures in most cases. Therefore, the proposed model is expected to be well applicable to the road network with a mixed traffic and varying number of lanes. (C) 2017 Elsevier Ltd. All rights reserved.
机译:本文提出了一种新颖的多类宏观模型。为了增强多类流量建模中的先进先出属性(FIFO)和传输功能,从其先前的多类小区传输模型(M)扩展了具有FIFO属性的新的多类小区传输模型(以下称为FM-CTM)。 -CTM)。另外,为了提高分析的紧凑性和计算的便利性,本文将FM-CTM公式化为一组封闭形式的矩阵方程。目的是通过在快速车辆由于单车道的限制而不能超越慢速车辆时实施FIFO属性来提高交通状态估计的准确性。此外,所提出的模型考虑了每类车辆在拥挤的道路条件下前进的不同优先级,这使得流量计算独立于其自由流动速度。通过与M-CTM和常规CTM进行比较,选择一些假想的和真实世界的车道,这些车道具有恒定或变化的车道数以验证FM-CTM。选择VISSIM的观测密度和I-80的实际数据集,以与三个CTM的模拟密度进行比较。数值结果表明,在大多数情况下,FM-CTM优于其他两种模型的精度为15%。因此,该模型有望很好地适用于混合交通和车道数量变化的道路网络。 (C)2017 Elsevier Ltd.保留所有权利。

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