混合交通条件下信号灯控制的街区中央人行横道的车辆延迟模型

Sandeep Manthirikul, Udit Jain
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引用次数: 0

摘要

对于信号灯位置,延迟是评估服务水平和重新设计交通信号灯的关键参数之一。在世界各地,《公路通行能力手册》(HCM)和韦伯斯特延迟模型通常用于计算信号交叉口的车辆延迟,尽管这些模型假定交通条件和车道规则是相同的。此外,值得注意的是,以往的研究大多集中于开发信号交叉口的延迟模型,而信号灯控制的中间街区人行横道却被以往的研究人员所忽视。有鉴于此,本研究针对混合交通条件下的信号灯控制的中间街区人行横道(SMCs)提出了一种改进的车辆延误模型。混合交通条件是指车辆种类繁多、相互作用复杂、道路上没有车道规则,这些条件通常在发展中国家比较常见。本研究对 HCM 车辆延误模型进行了修改,纳入了与混合交通条件和 SMC 相关的因素。为了测试所提模型的准确性,我们使用在印度海得拉巴其他 SMC 收集的数据对该模型进行了实地延迟验证。现场延迟的计算方法是将测得的队列长度与信号周期长度进行对比。均方根误差 (RMSE)、平均绝对百分比误差、纳什-苏特克利夫效率、RMSE-观测值标准偏差比和偏差百分比等性能指标用于测试所提模型的准确性。结果表明,所提模型的误差率最低,模型与实际车辆延误之间的相关系数最高。现场延迟与模型延迟之间的最大误差小于 5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle Delay Model for Signalized Midblock Crosswalk Under Mixed Traffic Conditions
For signalized locations, delay is one of the key parameters in assessing the level of service and redesigning the traffic signals. Around the world, Highway Capacity Manual (HCM) and Webster delay models are commonly used in calculating the vehicle delay at signalized intersections, notwithstanding that these models assume homogeneous traffic conditions and lane discipline. Furthermore, it is worth noting that previous studies have mostly focused on developing delay models for signalized intersections, whereas signalized midblock crosswalks were overlooked by past researchers. In view of this, the current study presents a modified vehicle delay model for signalized midblock crosswalks (SMCs) under mixed traffic conditions. Mixed traffic conditions refers to diverse vehicles, complex interactions, and no lane discipline on the road—conditions which are usually noticed in developing nations. The HCM vehicle delay model is modified in the current study by incorporating factors related to mixed traffic conditions and SMCs. To test the accuracy of the proposed model, it was validated with the field delay using data collected at other SMCs in Hyderabad, India. Field delay was calculated by plotting the measured queue length against the signal cycle length. The performance metrics of root mean square error (RMSE), mean absolute percentage error, Nash–Sutcliffe efficiency, RMSE-observations standard deviation ratio, and percent bias were used to test the accuracy of the proposed model. Results revealed that the proposed model yielded the lowest error rates and the highest correlation coefficients between modeled and actual vehicle delay. The maximum error between the field delay and the modeled delay was found to be less than 5%.
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