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From page 26... ... 26 Purpose The purpose of this background technical memorandum, the deliverable for Task 1 of the second Strategic Highway Research Program (SHRP 2) L33 project, is to identify important findings and lessons learned that will help validate and extend the L03 predictive reliability models. Read the entire page →
From page 27... ... 27 2. "A simpler model based on the fact that many of the applications [Highway Capacity Manual (HCM) Read the entire page →
From page 28... ... 28 Figure A.1. Data-rich modeling concept. Read the entire page →
From page 29... ... 29 Table A.1. Site Selection Design Criteria Factors Levels Highway Type Urban Rural Freeways Signalized Arterials Freeways Area size Small, medium • • Large, very large • • Base congestion Low (AADT/Ca < 7) Read the entire page →
From page 30... ... 30 Table A.2. SHRP 2 L03 Study Sites City Number of Sections Traffic Data Incident/Work Zone Data Weather Data Houston 13 Toll Tag Traffic.com NCDC/NOAA Minneapolis 16 Fixed Point Traffic.com NCDC/NOAA Los Angeles 3 Fixed Point Traffic.com NCDC/NOAA San Francisco Bay 4 Toll Tag/Fixed Point Traffic.com NCDC/NOAA San Diego 6 Fixed Point Traffic.com NCDC/NOAA Atlanta 10 Fixed Point, AirSage GDOT (NaviGAtor) Read the entire page →
From page 31... ... 31 Table A.3. L03 Final Analysis Data Set Category Sample Measures Reliability Metrics • Mean, standard deviation, median, mode, minimum, and percentile travel times and travel time indices (TTIs) Read the entire page →
From page 32... ... 32 The detector zone length spans the distance between the current detector and halfway to its nearest neighboring detectors in the upstream and downstream directions. When aggregating to the section level, VMT and VHT were marked as missing if less than half of the detectors reported valid data for each of the 5-min periods. Read the entire page →
From page 33... ... 33 Once the congested time period has been defined, it is split into two halves. The demand in the first half of congestion is assumed to be equal to the average volume measured in the two 5-min periods before the start of congestion. Read the entire page →
From page 34... ... 34 VDS 1201292 VDS 1201419 VDS 1202105 VDS 1201839 VDS 1201348 VDS 1217710 Figure A.5. Demand-estimation methodology applied to detector data in Orange County, California. Read the entire page →
From page 35... ... 35 The capacity used in both ratios is the hourly capacity according to HCM methods. Calculating Lane-Hours Lost The lane-hours lost term in the model is meant to be the sum of lane-hours lost because of incidents and lane-hours lost because of work zones. Read the entire page →
From page 36... ... 36 on-time trips made within 1.1 and 1.25 times the median TTI; and the percentage of on-time trips with 30-, 45-, and 50-mph speed thresholds. Two sets of data-poor equations are presented in the L03 final report and have been included in the attachment of this document. Read the entire page →
From page 37... ... 37 the lack of a rain variable in the weekday models; rain is an important factor in Seattle congestion. The data-poor model exhibits the same underprediction trend, particularly with the 95th-percentile equation. Read the entire page →
From page 38... ... 38 Recommendations The L33 project team reviewed the L03 final report and final technical expert task group (TETG) presentation and communicated with the L03 principal investigator to assess the lessons learned and final conclusions from that project. Read the entire page →
From page 39... ... 39 developed predictive travel time reliability models: L07, L04, and L08. Together the four projects support analyses at the sketch planning, project planning, facility performance, travel demand forecasting, and traffic simulation levels. Read the entire page →
From page 40... ... 40 These relationships were ultimately used to validate the results of the travel times output by the project's mesoscopic model. The L07 project team fit a linear regression model to the mean travel time per mile and standard deviation travel time per mile output by their mesoscopic model and compared the coefficients with those obtained from fitting the linear model to 4 hours of GPS trajectory data in New York City purchased from TomTom. Read the entire page →
From page 41... ... 41 heavy rain (>0.5 in./h) determined from Weather Underground data. Read the entire page →
From page 42... ... 42 that 5-min period over a year, or (2) a facility-level PDF, in which the individual vehicle travel times are averaged within each 5-min period, and the PDF is composed of the 5-min average travel times across the year. Read the entire page →
From page 43... ... 43 L08 methodology incorporates these factors by discretizing a particular factor into a category and estimating the probability that each category of factor will occur during a particular time period. Demand is categorized into different demand patterns that are facility-specific and organized by day of week and month. Read the entire page →
From page 44... ... 44 Measuring the Travel Time Probability Density Function In measuring the travel time probability density functions of each study section for calibration and validation of the datarich and data-poor models, the L03 project team weighted each measured travel time bin by the frequency it occurred as well as the average volume on the segment across the 5-min time periods that experienced that travel time. According to the L03 principal investigator, this was done to ensure that the models will still be applicable in the future when it is possible to directly measure travel times from every vehicle traversing a segment. Read the entire page →
From page 45... ... 45 Appendix A Attachment This attachment lists the equations for the data-rich and datapoor models from L03 and L07 projects, which are noted in Appendix A L03 data-Rich equations, Chapter 7 of Final Report Peak Period mean TTI (1) Read the entire page →
From page 46... ... 46 95th-percentile TTI (15) 0.07812 dccrite = ( ) Read the entire page →
From page 47... ... 47 where PctTripsOnTime45mph is the percentage of trips that occur at space mean speeds above the threshold of 45 mph. pPctTripsOnTime30mph 1 0.4139 meanTTI 1 RMSE 4.4% (42) Read the entire page →

From page 26...

... 26 Purpose The purpose of this background technical memorandum, the deliverable for Task 1 of the second Strategic Highway Research Program (SHRP 2) L33 project, is to identify important findings and lessons learned that will help validate and extend the L03 predictive reliability models.