A model based approach to predict stream travel time using public transit as probes

S. Vasantha Kumar, L. Vanajakshi, S. Subramanian
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引用次数: 24

Abstract

Travel time is one of the most preferred traffic information by a wide variety of travelers. Travel time information provided through variable message signs at the roadside could be viewed as a traffic management strategy designed to encourage drivers to take an alternate route. At the same time, it could also be viewed as a traveler information service designed to ensure that the driver has the best available information based on which they can make travel decisions. In an Intelligent Transportation Systems (ITS) context, both the Advanced Traveler Information Systems (ATIS) and the Advance Traffic Management Systems (ATMS) rely on accurate travel time prediction along arterials or freeways. In India, currently there is no permanent system of active test vehicles or license plate matching techniques to measure stream travel time in urban arterials. However, the public transit vehicles are being equipped with Global Positioning System (GPS) devices in major metropolitan cities of India for providing the bus arrival time information at bus stops. However, equipping private vehicles with GPS to enable the stream travel time measurement is difficult due to the requirement of public participation. The use of the GPS equipped buses as probe vehicles and estimating the stream travel time is a possible solution to this problem. The use of public transit as probes for travel time estimation offers advantages like frequent trips during peak hours, wide range network coverage, etc. However, the travel time characteristics of public transit buses are influenced by the transit characteristics like frequent acceleration, deceleration and stops due to bus stops besides their physical characteristics. Also, the sample size of public transit is less when compared to the total vehicle population. Thus mapping the bus travel time to stream travel time is a real challenge and this difficulty is more complex in traffic conditions like in India with its heterogeneity and lack of lane discipline. As a pilot study, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in the present study. Since it is only a pilot study, only twowheeled vehicles have been considered as they constitute a major proportion in the study area. The prediction scheme is corroborated using field data collected by carrying GPS units in two-wheelers traveling along with the buses under consideration. The travel time estimates from the model were compared with the manually observed travel times and the results are encouraging.
一种基于模型的以公共交通为探针的流行程时间预测方法
旅行时间是各种旅行者最喜欢的交通信息之一。通过路边的可变信息标志提供的行驶时间信息可以被视为一种交通管理策略,旨在鼓励司机选择另一条路线。与此同时,它也可以被视为一种旅行者信息服务,旨在确保司机拥有最佳的可用信息,并据此做出旅行决定。在智能交通系统(ITS)的背景下,高级旅客信息系统(ATIS)和高级交通管理系统(ATMS)都依赖于沿主干道或高速公路的准确旅行时间预测。在印度,目前还没有永久性的主动测试车辆系统或车牌匹配技术来测量城市主干道上的车流行驶时间。然而,印度主要大城市的公共交通车辆都配备了全球定位系统(GPS)设备,以便在公交车站提供公交车到达时间信息。然而,由于公众参与的要求,为私家车辆配备GPS以实现流旅行时间的测量是困难的。利用配备GPS的公交车作为探测车,估算流行驶时间是解决这一问题的一种可能方法。使用公共交通工具作为出行时间估计的探针,具有在高峰时段频繁出行、网络覆盖范围广等优点。而公交车辆的行驶时间特性除了受其自身物理特性的影响外,还受到公交站点频繁加、减速、停车等交通特性的影响。此外,与车辆总数相比,公共交通的样本量较小。因此,将公交车行驶时间映射到流行驶时间是一个真正的挑战,而在印度这样的交通条件下,这种困难更加复杂,因为它的异质性和缺乏车道纪律。作为一项试点研究,本研究采用基于模型的卡尔曼滤波技术来预测公共交通流的旅行时间。由于这只是一项试点研究,因此只考虑了两轮车辆,因为它们在研究地区占主要比例。该预测方案通过携带GPS装置的两轮车与考虑中的公共汽车一起行驶收集的现场数据得到证实。将模型估计的行程时间与人工观测的行程时间进行了比较,结果令人鼓舞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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