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3 nition errors; more than 40% of near crashes resulted from inattention or distraction. Among these near crashes, almost one-quarter involved the subject driver's not seeing the other vehicle during a lane change or merge. Countermeasures were proposed to correct the driver behaviors that caused these events. Almost all the crashes in the RDCWS FOT study have the potential to be prevented if one or multiple countermeasures are applied; 91% of the near crashes in that study are correctable. In the 100-Car Study, almost 40% of the crashes can or are likely to be prevented, and more than 80% of the near crashes can or are likely to be prevented given reasonable countermeasures. In the two truck studies, all the crashes, tire strikes, and near crashes are preventable using appro- priate countermeasures. To model nonrecurring congestion related to modifying driver behavior, it is ideal to find a sub- stantial number of crashes that result in changes in traffic conditions. The congestion, therefore, can be monitored and modeled. According to the data reduction results, not enough realizations of such crashes occurred. Other supplemental data sets were used to construct the statistical model. The travel time data in the 100-Car Study were used only to validate the multimode distribution of travel time that is being proposed; the model is valid regardless of the source of the travel time data. The travel time reliability model results provide a better fit to field data compared with tra- ditional unimodal travel time model results. The reliability model is also more flexible and pro- vides superior fitting to travel time data compared with single-mode models. It provides a direct connection between the model parameters and the underlying traffic conditions and can be directly linked to the probability of incidents. Thus, it can capture the impact of nonrecurring congestion on travel time reliability. Conclusions The team explored the identified data sets to discuss the various issues associated with video and other supplementary data collection and data reduction, to propose models for travel time relia- bility, and to identify potential problems in data. From the analysis of the naturalistic data sources, this study demonstrates the following: 1. It is feasible to identify driver behavior before near crashes and crashes from video data col- lected in a naturalistic driving study and to thus infer the causes of those events. 2. Recommendations can be made to change driver behavior and, therefore, prevent crashes and near crashes or reduce the frequency of such events. 3. Naturalistic data are useful to identify impacts of crashes on traffic conditions. Given the small sample of crashes and the fact that the data acquisition system (DAS) does not gather data when the engine is off, it is not possible to study the impact of incidents on travel time reliability. When effectively integrated with external data sources, which is extremely feasi- ble given an accurate time and location stamp in the data set, naturalistic data can be highly efficient in recognizing the relationship between modifying driver behavior and nonrecur- ring congestion. 4. Increased coordination with weather and traffic volume data is required to determine when nonrecurring congestion exists, as well as to determine what driver actions are a result of these nonrecurring events. 5. It is possible to analyze naturalistic driving data to characterize typical levels of variability in travel times and to develop measures for quantifying travel time reliability. Limitations of Existing Data Sets The team reviewed multiple naturalistic driving data sets involving video data that are currently available. In analyzing driver behavior, as is the case with this research effort, high-quality video data is required. In general, all existing data sets are satisfactory in terms of video quality because