NSF/USDOT ICSST: Exploring New Traffic Characteristics and Performance Measures Using Feature Extraction and Texture Characterization of Spatiotemporal Traffic Contour Maps

信息和通信技术继续改善水陆运输系统的运输数据获取和业绩监测能力。 目前，全国范围内的交通管理中心（TMC）真实的实时收集大量数据，从中提取的信息很少。 尽管在交通数据收集技术方面取得了巨大的进步，但还没有通过最大限度地利用存档和实时交通数据中的信息提取方法来改进现有的绩效措施。 目前的平均行程时间和延误的点估计形式的措施，基本上是从一阶统计的属性，并没有充分揭示某些局部和系统范围内的属性，如平滑度，粗糙度，规律性，均匀性，熵，和其他。 这样的属性只能解开先进的程序和二阶统计，能够利用时空的依赖关系和纹理特征的构建时空交通等高线图，一个概念，这是类似于数字图像的纹理表征的交通运营的质量。 本研究的主要目标是：（1）通过量化时空等高线图的特征，开发一种新的性能指标;（2）利用新的性能指标研究过渡阶段的交通行为;（3）建立高速公路扩建路段的在线和离线性能评估程序;（4）建立高速公路扩建路段的在线和离线性能评估程序;（5）建立高速公路扩建路段的在线和离线性能评估程序。（4）调查新措施与事故频率和严重程度之间的相关性;（5）比较经常性和非经常性条件的结构特征;（6）开发新措施的实施模块，以便由TMC真实的实时执行。该项目研究时空高速公路交通等高线图，以改善现有的高速公路性能的措施，并确定高速公路拥堵和碰撞的前兆条件。 我们将大力鼓励本科生与研究生在各种项目研究活动中合作，这将向他们介绍创新的基础/应用研究技术，并加强他们的计算和编程技能。 研究生将在项目中发挥主导作用，并将有机会为他们的论文和学位论文从事当代研究课题。 该项目的活动和结果将导致与交通，信息技术和计算机科学/工程交叉领域的其他学科的合作研究机会。 据设想，这项研究将有可能引发其他ITS研究活动，将扩大利益的运输系统用户和供应商在全国范围内和路易斯安那州。

Information and communication technologies continue to improve transportation data acquisition and performance monitoring capabilities for surface transportation systems. Currently, little information has been extracted from the massive amounts of data being collected in real time by traffic management centers (TMC) nationwide. Despite tremendous advances in transportation data collection technologies, no parallel efforts have been made to improve the existing performance measures by maximizing the utility of information extraction methods from both archived and real-time transportation data. Current measures in the form of point estimates of average travel time and delay are essentially properties derived from first-order statistics and do not adequately reveal certain local and system-wide properties such as smoothness, coarseness, regularity, homogeneity, entropy, and others. Such properties can only be unraveled by advanced procedures and second-order statistics that are capable of characterizing the quality of traffic operations by exploiting spatiotemporal dependencies and textural features of constructed spatiotemporal traffic contour maps, a concept that is similar to texture characterization of digital images. The primary objectives to be achieved in this research are: (1) develop a new class of performance measures by quantifying special features of spatiotemporal contour maps; (2) study the behavior of traffic during transient stages using the new performance measures; (3) establish a procedure for on-line and off-line performance assessment for extended freeway segments; (4) investigate the correlation between the new measures and accident frequency and severity; (5) compare textural characteristics of recurrent and non-recurrent conditions; and (6) develop an implementation module of the new measures to be executed in real time by TMCs. The project examines spatiotemporal freeway traffic contour maps to improve the existing freeway performance measures and to identify precursor conditions for freeway congestion and crashes. Undergraduate students will be strongly encouraged to collaborate with graduate students in the various project research activities, which will introduce them to innovative basic/applied research techniques and strengthen their computational and programming skills. Graduate students will have a leading role in the project and will find opportunities to work on contemporary research topics for their theses and dissertations. The activities and findings of this project will lead to collaborative research opportunities with other disciplines at the intersection areas of transportation, information technology, and computer science/engineering. It is also envisioned that this research study will potentially spark other ITS research activities that would extend benefits to both transportation system users and providers nationwide and within the state of Louisiana.