Analytical formulation and empirical evaluation of pre-signals for bus priority
Analytical formulation and empirical evaluation of pre-signals for bus priority
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DOI：
10.1016/j.trb.2014.03.004
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发表时间：
2014-06
影响因子：
6.8
通讯作者：
S. I. Guler;M. Menéndez
中科院分区：
文献类型：
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作者：
S. I. Guler;M. Menéndez
在城市环境中，公交车延误的主要原因之一是信号交叉口。在信号交叉口给予公交车优先通行权的一个常用解决方案是为公交车专用车道。然而，这种策略可能会浪费信号灯处宝贵的绿色时间，并对汽车造成额外的延迟，特别是当公交车流量较低时。总的来说，系统中的总人时延误（即，公共汽车和汽车）可能由于汽车用户经历的过度延迟而增加。为此，一个额外的信号上游的主信号，称为预信号，可以用来更好地利用主信号的能力，同时仍然提供公交优先，以减少整个系统的人小时的delays.The本研究的目的是分析量化和经验评估遇到的汽车和公交车使用预信号的延误。最终的目标是为所提出的战略提供应用领域。本文提出了分析公式计算公交车和小汽车延误排队论。分析模型表明，在广泛的情况下，预信号可以最大限度地减少系统范围内的人小时的延误，相比专用车道的公交车只使用或经营巴士和汽车完全混合。分析模型进行了验证与经验数据收集在现有的前信号，这表明该模型的延迟预测密切跟随现实。
One of the major causes of bus delays in urban environments are signalized intersections. A commonly used solution to give priority to buses at signalized intersections is to dedicate a lane for bus-use only. However this strategy can waste valuable green time at signals and impose additional delays to cars, especially when bus flows are low. Overall, the total person hours of delays in the system (i.e., buses and cars) can increase due to excessive delays experienced by car users. To this end, an additional signal upstream of the main signal, called a pre-signal, can be used to better utilize the capacity of the main signal while still providing bus priority to reduce the system-wide person hours of delays.The aim of this research is to analytically quantify and empirically evaluate the delays encountered by cars and buses with the use of pre-signals. The ultimate goal is to provide domains of applications for the proposed strategy. The paper presents analytical formulations to compute bus and car delays using queuing theory. The analytical models show that for a wide range of cases pre-signals can minimize the system-wide person hours of delays, as compared to dedicating a lane for bus-use only or operating buses and cars completely mixed. The analytical model is validated with empirical data collected at an existing pre-signal, which shows that the delay predictions of the model closely follow reality.