通过模拟的服务容量和水平——以TRAF-NETSIM为例

S. Wong
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引用次数: 2

摘要

我们通过一个案例研究,探讨了使用TRAF-NETSIM微观模拟模型来估计服务容量和水平的前景。在案例研究中,我们收集了停车延误、饱和流量、公交车停留时间、双倍停车时间、车辆和行人数量等数据。这些数据作为运行和校准模型以及检查模型结果的基础。虽然TRAF-NETSIM不直接提供容量和服务级别,但我们展示了如何利用其详细的仿真功能和图形来获得容量和服务级别。我们还展示了如何校准模型以表示当地的交通状况。模拟的容量、停止时延和服务水平与现场结果非常接近。由于TRAF-NETSIM是一个随机模型,因此人们担心其结果可能会有所不同。我们通过输入不同的随机数种子和不同的模拟时间来检验其可变性。我们发现容量的变化不显著,而停止延迟的变化是混合的。我们还检查了所需的运行次数和模拟时间长度,以获得95%的置信度。traffic - netsim有很多优点。它的动画和静态图形可以显示正在发生的事情或如何推导结果。其众多的校准参数使其适用于许多交通条件。它产生了许多对其他分析有用的统计数据。它考虑了个体因素以及可能影响服务能力和水平的不同因素之间的相互作用。人们可以分析这些因素对一个路口或整个网络的影响。使用像TRAF-NETSIM这样的模拟模型来计算容量和服务水平的前景似乎很有希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Capacity and level of service by simulation – A case study of TRAF-NETSIM
We explore the prospect of using TRAF-NETSIM, a microscopic simulation model, to estimate capacity and level of service through a case study. In the case study, we collected stopped delay, saturation flow, bus dwell time, double parking duration, vehicle and pedestrian volumes, etc. These data served as the bases to run and to calibrate the model, and to check the model results. Although TRAF-NETSIM does not provide capacity and level of service directly, we showed how to make use of its detail simulation capabilities and graphics to obtain capacity and level of service. We also showed how to calibrate the model to represent local traffic conditions. The simulated capacity, stopped delay and level of service were very close to the field results. Since TRAF-NETSIM is a stochastic model, there is concern that its results may vary. We examined its variability by inputting different random number seeds with different simulation times. We find that the variation of capacity was insignificant while that of stopped delay was mixed. We also examined the required number of runs and length of simulation times to obtain 95% level of confidence. TRAF-NETSIM has many advantages. Its animated and static graphics can show what is going on or how the result is derived. Its numerous calibrating parameters enable it to be applicable to many traffic conditions. It produces many statistics which are useful for other analyses. It considers individual factors as well as the interaction of different factors which may affect capacity and level of service. One can analyze the impacts of these factors on one intersection or on the network as a whole. The prospect of using a simulation model such as TRAF-NETSIM for capacity and level of service appears to be promising.
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