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Travel time estimation method for urban road based on traffic stream directions

机译:基于交通流向的城市道路行驶时间估计方法

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摘要

Traffic flow on a given link in an urban road network can be divided into several traffic streams, depending on their turning manoeuvres when entering and leaving the link. These traffic streams may experience various travel times due to multiple reasons, such as fluctuations in traffic demand/supply and stochastic arrivals/departures at signalised intersections. However, the current travel time estimation methods take traffic flow as a whole and produce a single estimation value. This approach can produce large errors. Furthermore, given the travel time information of each traffic stream, the results of dynamic traffic assignment models can be made much more accurate and the effect of signal controls improved. In this paper, a comparison analysis is conducted to verify the significant difference in link travel times of different traffic streams. Then, link travel time is redefined in consideration of traffic stream directions. This process is also successful in filtering noise, as shown in the numerical experiments. In addition, existing estimation methods cannot reflect real values or fluctuations of travel times in sampling intervals without any valid observational data. To solve this problem, a regression model is built and integrated into the travel time estimation model. Error analysis of several links on two different days demonstrates the improvements made by the model.
机译:城市道路网络中给定链接上的交通流可以分为几个交通流,具体取决于它们进出该链接时的转向动作。这些交通流可能由于多种原因而经历各种旅行时间,例如交通需求/供应的波动以及信号交叉口的随机到达/离开。然而,当前的行进时间估计方法将交通流作为整体并产生单个估计值。这种方法会产生较大的错误。此外,给定每个交通流的行驶时间信息,可以使动态交通分配模型的结果更加准确,并改善信号控制的效果。在本文中,进行了比较分析,以验证不同业务流的链路旅行时间的显着差异。然后,考虑交通流方向重新定义链接旅行时间。如数值实验所示,该过程也成功地滤除了噪声。此外,现有的估算方法无法在没有任何有效观测数据的情况下反映采样间隔中的真实值或行进时间的波动。为了解决这个问题,建立了回归模型并将其集成到行程时间估计模型中。在两天之内对几个链接进行的错误分析证明了该模型的改进。

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