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Xuegang Ban Peng Hao Zhanbo Sun 《Transportation Research Part C: Emerging Technologies》2011,19(6):1133-1156
We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation. 相似文献
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With the progress of information and sensing technologies, estimating vehicular queue length at signalized intersections becomes feasible and has attracted considerable attention. The existing studies provided a solid theoretical foundation for the estimation; however, the studies have some restrictions or limitations more or less. This paper presents a new methodology for estimating vehicular queue length at signalized intersections using multi-source detection data under both undersaturated and oversaturated conditions. The methodology applies the shockwave theory to model queue dynamics. Using data from probe vehicles and point detectors, analytical formulations for calculating the maximum and minimum (residual) queue lengths of each cycle are developed. Ground truth data were collected from numerical experiments conducted at two intersections in Shanghai, China, to verify the proposed methodology. It is found that the methodology has mean absolute percentage errors of 17.09% and 12.28%, respectively, for maximum queue length estimation in two tests, which are reasonably effective. However, the methodology is unsatisfactory in estimating the residual queue length. Other limitations of the proposed models and algorithms are also discussed in the paper. 相似文献
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简单输入输出模型与冲击波分析是研究交通瓶颈问题的两种常用方法,本文着重分析两者的一致性。在数学推导中考虑到达需求与流出率的变化时,两者对于排队和延误的预测结果是一致的。在不同的交通和边界条件下,两者在拥堵形成至消散的任意时刻所得排队长度,拥堵时间等结果均是一致的。以往认为两种方法不一致的研究忽略了一些与背景交通密切相关的基本因素。实例分析说明正是这些基本因素使得这两种分析方法在不同流量-密度关系下都具有一致性。 相似文献
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车辆轨迹蕴含着大量丰富的交通流时空信息,对于全面解构城市交通路网运行具有至关重要的意义.传统车辆轨迹重构模型大多基于定点线圈检测数据或者浮动车轨迹数据作为输入数据,并且普遍未考虑过饱和交通状态.本文提出了一种基于车辆身份感知数据的车辆路段轨迹重构方法,通过构建一种绿灯相位回溯框架,基于交通流激波理论分段重构车辆行程轨迹,每次回溯过程包含两个主要步骤,即估计车辆状态和分状态重构车辆行程轨迹;然后在Paramics 微观交通仿真平台上对本方法模型的准确性进行了验证.结果表明,该方法在各种饱和状态下均能达到令人满意的应用效果. 相似文献
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车辆轨迹蕴含着大量丰富的交通流时空信息,对于全面解构城市交通路网运行具有至关重要的意义.传统车辆轨迹重构模型大多基于定点线圈检测数据或者浮动车轨迹数据作为输入数据,并且普遍未考虑过饱和交通状态.本文提出了一种基于车辆身份感知数据的车辆路段轨迹重构方法,通过构建一种绿灯相位回溯框架,基于交通流激波理论分段重构车辆行程轨迹,每次回溯过程包含两个主要步骤,即估计车辆状态和分状态重构车辆行程轨迹;然后在Paramics 微观交通仿真平台上对本方法模型的准确性进行了验证.结果表明,该方法在各种饱和状态下均能达到令人满意的应用效果. 相似文献
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车流波现象是城市交通流中一种不稳定状态.当城市快速路出现车流波现象时,车流运行的平稳性会明显降低,同时燃油消耗量和尾气排放量却随之上升.因此,对车流波状态下尾气排放进行研究是十分必要的,对于整个城市大气环境的改善意义重大,也更加有利于和谐、可持续交通的发展. 相似文献
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Secondary crash (SC) occurrences are non-recurrent in nature and lead to significant increase in traffic delay and reduced safety. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes in order to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, their identification along with the primary contributing factors is imperative. The objective of this study is to develop a procedure to identify SCs using a static and a dynamic approach in a large-scale multimodal transportation networks. The static approach is based on pre-specified spatiotemporal thresholds while the dynamic approach is based on shockwave principles. A Secondary Crash Identification Algorithm (SCIA) was developed to identify SCs on networks. SCIA was applied on freeways using both the static and the dynamic approach while only static approach was used for arterials due to lack of disaggregated traffic flow data and signal-timing information. SCIA was validated by comparison to observed data with acceptable results from the regression analysis. SCIA was applied in the State of Tennessee and results showed that the dynamic approach can identify SCs with better accuracy and consistency. The methodological framework and processes proposed in this paper can be used by agencies for SC identification on networks with minimal data requirements and acceptable computational time. 相似文献
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