SHRP 2 report S2-L33-RW-1.

2015-01-01 00:00:009780309274241https://doi.org/10.17226/22282National Academy of Sciences2015PDFTech ReportSHRP 2 Report S2-L33-RW-1FreewaysTraffic dataTraffic modelsTravel timeUrban highwaysValidationIntelligent transportation systemsTravel time reliabilityUnited StatesNational Research Council (U.S.). Transportation Research BoardHranac, RobBarkley, TiffanySambana, KavyaDerstine, BrianMirchandani, PituZhou, ZhuoyangAhn, SoyoungIteris, Inc.Arizona State UniversityUniversity of Wisconsin--MadisonSecond Strategic Highway Research Program (U.S.)National Research Council (U.S.). Transportation Research BoardUnited States. Federal Highway AdministrationAmerican Association of State Highway and Transportation OfficialsNTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATIONNTL-OPERATIONS AND TRAFFIC CONTROLS-OPERATIONS AND TRAFFIC CONTROLSNTL-PLANNING AND POLICY-PLANNING AND POLICYIntelligent Transportation Systems Joint Program OfficeThis report describes the methodology, data, conclusions, and enhanced models regarding the validation of two sets of models developed in the Strategic Highway Research Program 2 (SHRP 2) Reliability Project L03, Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies. The significance of the L03 models is they were among the first models that could be used to predict travel time reliability. Two sets of models were developed in Project L03, the data-poor and the data-rich models. The data-poor models predict a measure of reliability as a function of just the mean Travel Time Index, except for on-time measures. The data-rich models predict a measure of the variability of travel time as a function of a number of explanatory variables: the demand-to-capacity ratio, lane-hours lost (due to traffic incidents or work zones), and rainfall. Both the data-poor and data-rich models were estimated from data collected over a year from a subset of urban freeway segments in seven cities. The data-poor models apply to all time slices throughout a day, whereas four sets of data-rich models concerning different moments of the TTI distribution were estimated for the peak hour, the peak period, the midday, and weekdays. Validation data totaled 323 segment-years covering both midday and peak periods. In conducting the validation, the research team was prohibited from using data that were used to estimate the data-poor and data-rich models. The project used two criteria for assessing the validity of the L03 models. The first was the difference between the predicted and measured values of the dependent variable. The second was whether the estimated models satisfied the assumptions of linear regression. This report describes the degree to which the different models perform well in terms of prediction and satisfying regression assumptions. The data-poor models predicted acceptably well but had some shortcomings in terms of satisfying the regression assumptions. Three sets of enhanced models were developed. The research team could not find satisfactory enhancements to the data-rich models. The degree to which the data-rich models predict well and satisfy the assumptions of linear regression is also described in the report.78