ABSTRACT

This paper discusses the development of a novel off-line vision-based system to obtain the naturalistic trajectory database of traffic streams from recorded video footages. The developed system uses a semi-automatic mechanism that provides manual interference for vehicle identification and classification and executes automated tracking of the identified vehicles. A trajectory database of typical disordered heterogeneous traffic stream was collected to evaluate the performance of the developed system. Results show that the developed system significantly enhances the process of trajectory data collection in such traffic conditions. The collected trajectory database is then used to investigate two crucial aspects that characterize the disordered heterogeneous traffic (i) interaction of different types of vehicles in the longitudinal and staggered following scenario, and (ii) lateral shift propensity of different types of vehicles. The analysis emphasizes the behavioral difference between different types of vehicles, and utility of the developed system to address the prevailing research gaps.

摘要

本文讨论了一种新颖的基于离线视觉的系统的开发，该系统可从录制的视频镜头中获取交通流的自然轨迹数据库。所开发的系统使用半自动机制，可为车辆识别和分类提供手动干扰，并对识别出的车辆执行自动跟踪。收集了典型无序异构流量流的轨迹数据库，以评估开发系统的性能。结果表明，在这种交通状况下，开发的系统大大增强了轨迹数据的收集过程。然后，使用收集的轨迹数据库来研究表征无序异构交通的两个关键方面（i）在纵向和交错跟随情况下不同类型车辆的相互作用，以及（ii）不同类型车辆的侧向移动倾向。分析强调了不同类型车辆之间的行为差​​异以及所开发系统的实用性，以弥补当前的研究空白。