Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Minnesota (2014) / Chapter Skim
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SHRP 2 Reliability Project L38B Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Minnesota
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SHRP 2 Reliability Project L38B Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Minnesota Michael Sobolewski Minnesota Department of Transportation Roseville, Minnesota Todd Polum, Paul Morris, Ryan Loos, and Krista Anderson SRF Consulting Group, Inc. Minneapolis, Minnesota TRANSPORTATION RESEARCH BOARD Washington, D.C.
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© 2015 National Academy of Sciences. All rights reserved.
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DISCLAIMER The opinions and conclusions expressed or implied in this document are those of the researchers who performed the research. They are not necessarily those of the second Strategic Highway Research Program, the Transportation Research Board, the National Research Council, or the program sponsors.
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The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished scholars engaged in scientific and engineering research, dedicated to the furtherance of science and technology and to their use for the general welfare. On the authority of the charter granted to it by Congress in 1863, the Academy has a mandate that requires it to advise the federal government on scientific and technical matters.
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Contents 1 Executive Summary 1 Key findings and recommendations 5 CHAPTER 1 Introduction 5 Overview of Pilot Testing Process 7 Refined Testing Analysis 8 CHAPTER 2 TTRMS Development 8 Travel Time and Traffic Data 8 Transportation Information and Condition Analysis System Tool 11 Travel Time Data Extraction Process 13 Weather Data 20 Event Data 24 Crash and Incident Information 31 Road Work Data 32 TTRMS Database Development 32 Input Data Processing 34 TTRMS Database Format 34 TTRMS Analysis Tool 40 Aggregate Reliability Measures 45 CHAPTER 3 Reliability Report 45 Description of Facilities 45 Results Summary 55 Facility Observations 58 CHAPTER 4 Evaluation of the Project L07 Tool 58 Introduction 58 Initial Investigation 59 Findings Summary 59 Evaluation Process 65 Validation Comparison 73 Additional Sensitivity Testing and Exploration 77 Detailed Summary of Findings 79 Recommended Refinements 81 Opportunities for Future Testing of the L07 Tool 82 CHAPTER 5 Minnesota Reliability Workshop 82 Overview 83 Workshop Introduction 83 SHRP 2 Background and Concept 93 Technical Analysis of the SHRP 2 Tools 118 Utility of the SHRP 2 Tools 141 Review of Background and Concepts
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142 Example Applications for Travel Time Reliability 176 Conclusions and Next Steps 182 Key Findings 186 CHAPTER 6 Refined Technical Analysis 186 Alternative Time Intervals 192 Disaggregation of Delay Causes 195 Demand Regimes 200 SMART Signal Traffic Data 201 Updated L07 Benefit-Cost Tool 204 CHAPTER 7 Findings and Recommendations 204 Project L02 205 Project L07 205 Project L05 207 References A-1 APPENDIX A Study Facility Reliability Reports B-1 APPENDIX B Test Results of L07 Tool Evaluation
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EXECUTIVE SUMMARY The Minnesota pilot site has undertaken an effort to test data and analytical tools developed through the Strategic Highway Research Program (SHRP) 2 Reliability focus area.
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A wide variety of graphical products and performance measures can be produced using results from the TTRMS database. The Minnesota pilot team found the following items provide the greatest value to potential audiences: • Surface plots • Pie charts • Cumulative density function (CDF)
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Minnesota team recommends restoration of this feature to provide more flexibility for performing cost-benefit analyses. The tool does not provide any functionality for traffic growth over the project's lifetime.
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Following the pilot testing technical work and outreach, MnDOT is committed to advancing reliability evaluation in its business practices. This was most clearly demonstrated by the success of the project example used for the I-94 traffic study conducted alongside the pilot testing work.
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CHAPTER 1 INTRODUCTION The Minnesota pilot site has undertaken an effort to test data and analytical tools developed through the second Strategic Highway Research Program (SHRP) 2 reliability focus area.
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Project L02 The L02 guidance was utilized to establish a framework for collection, storage, processing, and analysis of travel time data to evaluate reliability performance. First, the mechanics of this process were established in terms of data sources and their collection and processing techniques.
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Project L05 The Minnesota pilot team carried forward guidance developed in the L05 project to initiate a dialog regarding incorporation of reliability evaluation in the planning and programming process. This was accomplished through an extensive outreach effort over the course of the pilot testing work.
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CHAPTER 2 TTRMS DEVELOPMENT This chapter documents the development of a travel time reliability monitoring system (TTRMS) for the Minnesota pilot site.
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of Minnesota Duluth. This has led to the development of an interface program named the Transportation Information and Condition Analysis System (TICAS)
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Figure 2.2. Schematic of TICAS travel time calculation.
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Travel Time Data Extraction Process TICAS was used to extract the traffic data for the TTRMS and to perform additional calculations to derive the travel time and VMT information. TICAS calculates and provides cumulative travel time with records every 0.1-mile from the specified start point to the end point along the highway.
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Table 2.3 and Table 2.4 provide an example of the travel time and VMT data downloaded using TICAS. Table 2.3.
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Table 2.5. TTRMS Traffic Data Format Time Stamp Travel Time VMT 20090101 00:05 13.98 647.90 20090101 00:10 13.71 659.07 20090101 00:15 13.92 801.09 20090101 00:20 14.22 934.82 20090101 00:25 13.92 1089.63 20090101 00:30 13.68 1202.14 20090101 00:35 13.96 1238.10 This format provided the backbone for the TTRMS data structure.
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For each site, R/WIS has atmospheric (weather condition) and precipitation history tables available to view and export.
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The attributes reported from the precipitation history are the following: • Precipitation type • Precipitation intensity • Precipitation rate • Precipitation start time • Precipitation end time • 10 Minute precipitation accumulation • 1 Hour precipitation accumulation • 3 Hour precipitation accumulation • 6 Hour precipitation accumulation • 12 Hour precipitation accumulation • 24 Hour precipitation accumulation After reviewing the data provided by the atmospheric and precipitation history tables, it was determined that the four key attributes to be referenced in the database spreadsheet are • Precipitation Intensity: Intensity of the precipitation as derived from the precipitation rate. The National Weather Service defines the following intensity classes: light, moderate, or heavy.
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that reduced the rate at which data could be downloaded is that every 2 to 3 hours the website would become unavailable for up to 10 minutes. One year of atmospheric and precipitation history at one site requires approximately 10 hours of work.
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Weather Underground The third source of weather data considered was the online service Weather Underground, which was developed in 1995 as an offshoot of the University of Michigan's Internet weather database. Jeff Masters, a Ph.D.
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Figure 2.4. Weather Underground station locations.
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The one key attribute that the Weather Underground data was missing that was provided by the R/WIS data was the precipitation type. The R/WIS data reported the precipitation type as none, rain, frozen, snow, or other.
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precipitation totals from both sources were compared to historical weather data from the Minnesota Climatology Working Group and KARE 11 News. The precipitation reported by Weather Underground was much more consistent than the amount reported by R/WIS.
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Speed and volume observations were made for the following highways: • I-94: The selected loop detectors were located just east of the 5th Street off-ramp and the 6th Street on-ramp. The following results were recorded: − Arrival duration lasted 100 minutes and occurred up to 15 minutes before the start of the game.
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Minnesota Twins The Minnesota Twins are a Major League Baseball team that plays home games in downtown Minneapolis. For the Minnesota Twins, game data from 2006 to 2012 was found on Wikipedia, and game start times were identified from ESPN.com because they were not provided in the Wiki data.
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Additional Sources Two additional sources were used to collect information on events taking place in downtown Minneapolis. The Minneapolis Event Log, collected by the City of Minneapolis, provides data on the start and end time for events taking place in Minneapolis in 2012 (it did not exist prior to 2012)
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Table 2.7. TTRMS Even Data Formatting Event Record Number Date Start Time End Time Event Type 11 11/11/2012 11/11/2012 9:00 11/11/2012 12:00 Vikings_A 12 11/11/2012 11/11/2012 15:00 11/11/2012 17:00 Vikings_D 13 12/9/2012 12/9/2012 9:00 12/9/2012 12:00 Vikings_A 14 12/9/2012 12/9/2012 15:00 12/9/2012 17:00 Vikings_D 15 12/30/2012 12/30/2012 12:25 12/30/2012 15:25 Vikings_A 16 12/30/2012 12/30/2012 18:25 12/30/2012 20:25 Vikings_D 17 4/9/2012 4/9/2012 12:10 4/9/2012 15:10 Twins_A 18 4/9/2012 4/9/2012 17:40 4/9/2012 19:40 Twins_D 19 4/11/2012 4/11/2012 16:10 4/11/2012 19:10 Twins_A 20 4/11/2012 4/11/2012 21:40 4/11/2012 23:40 Twins_D Crash and Incident Information Crashes and other incidents are significant sources of travel time unreliability on the highway system.
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Computer Aided Dispatch Data The research team obtained the CAD database for the analysis years from the RTMC, which hosts the joint dispatch center with the State Patrol. The CAD data provide information about calls received by State Patrol 911 operators, call records, and emergency response actions.
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Figure 2.6. Schematic of DMS event detail description extraction.
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Conflation of Crash and Incident Records Many of the crash and incident records in the CAD, DMS, and MnCMAT databases are believed to represent the same events occurring on the highway. For example, in the case of a crash, the dispatcher would receive a 911 call and make an entry in the CAD system.
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Figure 2.8. Schematic of crash and incident record spatial join relationships.
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Table 2.8. DMS Crash and Incident Type and Estimated Duration Crash and Incident Type Estimated Duration (minutes)
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Crash Impact: The DMS records provided the best data source for roadway capacity, since the displays typically included messages such as "Crash on Shoulder" or "Left Lane Closed.". CAD data included less reliable impact data, subject to what the dispatcher elected to include in the database.
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effort, road work is defined as any agency activity to maintain or improve the roadway that may result in impacts to capacity. This may include short-term activities such as guardrail, sign, or lighting repair as well as more significant, long-term construction actions.
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to the other attributes. The code was eventually modified to include all timestamps, even for days with no traffic data.
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arrival) or "Vikings_D" (for departure)
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The analysis tool references the TTRMS database with all the associated records shown in Table 2.13. It is a macro-enabled spreadsheet which produces basic travel time reliability measures such as cumulative density function (CDF)
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Figure 2.9. Query tool CDF curve example.
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Figure 2.10. 2012 travel time surface plot for TH-100 northbound.
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Figure 2.12. 2012 weather surface plot for TH-100 northbound.
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Figure 2.14. 2012 incident surface plot for TH-100 northbound.
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Aggregate Reliability Measures The TTRMS analysis tool also provides users with aggregate reliability measures for the facility under evaluation. These measures include many of the tools described in previous SHRP 2 reliability literature, such as CDF curves and reliability indices.
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observed. In this example, a 5-minute interval was used, resulting in a total of 105,120 intervals in 1 year.
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Figure 2.17. 2012 observation frequency pie chart by regime.
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Figure 2.18. 2012 delay pie chart by regime.
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PTI = TT95%TTFreeFlow Planning Time Failure/On-Time Measures: Describes the percentage of trips with travel times within a certain factor of the median travel time. Common thresholds include 1.1 ∗ 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑇𝑇𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑇𝑇 𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀 1.25 ∗ 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑇𝑇𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀𝑇𝑇 𝑇𝑇𝑀𝑀𝑇𝑇𝑀𝑀 Other formulations of these measures denote the percentage of trips with average speeds below a specified threshold: for example, 50 mph, 45 mph, or 30 mph.
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CHAPTER 3 RELIABILITY REPORT Description of Facilities Three study highways were selected for the Minnesota pilot testing of the SHRP 2 reliability products. The first facility analyzed was TH-100, starting at 77th Street in Edina and ending at 57th Avenue in Brooklyn Center.
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Facility Characteristics For each highway presented in the reliability report, a series of roadway characteristics are provided. These begin with a number of basic elements describing the facility's physical attributes and traffic demand.
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• Road Work • Event • Travel Time A key modification to these plots was that records without a given condition present are shown as blank rather than a color. This is intended to avoid confusion that a "None" condition is a part of the data, whereas this method correctly shows the blank areas represent the absence of records in the data.
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Figure 3.2. 2012 TH-100 northbound crash surface plot.
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Figure 3.4. 2012 TH-100 northbound road work surface plot.
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Traffic Data The traffic data is a continuous, rather than a discrete data source, and is therefore displayed differently in the surface plots compared to the nonrecurring conditions data. The traffic data category is comprised of the VMT and travel time observations.
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• 1.5-2.0 times the TTI • 2.0-2.5 times the TTI • 2.5-3.0 times the TTI • 3.0-3.5 times the TTI • 3.5-4.0 times the TTI • Greater than 4.0 times the TTI Figure 3.7. 2012 TH-100 northbound travel time surface plot.
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Figure 3.8. 2012 TH-100 northbound travel time CDF curve.
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Figure 3.9. 2012 TH-100 northbound observation pie chart.
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Figure 3.10. 2012 TH-100 northbound delay pie chart.
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Comparison Bar Charts The comparison bar charts shown in Figure 3.12 display the total delay separated by each year for a particular highway. The solid black bar represents the average volume of daily trips recorded along the facility.
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TH 100 Southbound Southbound TH-100 had higher VMT during the a.m. peak hour than northbound TH-100.
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I-94 Eastbound: Minneapolis to Saint Paul Eastbound I-94 between Minneapolis and Saint Paul consistently had a lower VMT each year than westbound I-94. In addition, the delay caused by events was higher in the eastbound direction compared to I-94 in the westbound direction.
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CHAPTER 4 EVALUATION OF THE PROJECT L07 TOOL Introduction The L07 tool was developed as an economic analysis tool to compare treatments that help mitigate nonrecurring congestion on freeway and major arterial segments. It is designed for use by agencies seeking a tool to assist with the analysis and prioritization of projects addressing nonrecurring congestion.
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• Can the L07 tool accurately replicate travel conditions along the highly congested test segment? • What is the value in developing additional detailed data for use in the L07 tool?
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Figure 4.1. Example incident input data.
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Detailed Scenario Geometry: Same process as the default scenario. Demand: Same process as the default scenario.
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Event data were used in combination with VMT data from the TTRMS database to determine the percent increase in traffic volume during events by hour of the day. VMT from the TTRMS could not be specifically defined to an individual event type, so all events were combined into a single recurring average event that occurred 46 days a year.
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Figure 4.3. Example event input computation for 1 hour of the day.
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Figure 4.4: Example work zone input data. Validation Comparison Validation of the L07 tool was performed by comparing the TTI percentile curves observed by the TTRMS to those produced by the L07 tool for both scenarios.
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Figure 4.5. Hourly travel time index profile: observed field data from L02 analysis.
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Figure 4.7. Hourly travel time index profile: L07 analysis with detailed inputs.
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Figure 4.8. Segment volume comparison of demand computed by L07 method vs.
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Figure 4.10. Morning peak (7:00 a.m.)
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Figure 4.12. Off-peak (3:00 a.m.)
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Figure 4.13. Hourly travel time index profile: L07 analysis with default inputs -- volume test Figure 4.14.
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Reviewing Figure 4.13 and Figure 4.14 shows that, for this particular segment, the 99th percentile curve shape matches the real-life conditions from the TTRMS data fairly closely (Figure 4.5) , but the other TTI curves remain relatively flat.
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Figure 4.16. Afternoon peak (4:00 p.m.)
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Figure 4.17. Speed computation comparison in the L07 tool.
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Figure 4.18. Crash rate calculation in the L07 tool.
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Table 4.1. Treatment Comparison: Low Demand/Low Incident Life Cost Maintenance B/C Ratio Accessible Shoulder 25 $250,000 $2,000 0.21 Alternating Shoulder 25 $150,000 $2,000 0.29 Crash Investigation Site 20 $50,000 $2,000 0.55 Emergency Pull-off 25 $10,000 $500 2.74 Emergency Access 20 $20,000 $1,000 0.89 Emergency Crossovers 30 $5,000 $500 2.61 Gated Turnarounds 20 $10,000 $3,000 0.57 Drivable Shoulders 25 $250,000 $2,000 0.12 Extra High Median Barrier 20 $30,000 $3,000 1.69 Runaway Truck Ramp 20 $50,000 $2,000 0.47 Incident Screen 20 $10,000 $5,000 0.32 Wildlife Crash Reduction 20 $45,000 $1,000 2.09 Anti-icing Systems 10 $50,000 $5,000 0.74 Snow Fence 10 $80,000 $4,000 0.61 Blowing Sand 10 $30,000 $5,000 0.2 It was concluded that benefit-cost (B/C)
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Table 4.2. Treatment Comparison: High Demand/High Incident Life Cost Maintenance B/C Ratio Accessible Shoulder 25 $300,000 $2,400 123.3 Alternating Shoulder 25 $150,000 $2,000 182.69 Crash Investigation Site 20 $50,000 $2,000 292.87 Emergency Pull-Off 25 $10,000 $500 1154.8 Emergency Access 20 $20,000 $1,000 8.61 Emergency Crossovers 30 $5,000 $500 18.18 Gated Turnarounds 20 $10,000 $3,000 6.05 Drivable Shoulders 25 $300,000 $2,400 0.57 Extra High Median Barrier 20 $30,000 $3,000 58.59 Runaway Truck Ramp 20 $50,000 $2,000 3.79 Incident Screen 20 $10,000 $5,000 2.64 Wildlife Crash Reduction 20 $4,500 $1,000 23.91 Anti-icing Systems 10 $50,000 $5,000 5.74 Snow Fence 10 $80,000 $4,000 4.69 Blowing Sand 10 $30,000 $5,000 0.2 Detailed Summary of Findings While using the L07 tool and performing detailed test scenarios, the pilot team discovered several challenges with compiling input data for use in the tool, which are detailed below: Demand Demand calculations have proven to be a challenge in tool calibration.
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analyst should determine the 30th highest hour or the year for each individual time slice. This creates a scenario where adjacent time slices could have demands occurring on different days of the year.
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across the entire 15 years of analysis. Additional guidance should be provided on how to address construction work zones that occur every few years.
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• Guidance stating that demand should be equal to volume over the course of a day. AADT could be used with hourly percentages to accomplish this.
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• Allow the user to input an amount of lane closures equal to the total lanes on the segment to account for work zones that close the entire roadway segment. − This is a common strategy used by MnDOT to accelerate maintenance projects.
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CHAPTER 5 MINNESOTA RELIABILITY WORKSHOP Overview The Minnesota pilot team hosted an interactive workshop on Thursday, February 20, 2014, to share the progress that the team had made on travel time reliability testing since April 2013 as part of the SHRP 2 L38B Reliability program. The purpose of the workshop was to introduce the concept of travel time reliability to stakeholders; demonstrate the utility and effectiveness of tools developed as part of the reliability research programs; share findings discovered while testing the reliability tools; and provide a forum to discuss future policy for making planning and programming decisions as reliability becomes implemented as a performance measure.
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− Benefit-Cost Enhancement − Other Applications • Closing Travel Time Reliability Survey • Next steps Workshop Introduction Presenter: Mike Sobolewski of the Minnesota Department of Transportation Figure 5.1. Welcome slide.
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SHRP 2 Background and Concept Presenter: David Plazak of SHRP 2 Following the introduction to the workshop, background information was provided to help audience members better understand the SHRP 2 Reliability focus area (see Figure 5.2 and Figure 5.3)
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An important concept from this portion of the workshop is that SHRP 2 is a large, targeted research program that has a limited amount of time associated with it and has built on the success of the original SHRP. The original SHRP ended in 1993 and resulted in several technologies, including SuperPave mix design and winter pretreatments.
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Opening Travel Time Reliability Survey Presenter: Renae Kuehl of SRF Consulting Group Once introductions were completed, an opening survey was conducted to gauge the audience's level of knowledge and understanding of travel time reliability. A series of nine questions was posed to the audience to gauge their understanding and use of reliability data.
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Figure 5.5. Reliability background.
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Figure 5.6: Minnesota pilot site. After beginning the reliability study, it took the team a short time to understand exactly what was being evaluated with these tools, using the wide range of data available.
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Figure 5.7. SHRP 2 reliability tools.
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Figure 5.9. SHRP 2 C11 tool.
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Figure 5.11. SHRP 2 L07 tool.
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The Minnesota team evaluated three SHRP 2 reliability products: • Project L02 Guide: Establishing Monitoring Systems for Travel Time Reliability. The purpose of the L02 tool was to compile different data sources into one data set for each study facility and to understand how the system is functioning today.
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Figure 5.13. Project schedule.
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Technical Analysis of the SHRP 2 Tools Project L02 Technical: Monitoring System Presenter: Paul Morris of SRF Consulting Group To begin, the presenter explained that during this part of the workshop, the audience would be looking at numbers and graphics to illustrate how the team performed their analysis. The purpose of this tool is to have a better understanding of travel time when it's not an ideal weather condition, or when there are other events impacting the system (e.g., a crash on the side of the road)
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Figure 5.16. TH-100 northbound (NB)
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Figure 5.17. TH-100 NB travel times.
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Figure 5.18. TH-100 NB 2012 CDF curve.
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Figure 5.19. Pie chart of observations of nonrecurring conditions.
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Figure 5.20. Pie chart of delay by nonrecurring conditions.
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To develop a database using the crash and incident data that was collected, the L02 guide was used. (See Figure 5.22.)
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Figure 5.23. Weather surface plot.
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Figure 5.25. Incident surface plot.
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Figure 5.27. Road work surface plot.
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Figure 5.28. VMT surface plot.
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Figure 5.30. Travel time CDF curve.
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Figure 5.31. Delay pie charts.
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Figure 5.33. Project L02 system monitoring.
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Project L07 Technical: Alternative Analysis Presenter: Ryan Loos of SRF Consulting Group To begin this portion of the workshop, background information about the L07 tool was provided (see Figure 5.34)
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Additional background information is shown in Figure 5.35. Figure 5.35.
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The same elements of nonrecurring congestion defined in the L02 technical portion of the workshop are used in the L07 tool. This tool compares treated versus untreated geometric conditions that the analyst chooses.
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Figure 5.38. Calculating delay.
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The optional detailed user inputs are shown in Figure 5.40. Figure 5.40.
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Figure 5.41. L07 design treatments.
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While it may appear that the L07 tool uses inputs from data sources that may not be available to perform the analysis, at a bare minimum most agencies should be able to use this tool to perform a benefit-cost analysis for these geometric treatments. The scenario that was shown at the workshop was a generic facility type of roadway experiencing nonrecurring congestion.
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Figure 5.43. SHRP 2 reliability tools.
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Figure 5.44. L05 implementing reliability.
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Figure 5.45. Policy and programming discussion.
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Figure 5.46. Performance measures.
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Communicating reliability was the next topic discussed. There are many audiences for the evaluation of travel time reliability, so it is challenging to present the large amount of technical analysis in an effective way that is understandable to the audience and enables them to grasp the implications of the analysis.
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Figure 5.49. Utility of SHRP 2 tools.
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Figure 5.51. Utility of SHRP 2.
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A primary concern of the pilot teams about these tools is whether or not they are providing users with new information. MnDOT, in particular, has established efficient methods for producing congestion and safety reports.
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Loop detectors were the principal source of traffic volumes for the facilities analyzed by the Minnesota team (see Figure 5.54)
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Figure 5.55. Bluetooth.
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SMART Signal data is another source of data for signalized highways. This is a proprietary program where data are processed through special software to provide speed, travel time, and other performance measures.
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Figure 5.58. Crash and incident data sources.
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only affect a portion of it, thus missing the sensors. The Minneapolis-Saint Paul airport data can also provide a reference for weather data.
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Figure 5.61. Work zone data sources.
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Figure 5.62. I-94 westbound example.
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Figure 5.64. L07 lessons learned.
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Figure 5.65. L07 product refinements.
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Figure 5.66. L07 reliability solutions.
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Figure 5.68. Reliability solutions.
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Figure 5.69. Planning and programming.
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Figure 5.70. Implementation issues.
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Figure 5.71. L05 tool applications.
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The presenter explained that there needs to be a balance between minor details and the big picture when new tools are integrated into existing processes. The user needs to step back and "see the forest for the trees." It is anticipated that this will be a difficult transition.
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Conclusions from Utility of SHRP 2 Tools Presenter: Mike Sobolewski of the Minnesota Department of Transportation All of the background information presented up to this point of the workshop was aimed at preparing the audience for the upcoming discussion. There were a few final questions for consideration posed to the audience.
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Figure 5.75. Implementation considerations.
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Figure 5.77. Final thoughts.
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Figure 5.78. Final thoughts.
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Figure 5.79. SHRP 2 reliability tools.
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Figure 5.80. Panelists.
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I-94 Maple Grove to Rogers Presenter: Paul Morris of SRF Consulting Group This is an example of an actual corridor study along I-94 in the northwest metro area that was one of the Minnesota pilot site test segments. This segment is highlighted in Figure 5.81.
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Figure 5.82. I-94 Travel time reliability evaluation.
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Figure 5.83.
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CDF curves were also developed for this example. Figure 5.85 shows the cumulative percentage of traffic that is able to get through the corridor at or below the specified travel time (which is along the x-axis)
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of vehicles on the roadway; it does not take into account whether there is more than one person in a single vehicle. This was one of the criticisms the team had, and the team speculates that there will be future improvements to adjust for occupancy.
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The travel time surface plot for the year 2008 is shown in Figure 5.87. Figure 5.87.
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Figure 5.88. I-94 2009 travel time.
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There was a concrete joint repair project that took place during September, October, and November of 2010, which the tool was able to capture. While this project only decreased the capacity by one third, the backups it caused were several miles long and lasted the entire day.
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Figure 5.91. I-94 annual traffic and delay.
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Figure 5.92. I-35 in Lakeville.
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commuter peak, which is why there is a consistent band of higher VMT between 6:00 a.m.
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Figure 5.95. I-35 in Lakeville travel time.
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Additional comments about the process for this bare minimum analysis are highlighted in Figure 5.97. Figure 5.97.
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Figure 5.98. Level-of-effort graph.
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Figure 5.99. Value of a shoulder.
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Figure 5.101. Value of a shoulder.
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Project assumptions and data sources are highlighted in Figure 5.103. Figure 5.103.
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Figure 5.104. Value of a shoulder analysis results.
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Figure 5.105. Year 2040 hourly nonrecurring delay comparison.
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The conclusions and considerations from this analysis are highlighted in Figure 5.107. Figure 5.107.
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Figure 5.108. Florida reliability.
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Figure 5.110. Reliability indices.
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Transportation (DOT) using this information looks like.
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sources and will apply the results of the analysis to evaluations of projects in spring–summer 2014. Figure 5.113.
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Figure 5.115. Project evaluation process.
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The team selected a group of highway sample sections that would be used to collect and analyze traffic and travel time data to develop this model. The first step was to categorize the highways by road type and facility function, as shown in Figure 5.117.
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Figure 5.118. Highway sample sections.
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Figure 5.119. Annual traffic profile.
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In the end, the team found 109 different unique day types, 25 of which are holiday- or event-related (see Figure 5.121)
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Figure 5.122. INRIX speed data.
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Figure 5.123. INRIX speed data.
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Figure 5.124. Weather/crash/incident data.
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Figure 5.125. Model estimation process.
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Other Applications Presenter: Mike Sobolewski of the Minnesota Department of Transportation There are many other applications where travel time reliability evaluation can be used. There is some functionality of these tools to many different programs, which are highlighted in Figure 5.127 to Figure 5.129.
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Figure 5.128. CMSP secondary screening example application.
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Conclusions and Next Steps Closing Travel Time Reliability Survey Presenter: Renae Kuehl of SRF Consulting Group A travel time survey that was administered at the beginning of the workshop was taken again by the workshop participants, to gauge the level of knowledge they gained after participating in the Minnesota Reliability Workshop. The results from the afternoon survey were compared with the results from the morning survey and are shown (in percentage of total votes)
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Figure 5.131. Question 2 results.
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Figure 5.133. Question 4 results.
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Figure 5.135. Question 6 results.
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Figure 5.137. Question 8results.
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Ongoing SHRP 2 Projects The determination of the reliability ratio is an important factor, as it is an assumed parameter in many reliability analysis tools. This number is different for different highway users, and different users have different needs as far as reliability is concerned.
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Project L07 Tool • Audience members expressed concern about the level of effort required to perform a fully detailed analysis, and they expressed concern that in some cases detailed data would not be available at all. • There was also concern expressed about the level of effort required to perform a systemlevel analysis using this tool.
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CHAPTER 6 REFINED TECHNICAL ANALYSIS Alternative Time Intervals The majority of the reliability analysis performed by the pilot team used 5-minute intervals; however, it was never determined if this was the optimal length. The travel time reliability monitoring system (TTRMS)
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Figure 6.1. Westbound I-94 observation pie charts.
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Figure 6.3. Northbound TH-100 observation pie charts.
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These pie charts show that as the size of the time interval increases, the amount of delay occurring during the periods with nonrecurring conditions increases. This is because the database methodology was to apply nonrecurring conditions to any interval in which they were present for any length of time.
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indicate a strong fit between the data sets. Similarly, correlation values close to one indicate that changes in one data set are reflected in the other.
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Figure 6.6. Total annual delay.
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The team concluded that the ideal time interval appears to be in the 10- to 15-minute range. These time intervals optimize the trade-off between accuracy of aggregate performance measures and staff and computing resources.
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Figure 6.9. Speed-flow plot example.
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Figure 6.11 shows the database the team used to separate the delay caused by nonrecurring factors. The difference between the observed travel time from the nonrecurring congestion (Column B: yellow)
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Figure 6.12. Updated pie chart.
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Figure 6.13. Project L02 example CDF graph with high, medium, and low demand regimes.
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Figure 6.14.
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Figure 6.15. Density vs.
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Figure 6.16. Demand thresholds.
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Figure 6.17. Demand regime thresholds.
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requiring thousands of manual steps to obtain a meaningful sample of data over months or years. Second, along the portion of TH-13 the team was analyzing, only 8 days of data were found to be available in the 2012 to 2013 time frame.
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Table 6.4. L07 Tool Test Scenarios Scenario Specified Information Scenario 1 Number of crashes (default scenario)
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Scenario 3 shows no difference compared to the default scenario. Upon further investigation, it was determined that the L07 tool does not read the number of incidents if the duration is not specified.
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CHAPTER 7 Findings and Recommendations This section highlights the key findings that were discovered through the pilot testing process. These topics are discussed in greater detail throughout this report; however, the following points provide a summary of critical takeaways for individuals and agencies considering adoption or exploration of the SHRP 2 reliability tools.
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Project L07 The L07 project evaluation tool is applicable to freeway facilities and is capable of analyzing one segment with uniform geometry and volume characteristics. When considering use of the L07 tool, it is critical to identify the primary bottleneck location along a congested freeway facility.
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• There is concern over the level of effort to conduct reliability evaluation. In particular, lack of consistency in crash, incident, and road work data sources make linking these congestion causes to unreliable travel times time-consuming.
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References Cambridge Systematics, Inc., Texas A&M Transportation Institute, University of Washington, Dowling Associates, Street Smarts, H Levinson, and H
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APPENDIX A Study Facility Reliability Reports A-1
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Key Terms
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