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Traffic Flow Analysis at Manual Toll booth Operation under Mixed Traffic Conditions

机译:混合交通情况下手动收费站操作的交通流分析

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Heterogeneous traffic conditions prevail in all countries across the world, but the degree of heterogeneity is different in developed and developing countries. In the United States, heavy vehicles (trucks and buses) are mixed with passenger cars, whereas more than seven different categories of vehicles in India are observed in the traffic stream. Such heterogeneity is also observed at tollbooths, where dedicated lanes are provided for each vehicle category; however, during peak hours, lane discipline is broken, and the same lane is used by different categories of vehicles. Such mixed traffic conditions lead to wide variations in service time for the same vehicle category and, hence, reduce tollbooth capacity. To convert the mixed traffic flow into the equivalent passenger car, the tollbooth equivalency factor (TEF) is proposed in the present study. The TEF is based on the service time and clearance time of a vehicle with respect to a passenger car at the same tollbooth. The TEF is found not to be a fixed value but varies with the approaching mixed traffic volume and composition of traffic in the queue. Vehicles in the traffic stream are divided into seven categories, and simultaneous equations are developed to determine the service time of a vehicle type based on approach volume and traffic composition. These equations are further used to depict variations in TEF with varying approach volume and composition. The change in TEF values is explained on the basis of the relative change in service time of a vehicle type with respect to a standard car at different volume levels. The accuracy of the TEF values estimated through simultaneous equations is checked by comparing the estimated values with those calculated directly from the field data. TEF values obtained for each type of vehicle are multiplied by the number of that vehicle type to obtain the approaching mixed traffic volume in an equivalent homogenous mix (TEF/h). This method will naturally require an estimation of TEF for each vehicle type from the field data. To avoid this exercise, the stream equivalency factor (SEF) at the tollbooth is proposed, which provides an overall multiplying factor for the entire traffic volume to convert heterogeneous traffic into the homogenous equivalent. The results presented in this paper will be useful for planners and tollbooth managers for the design and performance evaluation of toll plazas and to identify the number of lanes required during peak and nonpeak hours.
机译:异构交通状况在世界上所有国家都普遍存在,但发达国家和发展中国家的异构程度不同。在美国,重型车辆(卡车和公共汽车)与乘用车混合在一起,而在印度,交通流中发现了七种以上不同类型的车辆。在收费站也观察到这种异质性,在收费站为每种车辆类别提供专用车道。然而,在高峰时段,车道纪律被打破,不同种类的车辆使用同一车道。这种混合交通状况导致相同车辆类别的服务时间差异很大,因此降低了收费站的容量。为了将混合交通流量转换为等效乘用车,本研究提出了收费站当量因子(TEF)。 TEF基于相同收费站的车辆相对于乘用车的服务时间和通行时间。发现TEF不是固定值,而是随着接近的混合流量和队列中流量的组成而变化。交通流中的车辆分为七个类别,并根据联通量和交通量构成联立方程来确定车辆类型的服务时间。这些方程式还用于描述随着进场体积和组成的变化,TEF的变化。 TEF值的变化是根据车辆类型在不同音量下相对于标准车的服务时间的相对变化来说明的。通过将联立方程估算的TEF值的准确性与直接从现场数据计算得出的值进行比较,可以检查其准确性。将每种车辆类型获得的TEF值乘以该车辆类型的数量,以获得等效均质混合量(TEF / h)中接近的混合交通流量。这种方法自然需要根据现场数据估算每种车辆的TEF。为了避免这种情况,提出了收费站的流当量因数(SEF),它为整个交通量提供了一个整体的乘数因子,以将异构流量转换为同质等效物。本文介绍的结果将对规划者和收费站管理人员进行收费广场的设计和性能评估,并确定高峰和非高峰时段所需的车道数量有用。

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