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C H A P T E R 1 IntroductionSHRP 2 was established in 2006 to investigate the under- lying causes of highway crashes and congestion in a short-term program of four interrelated focus areas: Safety (significantly improve highway safety by understanding driving behavior in a study of unprecedented scale); Renewal (develop design and construction methods that cause minimal disruption and produce long-lived facilities to renew the aging high- way infrastructure); Reliability (reduce congestion and improve travel time reliability through incident management, response, and mitigation); and Capacity (integrate mobility, economic, environmental, and community needs into the planning and design of new transportation capacity). This report results from Project L10, part of the Reliability research of SHRP 2. Nonrecurring congestion is traffic congestion that results from nonrecurring causes, such as crashes, disabled 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. Three main causes of non- recurring congestion are incidents ranging from a flat tire to an overturned hazardous material truck (25% of congestion), work zones (10% of congestion), and weather (15% of con- gestion). Accidents resulting in fatalities and injuries may occur if a driver behaves in an inappropriate or less-than- optimal manner in response to such factors as incident scenes, work zones, inclement weather, roadside distractions, and queues of vehicles. In-vehicle video, along with other data, can potentially provide insight regarding how to modify driver behavior that contributes to nonrecurring congestion. The objective of this project is to determine the feasibility of using in-vehicle video data to make inferences about driver behavior that would allow6investigation of the relationship between observable driver behavior and nonrecurring congestion to improve travel time reliability. The successful execution of this research effort requires an in-depth understanding of existing research using video camera data and research about the modeling of travel time reliability. The team investigated key domestic and international studies that used in-vehicle video cameras to collect data. After an initial screening of candidate data sets, the feasibil- ity of using existing data was assessed by assigning scores to five general areas: access to the data; comprehensiveness of the data; types of vehicle performance data collected; ability to link to operational, traffic control, work zone, and environmental data; and data structure and format. The evaluation results generated a list of potential data sets. The data sets were examined in further detail, determining cri- teria to identify crashes and near crashes, contributing fac- tors to these safety-related events, and countermeasures to prevent these events. The team then reviewed literature in both the traffic engineering and human factors arena on safety impacts of driver behavior and travel time reliability. A new statistical method was also developed to model travel time reliability. To provide constructive suggestions for the next stage of research, potential problems and risks were identified and reviewed to determine strategies to address these shortcom- ings. Issues worthy of notice for future data collection efforts and the general guidelines for video data reduction and analy- sis are also discussed. The current report conveys the results of this effort, beginning with an introduction of the existing video-based driver performance databases (Tasks 1 and 2, as described in Chapter 2). Chapter 3 details each data set and identifies qualified data sources by assigning scores to the data set, including comprehensiveness, types of data, and data structure (Tasks 2 and 3). Chapter 4 details issues about data storage and
7the data reduction conducted by the original research team (Task 4). Chapter 5 summarizes step-by-step guidelines to analyze video data for studying driver behavior in relation to nonrecurring congestion (Tasks 1 through 4). Chapter 6 introduces the statistical models that were developed to assess travel time reliability (Task 5). Chapter 7 discusses potential problems, risks, and limitations in the data sets. Finally, Chapter 8 summarizes the data reduction resultsfor each candidate data set and proposes recommendations for future research efforts (Tasks 6 and 7). Reference 1. Federal Highway Administration, Office of Operations. Reducing Non-Recurring Congestion. http://ops.fhwa.dot.gov/program_areas/ reduce-non-cong.htm. Accessed May 12, 2011.
