Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 1
Executive Summary Nonrecurring congestion is traffic congestion due to nonrecurring causes, such as crashes, dis- abled vehicles, work zones, adverse weather events, and planned special events. According to data from the Federal Highway Administration (FHWA), approximately half of all congestion is caused by temporary disruptions that remove part of the roadway from use, or "nonrecurring" congestion. These nonrecurring events dramatically reduce the available capacity and reliability of the entire transportation system. The objective of this project is to determine the feasibility of using in-vehicle video data to make inferences about driver behavior that would allow investi- gation of the relationship between observable driver behavior and nonrecurring congestion to improve travel time reliability. The data processing flow proposed in this report can be summa- rized as (1) collect data, (2) identify driver behavior, (3) identify correctable driver behavior, and (4) model travel time reliability, as shown in Figure ES.1. Introduction Key domestic and international studies in which in-vehicle video cameras were used to collect data were investigated in this study. The research team reviewed video, kinematic, and external data col- lected in each candidate data set. To quantitatively assess the qualification of candidate data sets, dimensions of feasibility were defined to evaluate the data sets with respect to legal restrictions, comprehensiveness, video data quality, in-vehicle data quality, linkages to external data, and format and structure. A list of qualified data sets was identified for further data reduction and analysis. The original research goals, data reduction process, and data formats of these studies were examined by the research team. The video data were manually reviewed, and additional data reduction was conducted to identify contributing factors to crashes and near crashes using video data and supplementary data. If the events were caused by inappropriate driver behavior or driver inattention, then countermeasures were suggested to prevent these safety-related events. In modeling travel time reliability, the team reviewed models used by other researchers. A multimode distribution of travel time was then proposed to model travel time variations. A sta- tistical method to model travel time reliability was developed that considers two factors: the probability of encountering congestion and the probability that certain estimated travel times will be experienced when congestion occurs. Potential problems and risks associated with existing data, including kinematic data, video data, reduced data, and external data, were identified during the data reduction and analysis phase. To facilitate future data collection efforts, including those related to in-vehicle data, other external data were proposed to improve the efficiency of data collection, reduction, and analysis. 1