ABSTRACT

The present work introduced a framework of developing comprehensive extended vehicular trajectory data under heterogeneous non-lane-based traffic conditions like the NGSIM datasets in the United States. Due to the absence of automation and instrumentation, and even the lack of sensor deployment on roads in developing economies like India, it is even more challenging to study driver behavior. A new stitching-based algorithm was used for developing the extended trajectory database for three traffic-flow levels on a 535-m long section of an urban arterial. The algorithm was used to stitch the trajectory data over the segments such that the subject vehicle with continuous trajectory data points over the entire study stretch. The developed framework is a novel tool for establishing a trajectory dataset for mixed traffic, it should be of interest to researchers in developing and developed countries.

摘要

目前的工作介绍了在美国 NGSIM 数据集等异构非车道交通条件下开发综合扩展车辆轨迹数据的框架。由于缺乏自动化和仪器仪表，甚至在印度等发展中经济体的道路上也没有部署传感器，因此研究驾驶员行为更具挑战性。一种新的基于拼接的算法被用于在城市主干道的 535 米长的路段上开发三个交通流级别的扩展轨迹数据库。该算法用于拼接分段上的轨迹数据，使得具有连续轨迹数据的主题车辆在整个研究范围内指向。所开发的框架是一种用于建立混合交通轨迹数据集的新工具，