Proposed Chapters for Incorporating Travel Time Reliability into the Highway Capacity Manual (2013) / Chapter Skim
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Chapter 37/Travel Time Reliability: Supplemental Page 37-i Contents CHAPTER 37 TRAVEL TIME RELIABILITY: SUPPLEMENTAL CONTENTS 1.
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Contents Page 37-ii Chapter 37/Travel Time Reliability: Supplemental LIST OF EXHIBITS Exhibit 37-1 Rankings of U.S. Facilities by Mean TTI and PTI (AM Peak, Midday, and PM Peak Combined)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-iii Contents Exhibit 37-31 Example Problem 1: Study Section Geometry .....................................37-68 Exhibit 37-32 Example Problem 1: Sample Freeway Input Entries for Seed File .......37-69 Exhibit 37-33 Example Problem 1: Demand Ratios for I-40 Case Study (ADT/Mondays in January) ..................................................................................37-70 Exhibit 37-34 Example Problem 1: Consolidated Demand Ratios for I-40 Case Study ............................................................................................................37-70 Exhibit 37-35 Example Problem 1: Percent Time of Year by Season and Demand Pattern ....................................................................................................37-70 Exhibit 37-36 Example Problem 1: Percent Time Weather Categories Present on I-40 by Month ..................................................................................................37-71 Exhibit 37-37 Example Problem 1: Estimated Percent Time Weather Events Present on I-40 by Season ....................................................................................37-72 Exhibit 37-38 Example Problem 1: Mean Duration and Distribution of Incidents by Severity ............................................................................................................37-73 Exhibit 37-39 Example Problem 1: Estimated Percent Time Incidents Present on I-40 Eastbound ................................................................................................37-73 Exhibit 37-40 Example Problem 1: Percent Times for Incident Scenarios in Non-severe Weather .............................................................................................37-74 Exhibit 37-41 Example Problem 1: Schematic of Two Initial Scenarios and Probabilities ..........................................................................................................37-75 Exhibit 37-42 Example Problem 1: Estimated Incident Study Period Scenario Probabilities after Adjustment ..............................................................................37-76 Exhibit 37-43 Example Problem 1: Final Scenario Categorization .............................37-78 Exhibit 37-44 Example Problem 1: Free-flow CAFs and SAFs for Weather on I-40 ..................................................................................................................37-78 Exhibit 37-45 Example Problem 1: CAFs per Open Lane for Incidents on I-40 .........37-79 Exhibit 37-46 Example Problem 1: Travel Time Distribution Results for Different Inclusion Thresholds ............................................................................................37-80 Exhibit 37-47 Example Problem 1: Number of Scenarios and Coverage of Feasible Scenarios ..............................................................................................................37-81 Exhibit 37-48 Example Problem 1: Reliability Performance Measure Results for I-40 ..................................................................................................................37-81 Exhibit 37-49 Example Problem 1: Evaluation of TTI and PTI Results for I-40 ........37-83 Exhibit 37-50 Example Problem 1: Evaluation of Policy TTI and PTI Results for I-40 ..................................................................................................................37-83
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Contents Page 37-iv Chapter 37/Travel Time Reliability: Supplemental
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Chapter 37/Travel Time Reliability: Supplemental Page 37-1 Reliability Values for Selected U.S. Facilities 1.
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Reliability Values for Selected U.S. Facilities Page 37-2 Chapter 37/Travel Time Reliability: Supplemental Percentile Rank Freeways Urban Streets TTI Mean TTI PTI TTI Mean TTI PTI Minimum 1.01 1.02 1.07 1.03 1.06 1.23 Worst 95% 1.02 1.05 1.09 1.09 1.12 1.27 Worst 90% 1.02 1.06 1.13 1.13 1.15 1.29 Worst 85% 1.04 1.06 1.14 1.15 1.16 1.32 Worst 80% 1.05 1.08 1.17 1.17 1.20 1.33 Worst 75% 1.05 1.08 1.22 1.19 1.20 1.35 Worst 70% 1.05 1.09 1.25 1.19 1.22 1.36 Worst 65% 1.06 1.10 1.30 1.20 1.22 1.39 Worst 60% 1.07 1.12 1.34 1.20 1.23 1.41 Worst 55% 1.08 1.15 1.39 1.21 1.23 1.42 Worst 50% 1.10 1.16 1.47 1.23 1.26 1.44 Worst 45% 1.11 1.19 1.57 1.24 1.27 1.47 Worst 40% 1.13 1.23 1.73 1.25 1.28 1.49 Worst 35% 1.14 1.30 1.84 1.25 1.29 1.52 Worst 30% 1.17 1.33 1.97 1.26 1.30 1.54 Worst 25% 1.20 1.39 2.24 1.30 1.34 1.60 Worst 20% 1.26 1.43 2.71 1.33 1.36 1.63 Worst 15% 1.31 1.51 2.90 1.35 1.38 1.70 Worst 10% 1.59 1.78 3.34 1.39 1.47 1.84 Worst 5% 1.75 1.97 3.60 1.45 1.54 1.98 Maximum 2.55 2.73 4.73 1.60 1.66 2.55 Source: Derived from directional values in Exhibit 37-5 through Exhibit 37-10.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-3 Reliability Values for Selected U.S. Facilities Percentile Rank Freeways Urban Streets TTI Mean TTI PTI TTI Mean TTI PTI Minimum 1.02 1.03 1.07 1.05 1.07 1.23 Worst 95% 1.02 1.04 1.08 1.08 1.10 1.27 Worst 90% 1.02 1.05 1.11 1.15 1.18 1.28 Worst 85% 1.02 1.06 1.14 1.16 1.18 1.30 Worst 80% 1.03 1.06 1.15 1.18 1.20 1.33 Worst 75% 1.04 1.08 1.17 1.19 1.21 1.34 Worst 70% 1.05 1.08 1.20 1.19 1.22 1.37 Worst 65% 1.05 1.09 1.21 1.20 1.22 1.39 Worst 60% 1.05 1.09 1.24 1.20 1.23 1.41 Worst 55% 1.06 1.11 1.26 1.21 1.23 1.42 Worst 50% 1.06 1.12 1.32 1.22 1.24 1.45 Worst 45% 1.07 1.13 1.34 1.24 1.27 1.47 Worst 40% 1.09 1.15 1.37 1.25 1.29 1.48 Worst 35% 1.09 1.15 1.43 1.25 1.30 1.51 Worst 30% 1.10 1.17 1.51 1.27 1.32 1.53 Worst 25% 1.12 1.26 1.65 1.30 1.34 1.57 Worst 20% 1.14 1.30 1.92 1.31 1.34 1.60 Worst 15% 1.16 1.32 2.41 1.32 1.35 1.63 Worst 10% 1.17 1.42 2.85 1.33 1.38 1.63 Worst 5% 1.21 1.46 3.16 1.35 1.42 1.86 Maximum 1.31 1.76 3.96 1.47 1.55 2.01 Source: Derived from directional values in Exhibit 37-5 through Exhibit 37-10.
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Reliability Values for Selected U.S. Facilities Page 37-4 Chapter 37/Travel Time Reliability: Supplemental Exhibit 37-5 through Exhibit 37-7 present the source freeway data for the a.m.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-5 Reliability Values for Selected U.S. Facilities Location Roadway Length (mi)
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Reliability Values for Selected U.S. Facilities Page 37-6 Chapter 37/Travel Time Reliability: Supplemental Location Roadway Length (mi)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-7 Reliability Values for Selected U.S. Facilities target free-flow speed for its freeways at the posted speed limit plus 5 mi/h.
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Alternative Freeway Incident Prediction Method Page 37-8 Chapter 37/Travel Time Reliability: Supplemental 2. ALTERNATIVE FREEWAY INCIDENT PREDICTION METHOD As discussed in the Data Acquisition section of Chapter 36, Travel Time Reliability, it is only possible to estimate freeway incident probabilities directly in the rare cases where incident logs are complete and accurate over the entire reliability reporting period.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-9 Alternative Freeway Incident Prediction Method Finally, the probability of having at least one incident occur in demand pattern j is one minus the probability of having no incidents: 𝑃𝑗(> 0)
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Freeway Scenario Generation Page 37-10 Chapter 37/Travel Time Reliability: Supplemental 3. FREEWAY SCENARIO GENERATION INTRODUCTION This section provides details of the freeway scenario generation process.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-11 Freeway Scenario Generation Assumptions The following assumptions are made in generating initial scenarios: • The contributing factors to the travel time variation are independent. The method provides the ability to vary some factors (e.g., demand by weather type)
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Freeway Scenario Generation Page 37-12 Chapter 37/Travel Time Reliability: Supplemental generated, which directly impacts the time required to perform a reliability analysis. The probability of a given demand pattern d is the portion of the reliability reporting period PDP(d)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-13 Freeway Scenario Generation If local incident probabilities are not available for a facility, then local crash rates or crash rates predicted from the HERS model (7) can be used along with an incident-to-crash ratio to calculate the probabilities of different incident types.
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Freeway Scenario Generation Page 37-14 Chapter 37/Travel Time Reliability: Supplemental probabilities of weather PWDP(w,m) for weather type w in month m, and of incidents PDINPC(i,m)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-15 Freeway Scenario Generation wise" distinguishes it from other types of probabilities, such as VMT-wise, count-wise, or length-wise probabilities. Event Time-Wise Probability of Occurrence WEATHER EVENT Medium rain 600 min duration50 study periods × 4 h/study period × 60 min/h = 0.05 Non-severe weather 1 − 0.05 = 0.95 INCIDENT EVENT One-lane closure 900 min duration50 study periods × 4 h/study period × 60 min/h = 0.075 No Incident 1 − 0.075 = 0.925 Initial Scenario Development The initial scenario generation procedure is employed to generate different operational conditions on the freeway facility.
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Freeway Scenario Generation Page 37-16 Chapter 37/Travel Time Reliability: Supplemental Study Period Scenario Development Next, the event durations are introduced. Based on historical data, the average durations are 49 min and 32 min for the one-lane closure incident and the medium rain event, respectively.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-17 Freeway Scenario Generation event conditions are present in the remaining 13 analysis periods. Finally, in study period scenario 4 (medium rain and a 1-lane-closure incident)
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Freeway Scenario Generation Page 37-18 Chapter 37/Travel Time Reliability: Supplemental It is possible that the set of equilibrium equations could yield infeasible results (meaning that one of the resulting 𝜋𝑖 values is negative)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-19 Freeway Scenario Generation Operational Scenario Probability = π1 Operational Scenario Probability = π2 / 2 Operational Scenario Probability = π3 / 18 Operational Scenario Probability = π4 / 18 Demand Demand and weather Demand and incident Demand, weather, and incident Exhibit 37-16 Events Occurring During Each Analysis Period of Selected Operational Scenarios
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Freeway Scenario Generation Page 37-20 Chapter 37/Travel Time Reliability: Supplemental Algorithm Assumptions The following assumptions are incorporated into the algorithm for developing study period scenario probabilities: • The duration of incident events may be altered in the development of the operational scenarios later in the process without altering the study period probabilities. This assumption is not overly severe, since the three possible incident durations are selected to be at, below, and above the mean duration.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-21 Freeway Scenario Generation Initial Scenario # Demand Pattern Weather Type Incident Type Initial Scenario Probability Scenario Category 4 1 Normal weather No incident 8.84736% 1 16 1 Normal weather Shoulder closed 3.00484% 3 28 1 Normal weather 1 lane closed 0.90935% 3 29 1 Light snow No incident 0.44710% 2 42 1 Normal weather 2 lanes closed 0.23029% 3 45 1 Normal weather 3 lanes closed 0.18409% 3 48 1 Light snow Shoulder closed 0.14825% 4 49 1 Medium rain No incident 0.14309% 2 68 1 Low visibility No incident 0.06633% 2 74 1 Medium rain Shoulder closed 0.05025% 4 77 1 Light snow 1 lane closed 0.04479% 4 88 1 Low visibility Shoulder closed 0.02332% 4 96 1 Light-medium snow No incident 0.01666% 2 99 1 Medium rain 1 lane closed 0.01524% 4 104 1 Light snow 2 lanes closed 0.01134% 4 117 1 Light snow 3 lanes closed 0.00906% 4 120 1 Low visibility 1 lane closed 0.00707% 4 128 1 Light-medium snow Shoulder closed 0.00531% 4 138 1 Medium rain 2 lanes closed 0.00386% 4 146 1 Medium rain 3 lanes closed 0.00309% 4 163 1 Low visibility 2 lanes closed 0.00179% 4 164 1 Light-medium snow 1 lane closed 0.00160% 4 166 1 Low visibility 3 lanes closed 0.00143% 4 203 1 Light-medium snow 2 lanes closed 0.00040% 4 209 1 Light-medium snow 3 lanes closed 0.00032% 4 Conceptual Approach The study period probability adjustment method creates weather or incident events in the study period with a predetermined duration. Thus, the remaining time periods in that study period actually describe another scenario (usually the normal condition, scenario category 1)
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Freeway Scenario Generation Page 37-22 Chapter 37/Travel Time Reliability: Supplemental If the probability of occurrence of a study period is given as Π, then the probability of occurrence P(s) of a particular scenario category s that appears i times within the study period with individual durations ts,i is as follows 𝑃(𝑠)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-23 Freeway Scenario Generation Exhibit 37-19 Probability Calculation Methodology for Study Period Scenarios
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Freeway Scenario Generation Page 37-24 Chapter 37/Travel Time Reliability: Supplemental Incident Categories I Weather Categories w Normal Medium Rain Low Visibility Light-Med. Snow Light Snow Total No incident 8.84737% 0.14309% 0.06633% 0.01666% 0.44710% 9.52054% Shoulder closed 3.00484% 0.05025% 0.02332% 0.00531% 0.14825% 3.23197% 1 lane closed 0.90935% 0.01524% 0.00707% 0.00160% 0.04479% 0.97805% 2 lanes closed 0.23029% 0.00386% 0.00179% 0.00040% 0.01134% 0.24769% 3 lanes closed 0.18409% 0.00309% 0.00143% 0.00032% 0.00906% 0.19799% Total 13.17593% 0.21553% 0.09995% 0.02430% 0.66053% 14.17625% Note: Med.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-25 Freeway Scenario Generation condition scenario (category 1) -- for example, the impact of wet pavement after a rain event has ended -- that effect is ignored in the method.
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Freeway Scenario Generation Page 37-26 Chapter 37/Travel Time Reliability: Supplemental There is no need to compute the residual probabilities for type N scenarios; therefore the remainder of this step focuses on type W and I scenarios. In this step a portion of the probability of each demand-plus-weather scenario (category 2)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-27 Freeway Scenario Generation Step 6: Check that Residual Probabilities Are Lower Than Category 2 and 3 Initial Scenario Probabilities If 𝜋𝑤′ and 𝜋𝑖′′ are greater than the probability of category 2 and 3 scenarios, it means that the impact of the difference between the weather and incident event durations ∆𝑤𝑖 is larger than the impact of the expected demand-plus-weather, or demand-plus-incident initial scenarios. In this case, the shorter event must be modeled with a longer duration in Step 3 and the procedure needs to be restarted from Step 3.
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Freeway Scenario Generation Page 37-28 Chapter 37/Travel Time Reliability: Supplemental Step 10: Calculate Category 1 Scenario Probability The difference between the sum of probabilities of the initial scenarios, and the current sum of probabilities for category 2–4 study period scenarios is assigned to the category 1 (normal condition) scenario.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-29 Freeway Scenario Generation given a probability equal to 𝜋𝑤𝑖 / (2 × 3 × 3 × A)
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Freeway Scenario Generation Page 37-30 Chapter 37/Travel Time Reliability: Supplemental Two of these items can be altered in a given operational scenario: number of operational lanes and free-flow speed, depending on the type of weather and/or incident event that occurs in the scenario. Demand Adjustments Demand in Data Poor Environments When agencies have no access to detailed demand information for the freeway facility, daily demands are computed based on AADT estimates for the facility, combined with day-of-week and month-of-year demand ratios.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-31 Freeway Scenario Generation Capacity and Speed Adjustments General Process Modeling an incident or weather event on a freeway facility is done by (a) applying a Capacity Adjustment Factor (CAF)
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Freeway Scenario Generation Page 37-32 Chapter 37/Travel Time Reliability: Supplemental C = original segment capacity (pc/h/ln) , CAF = capacity adjustment factor, and 𝑣𝑝 = segment flow rate (pc/h/ln)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-33 Freeway Scenario Generation Average Speed in Equation Ramp influence area )
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Freeway Scenario Generation Page 37-34 Chapter 37/Travel Time Reliability: Supplemental SAFs for Weaving Segments The equations for calculating the speed of weaving and nonweaving vehicles in weaving segments (Equations 12-18 through 12-20) are modified by multiplying each occurrence of FFS by SAF, and the space mean speed of all vehicles in the weaving segment (Equation 12-21)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-35 Urban Street Scenario Generation 4. URBAN STREET SCENARIO GENERATION WEATHER EVENT PREDICTION The weather event procedure is used to predict weather events during the reliability reporting period.
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Urban Street Scenario Generation Page 37-36 Chapter 37/Travel Time Reliability: Supplemental sT = standard deviation of daily mean temperature in a month (= 5.0)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-37 Urban Street Scenario Generation where trd,m = total rainfall for the rain event occurring on day d of month m (in./event) , Rtd = random number for rainfall total for day d (= Rrd)
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Urban Street Scenario Generation Page 37-38 Chapter 37/Travel Time Reliability: Supplemental where tsd,m = start of rain event on day d of month m (h) , 24 = number of hours in a day (h/day)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-39 Urban Street Scenario Generation average precipitation rate and average total rainfall per event, respectively, by the ratio of snow depth to rain depth. This ratio is estimated at 10 in./in.
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Urban Street Scenario Generation Page 37-40 Chapter 37/Travel Time Reliability: Supplemental If the traffic volumes provided in the base dataset and the alternative datasets are computed using planning procedures, then the volumes in the dataset are based on the average day of week and month of year. In this situation, the adjustment factors for day of week and month of year are set to a value of 1.0.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-41 Urban Street Scenario Generation Nhsp = total number of hours in Ny years with snow or ice on pavement and not snowing (h) , CFAFrf = crash frequency adjustment factor for rainfall, CFAFwp = crash frequency adjustment factor for wet pavement (not raining)
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Urban Street Scenario Generation Page 37-42 Chapter 37/Travel Time Reliability: Supplemental Step 3: Determine Whether an Incident Occurs During this step, each of the 24 hours in the subject day is examined to determine if an incident occurs. The analysis separately considers each street location (i.e., intersection and segment)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-43 Urban Street Scenario Generation ( ) dmoyddowdhhoddhweaistrdhdhweaistr fff Fi fi ,,,, )
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Urban Street Scenario Generation Page 37-44 Chapter 37/Travel Time Reliability: Supplemental weather condition wea(h,d) during hour h and day d, event type con, lane location lan, and severity sev.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-45 Urban Street Scenario Generation h and day d, event type con, lane location lan, and severity sev (= 0.8 -- distr(i)
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Urban Street Scenario Generation Page 37-46 Chapter 37/Travel Time Reliability: Supplemental with ∑ = = 12 1 )
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Chapter 37/Travel Time Reliability: Supplemental Page 37-47 Urban Street Scenario Generation )
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Urban Street Scenario Generation Page 37-48 Chapter 37/Travel Time Reliability: Supplemental They are also made to the saturation flow rate at intersections influenced by an incident or a weather event. The speed is also adjusted for segments influenced by an incident or a weather event.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-49 Urban Street Scenario Generation The factors obtained from Equation 37-65 apply when there is some precipitation falling. If the pavement is wet and there is no rainfall, then the adjustment factor is 0.95.
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Urban Street Scenario Generation Page 37-50 Chapter 37/Travel Time Reliability: Supplemental with dapnintidapnintipdodapnintifidapnintiic IIIb ,,) ,(,other,,)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-51 Urban Street Scenario Generation Step 6: Compute Traffic Demand Volumes Adjust Movement Volumes During this step, the volume for each movement is adjusted using the appropriate hour-of-day, day-of-week, and month-of-year factors to estimate the average hourly flow rate for the subject analysis period. The following equation is used for this purpose.
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Urban Street Scenario Generation Page 37-52 Chapter 37/Travel Time Reliability: Supplemental where fint(i) ,j,h,d = adjustment factor used to estimate the standard deviation of demand flow rate for movement j at intersection i during hour h and day d, PHFint(i)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-53 Urban Street Scenario Generation Step 7: Compute Speed for Segments Additional Delay During this step, the effect of incidents and weather on segment speed is determined. This effect is added to the HCM dataset as an additional delay incurred along the segment.
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Urban Street Scenario Generation Page 37-54 Chapter 37/Travel Time Reliability: Supplemental Iother,seg(i) ,n,ap,d = indicator variable for non-crash incident in the direction of travel served by NEMA phase n on segment i during analysis period ap and day d (= 1.0 if non-crash incident, 0.0 otherwise)
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Chapter 37/Travel Time Reliability: Supplemental Page 37-55 Measuring Reliability in the Field 5. MEASURING RELIABILITY IN THE FIELD This section provides a recommended method for measuring reliability in the field.
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Measuring Reliability in the Field Page 37-56 Chapter 37/Travel Time Reliability: Supplemental Loop Detectors and Similar Point Measures of Speed Loop sensors (or similar point measures of speed) are spaced perhaps as close as one-third to one-half mile apart, but can be much farther apart.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-57 Measuring Reliability in the Field The two measurement methods, since they sample the three-dimensional reliability space differently, will produce slightly different estimates of the travel time reliability distribution, as illustrated for one freeway in Exhibit 37-27. However, the differences between the methods will generally be less than the differences in reliability between different peak periods.
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Measuring Reliability in the Field Page 37-58 Chapter 37/Travel Time Reliability: Supplemental Source: Kittelson & Associates, Inc. Note: I-80 westbound, Contra Costa County, California.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-59 Measuring Reliability in the Field 3. Quality Check Data.
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Measuring Reliability in the Field Page 37-60 Chapter 37/Travel Time Reliability: Supplemental 7. Compute TTIs for Time Periods.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-61 Measuring Reliability in the Field 4. Compute Facility Travel Times for Each Analysis Period.
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Example Problem Page 37-62 Chapter 37/Travel Time Reliability: Supplemental 6. EXAMPLE PROBLEM EXAMPLE PROBLEM 1: EXISTING FREEWAY RELIABILITY Objective This example problem illustrates the process of: 1.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-63 Example Problem • Data required for an HCM freeway facility analysis (Chapter 10) : o Facility volumes by 15-min analysis periods (time slices)
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Example Problem Page 37-64 Chapter 37/Travel Time Reliability: Supplemental 5. Estimating incident frequencies.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-65 Example Problem instances of free-flow conditions, which will tend to mask or wash out the reliability problems. In this example, an examination of the facility over several days has determined the general spatial and temporal boundaries of congestion on the facility under fair weather, non-incident conditions.
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Example Problem Page 37-66 Chapter 37/Travel Time Reliability: Supplemental If an agency wishes to focus on non-weather effects and avoid vacation effects, then a single season may be selected, rather than a full year. The selection of the appropriate reliability reporting period hinges on the agency's purpose for the analysis.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-67 Example Problem The second standard is set based on the agency's congestion management goal of operating its freeways at 40 mi/h or better during the majority of the peak periods within the year. This particular standard requires that a modified travel time performance index, called the Policy Index (PI)
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Example Problem Page 37-68 Chapter 37/Travel Time Reliability: Supplemental Section A Section B Section C There were no extended grades in excess of 2% for longer than 0.5 mi on the facility (see page 11-15) , and the facility has a general level vertical profile, so a general terrain category of "level" was used to characterize the vertical geometry of the facility.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-69 Example Problem Coding Alternative Datasets As there is no need to account for special events or work zones, no alternative datasets need to be created. If there had been a need for them, they would have been developed in the same way as the base dataset, with appropriate modifications to the input data to reflect changes in demand, geometry, and traffic control.
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Example Problem Page 37-70 Chapter 37/Travel Time Reliability: Supplemental Day of Week Month Monday Tuesday Wednesday Thursday Friday January 1.00 1.03 1.04 1.05 1.08 February 0.94 1.01 1.04 1.09 1.14 March 1.04 1.07 1.06 1.11 1.17 April 1.07 1.09 1.10 1.16 1.22 May 1.08 1.11 1.11 1.16 1.21 June 1.08 1.09 1.07 1.14 1.18 July 1.08 1.07 1.10 1.15 1.18 August 1.05 1.05 1.06 1.09 1.16 September 1.02 1.02 1.02 1.07 1.15 October 1.05 1.05 1.07 1.11 1.16 November 0.97 1.00 1.04 1.08 1.07 December 0.97 0.96 0.99 0.92 1.01 Entries in Exhibit 37-33 are ADT demand adjustments for a given combination of day and month relative to ADT for a Monday in January. Exhibit 37-34 shows the consolidated table of demand ratios for the example problem.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-71 Example Problem A 10-year weather history of NWS METARS data was obtained for the nearby Raleigh-Durham Airport from Weather Underground. The data were filtered to eliminate "unknown" (-9999)
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Example Problem Page 37-72 Chapter 37/Travel Time Reliability: Supplemental Season Medium Rain Heavy Rain Light Snow LM Snow Low Visibility Normal Weather Total Winter 1.496% 0.000% 4.745% 0.175% 0.679% 92.905% 100.000% Spring 0.797% 0.802% 0.352% 0.000% 0.000% 98.049% 100.000% Summer 0.335% 0.335% 0.000% 0.000% 0.000% 99.330% 100.000% Fall 1.440% 0.180% 0.000% 0.000% 0.000% 98.380% 100.000% Total 1.010% 0.332% 1.229% 0.042% 0.163% 97.223% 100.000% Note: LM = light to medium. Seasonal weather probabilities are assumed to apply identically to all demand patterns within the season.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-73 Example Problem DM(m) = Demand multiplier for month m (unitless)
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Example Problem Page 37-74 Chapter 37/Travel Time Reliability: Supplemental Step 6: Scenario Generation Initial Scenario Development The initial scenario represents a specific combination of a demand level, a weather type, and an incident type. The demand levels are specified by month and day of week rather than by volume level.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-75 Example Problem All entries are percent time within the reliability reporting period when the specified conditions are present on facility. Not shown are percentages for rain, snow, and low visibility conditions.
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Example Problem Page 37-76 Chapter 37/Travel Time Reliability: Supplemental The simplest approach to overcome these differences is to readjust the relevant initial scenario probabilities such that the original incident probability is honored in all cases. This can be done using a simple equation to estimate the true study period scenario probability Π from Equation 37-80.
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Chapter 37/Travel Time Reliability: Supplemental Page 37-77 Example Problem • Start at the beginning or the middle of the study period; • Located at the beginning, middle, or end of the facility; and • Occurring for the 25th, 50th, or 75th percentile highest duration for a given incident type. Note that some operational scenario options may be prohibited.
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Example Problem Page 37-78 Chapter 37/Travel Time Reliability: Supplemental Scenario Type Number of Operational Scenarios Percent of Total No incidents and non-severe weather 12 0.6% No incidents and severe weather 66 3.2% Incidents and non-severe weather 528 25.7% Incidents and severe weather 1452 70.6% Total 2,058 100.0% It should be noted that the percentages shown here are not the probabilities of occurrence. They indicate the proportionate number of HCM analyses that will be performed on each scenario type for the reliability analysis.
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From page 194... ...
Chapter 37/Travel Time Reliability: Supplemental Page 37-79 Example Problem important to note that the factors in Exhibit 37-45 do not include the effect of the number of closed lanes. In other words, both the number of lanes closed and the resulting capacity per open lane on the segment must be specified by the user.
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From page 195... ...
Example Problem Page 37-80 Chapter 37/Travel Time Reliability: Supplemental Inclusion Thresholds As mentioned earlier, the procedure can generate several thousand scenarios many of which may have exceptionally low or exactly zero probability. In addition, some scenarios may be infeasible.
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From page 196... ...
Chapter 37/Travel Time Reliability: Supplemental Page 37-81 Example Problem Increasing the value of the inclusion threshold reduces the number of scenarios and consequently the runtime; however, at the same time it reduces the percentage of the coverage of feasible scenarios (Exhibit 37-47)
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From page 197... ...
Example Problem Page 37-82 Chapter 37/Travel Time Reliability: Supplemental analysis periods, squaring each result, weighting each result by its probability, and summing the results. The square root of the summed results was then taken to obtain the semi-standard deviation.
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From page 198... ...
Chapter 37/Travel Time Reliability: Supplemental Page 37-83 Example Problem peak period in the SHRP 2 L08 dataset, and consequently do not meet the agency's threshold of acceptability for reliable performance. Statistic I-40 Reliability Agency Threshold of Acceptability Conclusion Mean TTI 1.97 < 1.93 Marginally Unsatisfactory PTI 5.34 < 3.55 Unsatisfactory The agency's congestion management goal is to operate its freeways at better than 40 mi/h during 50% of the peak periods of the year and better than 25 mi/h during 95% of the peak periods during the year.
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From page 199... ...
References Page 37-84 Chapter 37/Travel Time Reliability: Supplemental 7.
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