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Modeling Urban Travel Time Variability by Analyzing Delay Distribution for Signalized Urban Trips

机译:通过分析信号化城市出行的延误分布来建模城市出行时间变异性

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It is widely accepted that travel times in urban road networks can vary largely, both within a shorttime period and from period to period in a single day. One important cause for this variability in urbantravel times is the delay that drivers experience at (particularly signalized) intersections. This delaycan be due to the time that the driver needs to wait for the red light or the time for the queue ofwaiting vehicles at the intersection to dissolve, or both. As a result, the ability to capture thecharacteristics of the delay at signalized intersections is a critical, yet challenging, step in estimatingand predicting urban link travel times. The uncertainties in these experienced delays are caused byboth the traffic conditions and traffic control at the intersections. In this paper, a delay distributionmodel is developed for an urban trip over a route with two consecutive fixed-time controlledintersections. The proposed model captures the stochasticity in the arrival and departure process at thefirst intersection. The presented example illustrates how these stochastic factors influence the realizeddelay distribution for the two consecutive signalized intersections. Delay distributions are analyzedfor different levels of mismatch of the two controlled intersections (from well coordinated to badlycoordinated), and under different degrees of saturation. Finally, the predicted delay distributions arevalidated using the VISSIM simulation model, and show that the proposed (analytical) model can wellrepresent the VISSIM simulation data.
机译:人们普遍认为,城市道路网中的旅行时间可能会在很短的时间内相差很大 时间段和一天中不同时期之间的时间。造成城市多变性的一个重要原因 行驶时间是驾驶员在(特别是信号灯)交叉路口遇到的延迟。这个延误 可能是由于驾驶员需要等待红灯的时间或排队等候的时间。 在交叉路口等待车辆解散,或两者兼而有之。结果,捕获 信号交叉口的延误特性是估算中至关重要但又具有挑战性的一步 并预测城市联系的旅行时间。这些经历的延误的不确定性是由于 交叉路口的交通状况和交通管制。本文中的延迟分布 针对连续两个固定时间受控的路线上的城市旅行开发了模型 交叉路口。所提出的模型捕获了到达和离开过程中的随机性。 第一个路口。所提供的示例说明了这些随机因素如何影响已实现的 两个连续的信号交叉口的延迟分布。分析延迟分布 对于两个受控交叉路口的不同程度的不匹配(从协调良好到不良 协调),并在不同的饱和度下。最后,预测的延迟分布为 使用VISSIM仿真模型进行了验证,并表明所提出的(分析性)模型可以很好地 代表VISSIM仿真数据。

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