Link travel time estimation using single GPS equipped probe vehicle

Yanying Li, M. McDonald
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引用次数: 115

Abstract

Probe vehicles can be used as an efficient method to collect real-time travel time information. Existing research on travel time estimation directly records the travel time of a probe vehicle and calculates the mean of travel times from a number of probe vehicles. This paper describes a new approach to estimate travel time by using a single probe vehicle based on the analysis of the speed-time profile. According to the features extracted from the speed profile, the driving pattern of a probe vehicle is classified by using fuzzy sets. Differing from the traditional concept in the research of driving behaviour, the driving pattern in this study is only associated with the difference between the travel time of the probe vehicle and mean travel time. A new variable, the maximum continuous acceleration (MCA), is introduced to reflect acceleration characteristics of the driver by combining the continuous acceleration and speed at the acceleration starting point. The MCA and average speed of a probe vehicle are taken as the input variables of fuzzy sets. The membership function values are determined by historical traffic data of the tested road segment. The travel time is calculated by corresponding equations for different driving patterns. A comparison of the estimated travel time and actual mean travel time illustrates the value of the approach.
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用单GPS探测车估计链路行程时间
探测车可以作为一种实时采集行车时间信息的有效手段。现有的旅行时间估计研究直接记录探测车辆的旅行时间,并从多个探测车辆中计算旅行时间的平均值。本文在分析速度-时间分布的基础上,提出了一种单探针车行驶时间估计的新方法。根据从速度剖面中提取的特征,利用模糊集对探测车的行驶模式进行分类。与传统的驾驶行为研究概念不同,本研究中的驾驶模式仅与探测车辆的行驶时间与平均行驶时间之差有关。引入了一个新的变量,即最大连续加速度(MCA),通过将连续加速度与加速起点的速度相结合来反映驾驶员的加速特性。以探测车辆的平均车速和最大时速作为模糊集的输入变量。隶属函数的值由被测路段的历史交通数据确定。利用相应的方程计算了不同驾驶模式下的行驶时间。通过对估计行程时间和实际平均行程时间的比较,说明了该方法的价值。
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