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Queue length estimation at isolated intersections based on intelligent vehicle infrastructure cooperation systems

机译:基于智能车辆基础设施协作系统的孤立路口排队长度估计

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With the advance of intelligent vehicle infrastructure cooperation systems (i-VICS), many traffic parameters can be inferred given data from the OBU (On Board Unit of i-VICS)-equipped vehicles to calculate some information used in the modern urban traffic management. As a typical application, the estimation of investigated queue length at isolated intersections is proposed in the paper. Firstly, the queue length estimation at isolated intersections is explored to be transformed into the problem of deriving the number of queued vehicles. Two models, the improved and advanced interpolation methods, are then introduced to the condition of single cycle and multiple cycles, respectively. The microscopic simulation software VISSIM is adopted to evaluate the effect of the derived models. Its analysis shows that, under the condition of single cycle, the mean absolute error (MAE) of the improved interpolation method is less than 2 vehicles when the equipped rate of i-VICS OBU is higher than 50% and the maximum MAE with 30% equipped rate will be no more than 4 vehicles. On the other hand, under the condition of multiple cycles, the MAE of the advanced interpolation method with very low OBU-equipped rates will be less than 6 vehicles. Finally, numerical results of proposed approach have shown the relations of MAE with the volume-to-capacity ratio and the OBU-equipped rate.
机译:随着智能车辆基础设施协作系统(i-VICS)的发展,从配备OBU(i-VICS的车载单元)的数据中可以推断出许多交通参数,以计算一些用于现代城市交通管理的信息。作为一种典型的应用,本文提出了对孤立路口的调查队列长度的估计。首先,探索了孤立路口的排队长度估计,将其转化为推导排队车辆数量的问题。然后分别将两种模型(改进和高级插值方法)分别引入到单循环和多循环条件下。采用微观仿真软件VISSIM对所推导模型的效果进行评估。分析表明,在单周期条件下,当i-VICS OBU的装配率大于50%且最大MAE为30时,改进插值方法的平均绝对误差(MAE)小于2辆。 \%装备率将不超过4辆。另一方面,在具有多个OBU的情况下,采用先进的插值方法的OE极低,其MAE将少于6辆车。最后,所提出方法的数值结果表明了MAE与体积-容量比和配备OBU的速率之间的关系。

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