Proposed Chapters for Incorporating Travel Time Reliability into the Highway Capacity Manual (2013) / Chapter Skim
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Chapter 36/Travel Time Reliability Page 36-i Contents CHAPTER 36 TRAVEL TIME RELIABILITY CONTENTS 1.
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Contents Page 36-ii Chapter 36/Travel Time Reliability Example Problem 3: Incident Management Treatment ....................................... 36-69 Example Problem 4: Safety Treatment ................................................................
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Chapter 36/Travel Time Reliability Page 36-iii Contents LIST OF EXHIBITS Exhibit 36-1 High-level Representation of the Method for Estimating the Travel Time Distribution .................................................................................................. 36-5 Exhibit 36-2 General Data Categories Required for a Reliability Evaluation ..............
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Contents Page 36-iv Chapter 36/Travel Time Reliability Exhibit 36-30 Urban Street Weather-Related Default Values .................................... 36-51 Exhibit 36-31 Urban Street Incident Default Values ..................................................
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Chapter 36/Travel Time Reliability Page 36-v Contents Exhibit 36-58 Example Problem 6: Urban Street Facility ...........................................36-75 Exhibit 36-59 Example Problem 6: Input Data Needs and Sources ............................36-76 Exhibit 36-60 Example Problem 6: Intersection #1 Signal Timing Data ....................36-77 Exhibit 36-61 Example Problem 6: Sample Weather Data for Lincoln, Nebraska ......36-80 Exhibit 36-62 Example Problem 6: Sample Generated Weather Events .....................36-81 Exhibit 36-63 Example Problem 6: Sample Demand Profile Calculations .................36-83 Exhibit 36-64 Example Problem 6: Locally Available Crash Frequency Data ...........36-84 Exhibit 36-65 Example Problem 6: Computation of Crash Frequency by Weather Type ......................................................................................................................36-85 Exhibit 36-66 Example Problem 6: Incident Determination for April 6, 9:00 a.m., for Segment 1-2 ....................................................................................................36-87 Exhibit 36-67 Example Problem 6: Incident Determination for January 10, 7:00 a.m., for Segment 1-2 ...................................................................................36-87 Exhibit 36-68 Example Problem 6: Sample Calculation of Incident Duration ............36-88 Exhibit 36-69 Example Problem 6: Reliability Performance Measure Results ...........36-90 Exhibit 36-70 Example Problem 6: Eastbound Travel Time Distribution ...................36-91 Exhibit 36-71 Example Problem 6: Confidence Interval Calculation for Eastbound Direction .............................................................................................36-92 Exhibit 36-72 Example Problem 6: Annual VHD by Cause .......................................36-92 Exhibit 36-73 Example Problem 6: Percentage of Annual VHD by Cause .................36-92 Exhibit 36-74 Example Problem 7: Results for Strategy 1 ..........................................36-95 Exhibit 36-75 Example Problem 7: Results for Strategy 2 ..........................................36-96 Exhibit 36-76 Example Problem 7: Results for Strategy 3 ..........................................36-96
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Contents Page 36-vi Chapter 36/Travel Time Reliability
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Chapter 36/Travel Time Reliability Page 36-1 Introduction 1. INTRODUCTION Travel time reliability reflects the distribution of travel time of trips using a facility over an extended period of time.
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Introduction Page 36-2 Chapter 36/Travel Time Reliability can get. This chapter's variability and reliability performance measures can be used as the basis for quantifying the degree of severity of level of service (LOS)
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Chapter 36/Travel Time Reliability Page 36-3 Introduction β’ Travel Time. The time required for a motorized vehicle to travel the full length of the facility from mainline entry to mainline exit points without leaving the facility or stopping for reasons not related to traffic conditions or traffic control.
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Introduction Page 36-4 Chapter 36/Travel Time Reliability capacity, geometry, and traffic control inputs to the facility model with each repetition (scenario)
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Chapter 36/Travel Time Reliability Page 36-5 Introduction compiled into the facility's travel time distribution. This distribution can then be used to develop a variety of reliability and variability performance measures for the facility.
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Introduction Page 36-6 Chapter 36/Travel Time Reliability describe base conditions (particularly demand and factors influencing capacity and free-flow speed) when work zones and special events are not present.
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Chapter 36/Travel Time Reliability Page 36-7 Introduction chapter's methods and obtain reasonable results. At the same time, the method allows analysts in "data rich" regions to provide local data for these inputs when the most accurate results are desired.
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Introduction Page 36-8 Chapter 36/Travel Time Reliability Demand Pattern Data Demand pattern data are used by the reliability method to adjust base demands to reflect demands during all the other portions of the reliability reporting period. Both freeway and urban street facilities require day-of-week and month-of-year variability data.
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Chapter 36/Travel Time Reliability Page 36-9 Introduction Weather Data The reliability method uses weather data to adjust the facility's capacity to reflect the effects of weather events on operations. The urban streets method also optionally allows adjustments to demand based on the presence of weather conditions.
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Introduction Page 36-10 Chapter 36/Travel Time Reliability Default values are available for each of these statistics for 284 locations in the U.S. based on data from 2001β2010.
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Chapter 36/Travel Time Reliability Page 36-11 Introduction Urban Streets Chapter 16, Urban Street Facilities, defines segments as including portions of their bounding intersections (segments extend from the upstream intersection stop bar to downstream intersection stop bar)
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Introduction Page 36-12 Chapter 36/Travel Time Reliability Crash Frequency Adjustment Factors for Inclement Weather Inclement weather conditions can increase the likelihood of crashes. Crash frequency adjustment factors are required for the following conditions: β’ Rainfall, β’ Snowfall, β’ Wet pavement (not raining)
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Chapter 36/Travel Time Reliability Page 36-13 Introduction β’ Proportion of breakdown and other non-crash incidents by street location and lane location (12 total values) ; proportions should total 1.000 for a given street location and lane location combination.
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Introduction Page 36-14 Chapter 36/Travel Time Reliability procedures, they are assumed to represent average day volumes. In this case, a date does not need to be provided by the analyst.
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Chapter 36/Travel Time Reliability Page 36-15 Introduction LIMITATIONS OF THE METHODOLOGY Because the reliability methods are based on applying the freeway and urban streets methodologies multiple times, they inherit the limitations of those methodologies, as described in Chapters 10 and 16β18, respectively. The reliability methods have additional limitations as described below.
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Introduction Page 36-16 Chapter 36/Travel Time Reliability detectors, signal heads, and controller hardware. A failure of one or more of these elements typically results in poor facility operation.
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Chapter 36/Travel Time Reliability Page 36-17 Concepts 2. CONCEPTS As travel time reliability methods are new to the HCM, reliability concepts do not appear in Volume 1.
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Concepts Page 36-18 Chapter 36/Travel Time Reliability Performance Measures Derived from the Travel Time Distribution The travel time distribution can be used to derive a variety of performance measures that describe different aspects of reliability. These include: β’ Percentile-based measures, such as the 95th percentile travel time; β’ On-time measures, such as the percent of trips completed within a defined travel time threshold; β’ Failure measures, such as the percent of trips that exceed a travel time threshold; and β’ Statistical descriptors of the distribution, such as standard deviation and kurtosis.
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Chapter 36/Travel Time Reliability Page 36-19 Concepts the travel time distribution, TTIs are often given as a stated percentile travel time (50th, 80th, and 95th are widely used) , or as a mean TTI, when mean travel time is used in the numerator.
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Concepts Page 36-20 Chapter 36/Travel Time Reliability Measures Describing Unreliability When one measures or predicts travel times over a long period of time (e.g., a year) , a distribution of travel times results.
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Chapter 36/Travel Time Reliability Page 36-21 Concepts comparing a facility's operation to that of those shown in these exhibits, as the analyst's facility may have different characteristics than the sample of facilities. These data are derived from field measurements.
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Concepts Page 36-22 Chapter 36/Travel Time Reliability Note: TTI = travel time index (50th percentile travel time divided by base travel time)
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Chapter 36/Travel Time Reliability Page 36-23 Concepts Note: TTI = travel time index (50th percentile travel time divided by base travel time)
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Concepts Page 36-24 Chapter 36/Travel Time Reliability detailed hourly weather data needed for a freeway facility analysis is available from larger airport weather stations and can be obtained from the NCDC website or other online sources (e.g., 3)
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Chapter 36/Travel Time Reliability Page 36-25 Concepts Work Zones A schedule of long-term work zones should be obtained from the roadway operating agency, indicating the days and times when the work zone will be in effect and the portions of the roadway that will be affected. Work zones that vary in intensity (e.g., one lane closed on some days and two lanes closed on others)
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Concepts Page 36-26 Chapter 36/Travel Time Reliability Minimum Speed Policy If the agency has a minimum acceptable facility speed policy, this information can be used to compute the reliability statistics instead of the freeflow speed. It is then relatively easy to determine the extent to which the facility meets the agency's target performance level by comparing the computed reliability statistic to the target value of 1.00.
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Chapter 36/Travel Time Reliability Page 36-27 Concepts Average Delay Per Trip = 3,600 Γ Length πΉπΉπ Γ (πππΌ β 1) Average Delay Per Mile = 3,600 Γ 1 πΉπΉπ Γ (πππΌ β 1)
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Concepts Page 36-28 Chapter 36/Travel Time Reliability Translating PTI Results into HCM LOS for Freeways The PTI provides the ratio of the 95th percentile travel time to the free-flow travel time. This value can be translated into the equivalent HCM LOS by converting the PTI to equivalent mean speed, converting the speed to the equivalent density, and looking up the LOS range for the freeway: π(95%)
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Chapter 36/Travel Time Reliability Page 36-29 Concepts speed ratio is compared to the urban street LOS criteria in Exhibit 16-4 to determine if the facility will operate at a LOS acceptable to the agency at least 95% of the time. Diagnosing the Causes of Reliability Problems Exhibit 36-10 identifies seven sources of congestion and unreliability, and shows how they interact with each other.
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Concepts Page 36-30 Chapter 36/Travel Time Reliability Generating a Simplified Matrix of Causes Identifying patterns of results in several thousand scenarios is impractical, so it is recommended that the analyst consolidate the many scenarios into a matrix of congestion causes along the lines of Exhibit 36-11. This is best done by combining similar scenarios that individually contribute less than 1% to annual delay.
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Chapter 36/Travel Time Reliability Page 36-31 Concepts source of delay on this example facility. High-demand conditions account for 65% of the annual delay on the facility.
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Freeway Facility Methodology Page 36-32 Chapter 36/Travel Time Reliability 3. FREEWAY FACILITY METHODOLOGY OVERVIEW This section describes the methodology for evaluating the reliability of a freeway facility.
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Chapter 36/Travel Time Reliability Page 36-33 Freeway Facility Methodology Base Dataset The base dataset contains all the required input data for the Chapter 10 freeway facility. Some data are specific to the freeway facility being studied.
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Freeway Facility Methodology Page 36-34 Chapter 36/Travel Time Reliability Facility Evaluation In the facility evaluation step, each scenario is provided to the core HCM freeway facility methodology for analysis. The performance measures of interest to the evaluation -- in particular, travel time -- are calculated for each scenario and stored.
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Chapter 36/Travel Time Reliability Page 36-35 Freeway Facility Methodology first example from above, the volumes associated with all analysis periods on Fridays in May would be multiplied by 1.21 from their base values.) If demand does not vary significantly between certain days or certain months, the analyst may choose to combine days or months together to reduce the total number of scenarios that will be generated and calculated (thus reducing the analysis time)
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Freeway Facility Methodology Page 36-36 Chapter 36/Travel Time Reliability Weather Event CAF SAF Medium rain 0.93 0.93 Heavy rain 0.86 0.92 Light snow 0.96 0.87 Light-medium snow 0.91 0.86 Medium-heavy snow 0.89 0.84 Heavy snow 0.78 0.83 Severe cold 0.92 0.93 Low visibility 0.90 0.94 Very low visibility 0.88 0.92 Minimal visibility 0.90 0.92 Non-severe weather 1.00 1.00 Source: Kittelson & Associates et al.
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Chapter 36/Travel Time Reliability Page 36-37 Freeway Facility Methodology maintaining only 75% of the remaining four open lanes' capacities. The end result is that only three lanes worth (50%)
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Freeway Facility Methodology Page 36-38 Chapter 36/Travel Time Reliability events, and assuming 12 demand pattern scenarios, 22 weather scenarios, and 91 incident scenarios, it is possible to generate up to 24,000 scenarios for a facility. In reality, many of the combinations do not exist or are negligible (e.g., snow in the summer in most places)
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Chapter 36/Travel Time Reliability Page 36-39 Freeway Facility Methodology Freeway Facilities Methodological Enhancements This section summarizes enhancements to the HCM 2010 freeway facilities method presented in Chapter 10 that have been implemented to make the method "reliability-ready." Details of these enhancements are provided in Chapter 37, Travel Time Reliability: Supplemental. Concurrent SAF and CAF Implementation on HCM Segments To remain in general compliance with the HCM 2010 freeway facilities methodology, the speed prediction model (Equation 25-1)
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Freeway Facility Methodology Page 36-40 Chapter 36/Travel Time Reliability Note: FFS = free-flow speed, CAF = crash adjustment factor. Queue-Discharge Flow Rate To more realistically model queue propagation and dissipation on congested freeway facilities, the freeway reliability methodology allows the analyst to specify a capacity loss due to freeway breakdown.
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Chapter 36/Travel Time Reliability Page 36-41 Freeway Facility Methodology β’ Facility TTI, based on a weighted average of the probabilities associated with each TTI observation. Each 15-min analysis period contributes one data point to the overall facility travel time distribution.
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Urban Street Methodology Page 36-42 Chapter 36/Travel Time Reliability 4. URBAN STREET METHODOLOGY OVERVIEW This section describes the methodology for evaluating the reliability of an urban street facility.
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Chapter 36/Travel Time Reliability Page 36-43 Urban Street Methodology Data Depository Every urban street reliability analysis requires a base dataset. This dataset describes the traffic demand, geometry, and signal timing conditions for the intersections and segments along the facility during the study period, when no work zones are present and no special events occur.
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Urban Street Methodology Page 36-44 Chapter 36/Travel Time Reliability shoulder, in one lane, or in multiple lanes. The procedure incorporates weather and traffic demand variation information from the previous procedures when generating incidents.
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Chapter 36/Travel Time Reliability Page 36-45 Urban Street Methodology Next, the selected performance measure data are summarized using the following statistics: β’ Average; β’ Standard deviation; β’ Skewness; β’ Median; β’ 10th, 80th, 85th, and 95th percentiles; and β’ Number of observations. In addition, the average base free-flow speed is always reported.
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Urban Street Methodology Page 36-46 Chapter 36/Travel Time Reliability Multiple Study Periods The geometric design elements, traffic control features (including signal timing plans) , and directional distribution of traffic are assumed to be constant during the study period.
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Chapter 36/Travel Time Reliability Page 36-47 Urban Street Methodology numbers are used for all evaluations. With this approach, the results from an evaluation of one alterative can be compared with those from an evaluation of the baseline condition.
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Applications Page 36-48 Chapter 36/Travel Time Reliability 5. APPLICATIONS DEFAULT VALUES This section provides default values for much of the input data used by this chapter's reliability methodologies.
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Chapter 36/Travel Time Reliability Page 36-49 Applications Note: Ratios represent demand relative to a Monday in January. Weather Events Weather event probabilities by month of each weather event for 101 U.S.
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Applications Page 36-50 Chapter 36/Travel Time Reliability Urban Streets The urban street default values have been derived from the best available research and data at the time of writing. Some of these values are based on the findings of several research projects and others are based on an aggregation of data from several agency databases.
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Chapter 36/Travel Time Reliability Page 36-51 Applications Month Expressway Principal Arterial Minor Arterial January February March April May June July August September October November December 0.802 0.874 0.936 0.958 1.026 1.068 1.107 1.142 1.088 1.069 0.962 0.933 0.831 1.021 1.030 0.987 1.012 1.050 0.991 1.054 1.091 0.952 0.992 0.938 0.881 0.944 1.016 0.844 1.025 1.060 1.150 1.110 1.081 1.036 0.989 0.903 Source: Hallenbeck et al.
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Applications Page 36-52 Chapter 36/Travel Time Reliability pavement hours fcdry. The crash adjustment factor for the weather condition is computed as the ratio of the two frequencies (i.e., CAFwea = fcwea / fcdry)
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Chapter 36/Travel Time Reliability Page 36-53 Applications Street Location Event Type Lane Location Severitya Clearance Time by Weather Condition (min) Dry Rain- fall Wet Pavement Snow or Iceb Segment Crash One lane 2+ lanes Shoulder FI PDO FI PDO FI PDO 56.4 39.5 56.4 39.5 56.4 39.5 42.1 28.6 42.1 28.6 42.1 28.6 43.5 29.7 43.5 29.7 43.5 29.7 76.7 53.7 76.7 53.7 76.7 53.7 Noncrash One lane 2+ lanes Shoulder Breakdown Other Breakdown Other Breakdown Other 10.8 6.7 10.8 6.7 10.8 6.7 5.6 2.4 5.6 2.4 5.6 2.4 5.7 2.8 5.7 2.8 5.7 2.8 14.7 9.1 14.7 9.1 14.7 9.1 Signalized Intersection Crash One lane 2+ lanes Shoulder FI PDO FI PDO FI PDO 56.4 39.5 56.4 39.5 56.4 39.5 42.1 28.6 42.1 28.6 42.1 28.6 43.5 29.7 43.5 29.7 43.5 29.7 76.7 53.7 76.7 53.7 76.7 53.7 Noncrash One lane 2+ lanes Shoulder Breakdown Other Breakdown Other Breakdown Other 10.8 6.7 10.8 6.7 10.8 6.7 5.6 2.4 5.6 2.4 5.6 2.4 5.7 2.8 5.7 2.8 5.7 2.8 14.7 9.1 14.7 9.1 14.7 9.1 Source: Kittelson & Associates, et al.
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Applications Page 36-54 Chapter 36/Travel Time Reliability Street Location Event Type Proportion Lane Location Proportion Severitya Proportion Joint Proportion Segment Crash 0.358 1 lane 2+ lanes 0.837 0.163 FI PDO FI PDO 0.304 0.696 0.478 0.522 0.091 0.209 0.028 0.030 Noncrash 0.642 1 lane 2+ lanes 0.881 0.119 Breakdown Other Breakdown Other 0.836 0.164 0.773 0.227 0.473 0.093 0.059 0.017 Total: 1.000 Signalized Intersection Crash 0.310 1 lane 2+ lanes 0.856 0.144 FI PDO FI PDO 0.378 0.622 0.412 0.588 0.100 0.165 0.018 0.026 Noncrash 0.690 1 lane 2+ lanes 0.859 0.141 Breakdown Other Breakdown Other 0.849 0.151 0.865 0.135 0.503 0.089 0.084 0.013 Total: 1.000 Source: Kittelson & Associates, et al.
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Chapter 36/Travel Time Reliability Page 36-55 Applications Reliability also adds another dimension of information on facility performance that can aid travel demand models to better predict the conditions under which people will choose to pay a toll for more reliable service. Reliability will enable better destination, time of day, mode, and route choice models.
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Applications Page 36-56 Chapter 36/Travel Time Reliability improvements that are likely to best address the identified primary causes of reliability problems on the facility. This case requires that the analyst: 1.
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Chapter 36/Travel Time Reliability Page 36-57 Applications Use Case #6 shares much with Case #5, but it introduces a new concept, acceptability or failure. The numerical results produced in Use Case #5 are compared to some standard -- a national, state, or agency-specific standard of acceptable performance.
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Applications Page 36-58 Chapter 36/Travel Time Reliability Thousands of scenarios may need to be analyzed using the alternative tool in addition to the number of replications per scenario required by the tool itself to establish average conditions. Extracting and summarizing the results from numerous applications of the alternative tool may be a significant task.
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Chapter 36/Travel Time Reliability Page 36-59 Example Problems 6. EXAMPLE PROBLEMS Problem Number Description Application 1 2 3 4 5 6 7 Freeway facility reliability under existing conditions Freeway facility reliability with a geometric treatment Freeway facility reliability with incident management Freeway facility reliability with a safety treatment Freeway facility reliability with demand management Urban street reliability under existing conditions Urban street reliability strategy evaluation Operational analysis Planning analysis Planning analysis Planning analysis Planning analysis Operational analysis Planning analysis The example problems in this section demonstrate the application of the freeway facility (Example Problems 1β5)
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Example Problems Page 36-60 Chapter 36/Travel Time Reliability Input Data This example illustrates the use of defaults and lookup tables to substitute for desirable, but difficult to obtain, data. Minimum facility inputs for the example problem include the following.
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Chapter 36/Travel Time Reliability Page 36-61 Example Problems Computational Steps Base Dataset Analysis The Chapter 10 freeway facility methodology is applied to the base dataset to make sure that the specified facility boundaries and study period are sufficient to cover any bottlenecks and queues. In addition, because incident data are being supplied in the form of a facility crash rate, the VMT associated with the base dataset is calculated so that incident probabilities can be calculated in a subsequent step.
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Example Problems Page 36-62 Chapter 36/Travel Time Reliability divided by the demand multiplier of 1.00, resulting in a 7% increase in the base dataset volumes across all analysis periods for that scenario. The probability of any given demand pattern is the ratio of the number of days (or hours)
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Chapter 36/Travel Time Reliability Page 36-63 Example Problems Incorporating Incident Variability For an existing freeway facility such as this one, it is desirable to have detailed incident logs that can be used to develop monthly or seasonal probabilities of various incident severities. However, in this case, incident logs of sufficient detail are not available.
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Example Problems Page 36-64 Chapter 36/Travel Time Reliability for shoulder-closure incidents, and a 180-min study period duration, the probability of a shoulder-closure incident for this demand pattern is: ππ π,ππππ,π/πΉ = 1 β πβ(0.803 Γ0.75)
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Chapter 36/Travel Time Reliability Page 36-65 Example Problems As a check, these probabilities add up to 10%, after accounting for rounding errors. The "Study Period and Detailed Scenario Generation" procedure given in Chapter 37 is applied to create the final set of the scenarios.
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Example Problems Page 36-66 Chapter 36/Travel Time Reliability The method allows the analyst to discard very-low-probability scenarios by applying an inclusion threshold. This approach entails a risk of missing some of the very severe scenarios (e.g., multiple lane closures in a snow storm)
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Chapter 36/Travel Time Reliability Page 36-67 Example Problems freeway facilities method, which generates an average travel time for each analysis period within the scenario's defined study period, along with the other performance measures that the Chapter 10 method produces. After all of the scenarios have been analyzed, a VMT-weighted probability value is applied to each scenario travel time.
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Example Problems Page 36-68 Chapter 36/Travel Time Reliability that 85% of the facility's VMT during the p.m. peak period operates at a speed of 45 mi/h or higher is an important benchmark.
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Chapter 36/Travel Time Reliability Page 36-69 Example Problems Reliability Performance Measure Value for Base Scenario Value from all Scenarios Percent Difference Mean facility TTI (corresponding speed, mi/h)
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Example Problems Page 36-70 Chapter 36/Travel Time Reliability Incident Type Month Shoulder Closed 1 Lane Closed 2 Lanes Closed 25th percentile 14 16 28 50th percentile 26 27 39 75th percentile 38 38 50 Results and Discussion The key congestion and reliability statistics for this example problem are summarized in Exhibit 36-51. The total number of possible scenarios decreases from 1,928 in Example Problem 1 to 1,664 here, while using a scenario inclusion threshold of 0.01% decreases the number of scenarios from 602 to 442.
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Chapter 36/Travel Time Reliability Page 36-71 Example Problems Data Inputs All the input data used in Example Problem 1 remain unchanged, except for the assumed incident probabilities given in Exhibit 36-43. These incident probabilities are modified as shown in Exhibit 36-52, based on the analyst's review of a peer agency's results following the implementation of a similar package of treatments.
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Example Problems Page 36-72 Chapter 36/Travel Time Reliability EXAMPLE PROBLEM 5: DEMAND MANAGEMENT STRATEGY In this example problem, demand management techniques are used to shift peak-hour demand to the shoulder periods. By reducing peak-period demand, a capacity buffer is provided that can possibly absorb some of the capacityreducing effects of severe weather and incidents.
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Chapter 36/Travel Time Reliability Page 36-73 Example Problems Results and Discussion Exhibit 36-58 summarizes the key congestion and reliability statistics for Example Problem 5. The total number of possible scenarios remains the same as in Example Problem 1 (1,928 with no scenario exclusion and 602 using a 0.01% scenario inclusion threshold)
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Example Problems Page 36-74 Chapter 36/Travel Time Reliability Several observations emerge from this comparison: β’ The lane-add treatment had the strongest effect on performance. The added lane essentially serves as a buffer that helps absorb the shock of capacity-reducing incident or weather events.
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Chapter 36/Travel Time Reliability Page 36-75 Example Problems alter their route, departure time, or mode, or may cancel their trip altogether. While the methodology accommodates user-defined changes in demand associated with weather or incidents that capability was not used in these example problems.
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Example Problems Page 36-76 Chapter 36/Travel Time Reliability Required Input Data This section describes the input data needed for both the reliability methodology and the core HCM urban streets methodology. The dataset that describes conditions where no work zones or special events are present is known as the base dataset.
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Chapter 36/Travel Time Reliability Page 36-77 Example Problems Traffic count data for the hour beginning at 7:00 a.m. are available from a recent traffic count taken on a Tuesday, January 4.
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Example Problems Page 36-78 Chapter 36/Travel Time Reliability Because events (e.g., a storm, a crash) are generated randomly in the urban street method, the possibility exists that highly unlikely events could be overrepresented or underrepresented in a given set of scenarios.
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Chapter 36/Travel Time Reliability Page 36-79 Example Problems Define the Reliability Analysis Box The results from a preliminary evaluation of the facility were used to define the general spatial and temporal boundaries of congestion on the facility under fair weather, non-incident conditions. A study period consisting of the weekday morning peak period (7 a.m.
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Example Problems Page 36-80 Chapter 36/Travel Time Reliability β’ Precipitation rate. One inch of snowfall is estimated to have the water content of 0.1 in.
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Chapter 36/Travel Time Reliability Page 36-81 Example Problems Date P re ci pi ta ti on R N R D P re ci pi ta ti on ?
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Example Problems Page 36-82 Chapter 36/Travel Time Reliability The default values for these factors are obtained from Exhibit 36-27 to Exhibit 36-30. Their selection is based on the functional class of the subject facility, which is "urban principal arterial." Determine Base Demand Ratio First, the demand ratios for the day of the traffic count are determined.
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Chapter 36/Travel Time Reliability Page 36-83 Example Problems intersection to estimate the equivalent hourly flow rate for the associated analysis period. Date Weekday Time Weather Weather Factor Hour Factor Day Factor Month Factor Total Factor Total/Base Jan 10 Mon 7:00 Snow 0.80 0.071 0.980 0.831 0.0463 0.800 Jan 10 Mon 7:15 Snow 0.80 0.071 0.980 0.831 0.0463 0.800 Jan 10 Mon 7:30 Snow 0.80 0.071 0.980 0.831 0.0463 0.800 Jan 10 Mon 7:45 Snow 0.80 0.071 0.980 0.831 0.0463 0.800 Jan 10 Mon 8:00 Snow 0.80 0.058 0.980 0.831 0.0378 0.654 Jan 10 Mon 8:15 Snow 0.80 0.058 0.980 0.831 0.0378 0.654 Jan 10 Mon 8:30 Dry 1.00 0.058 0.980 0.831 0.0472 0.817 Jan 10 Mon 8:45 Dry 1.00 0.058 0.980 0.831 0.0472 0.817 Jan 10 Mon 9:00 Dry 1.00 0.047 0.980 0.831 0.0383 0.662 Jan 10 Mon 9:15 Dry 1.00 0.047 0.980 0.831 0.0383 0.662 Jan 10 Mon 9:30 Dry 1.00 0.047 0.980 0.831 0.0383 0.662 Jan 10 Mon 9:45 Dry 1.00 0.047 0.980 0.831 0.0383 0.662 Apr 6 Wed 7:00 Dry 1.00 0.071 1.000 0.987 0.0701 1.212 Apr 6 Wed 7:15 Dry 1.00 0.071 1.000 0.987 0.0701 1.212 Apr 6 Wed 7:30 Dry 1.00 0.071 1.000 0.987 0.0701 1.212 Apr 6 Wed 7:45 Dry 1.00 0.071 1.000 0.987 0.0701 1.212 Apr 6 Wed 8:00 Dry 1.00 0.058 1.000 0.987 0.0572 0.990 Apr 6 Wed 8:15 Dry 1.00 0.058 1.000 0.987 0.0572 0.990 Apr 6 Wed 8:30 Dry 1.00 0.058 1.000 0.987 0.0572 0.990 Apr 6 Wed 8:45 Dry 1.00 0.058 1.000 0.987 0.0572 0.990 Apr 6 Wed 9:00 Dry 1.00 0.047 1.000 0.987 0.0464 0.802 Apr 6 Wed 9:15 Dry 1.00 0.047 1.000 0.987 0.0464 0.802 Apr 6 Wed 9:30 Dry 1.00 0.047 1.000 0.987 0.0464 0.802 Apr 6 Wed 9:45 Dry 1.00 0.047 1.000 0.987 0.0464 0.802 Step 5: Estimate Incident Events The procedure described in this step is used to predict incident event dates, times, and durations.
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From page 96... ...
Example Problems Page 36-84 Chapter 36/Travel Time Reliability Location Crash Frequency (cr/yr) Segment 1-2 (intersections 1 to 2)
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From page 97... ...
Chapter 36/Travel Time Reliability Page 36-85 Example Problems Segments Intersections Variable Definition 1-2 2-3 1 2 3 Fcstr(i) Observed average crash frequency 15 16 65 66 67 Ny Number of years 2 2 2 2 2 Nhdry Hours of dry weather 17026.98 17026.98 17026.98 17026.98 17026.98 Nhrf Hours of rainfall 278.22 278.22 278.22 278.22 278.22 Nhwp Hours of wet pavement 104.33 104.33 104.33 104.33 104.33 Nhsf Hours of snowfall 64.61 64.61 64.61 64.61 64.61 Nhsp Hours of snow/ice on pavement 45.86 45.86 45.86 45.86 45.86 Crash adjustment factors for⦠CAFrf Rainfall 2.0 2.0 2.0 2.0 2.0 CAFwp Wet pavement 3.0 3.0 3.0 3.0 3.0 CAFsf Snowfall 1.5 1.5 1.5 1.5 1.5 CAFsp Snow/ice on pavement 2.75 2.75 2.75 2.75 2.75 Calculated crash frequencies for⦠Fcstr(i)
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From page 98... ...
Example Problems Page 36-86 Chapter 36/Travel Time Reliability yrincidents/ 5.40 )
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From page 99... ...
Chapter 36/Travel Time Reliability Page 36-87 Example Problems Exhibit 36-66 demonstrates the determination of incidents for Segment 1-2 on April 6 for the 9:00 a.m. hour.
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From page 100... ...
Example Problems Page 36-88 Chapter 36/Travel Time Reliability incident duration and the standard deviation of incident duration as inputs. These values are supplied as input data.
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From page 101... ...
Chapter 36/Travel Time Reliability Page 36-89 Example Problems Identify Analysis Period Incidents The preceding steps of the incident estimation procedure are repeated for each hour of each day in the reliability reporting period. During this step, the analysis periods associated with an incident are identified.
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From page 102... ...
Example Problems Page 36-90 Chapter 36/Travel Time Reliability crucial, because it will be reproduced thousands of times by the scenario generator. The total delay for each scenario should be scanned to identify the study periods likely to be associated with exceptionally long queues.
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From page 103... ...
Chapter 36/Travel Time Reliability Page 36-91 Example Problems movement)
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From page 104... ...
Example Problems Page 36-92 Chapter 36/Travel Time Reliability Replication Average Travel Time (s) 95th Percentile Travel Time (s)
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From page 105... ...
Chapter 36/Travel Time Reliability Page 36-93 Example Problems Site The same urban street described in Example Problem 6 is used in this example problem. Required Input Data The same types of required input data described in Example Problem 6 are used here.
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From page 106... ...
Example Problems Page 36-94 Chapter 36/Travel Time Reliability Define the Reliability Analysis Box The results from a preliminary evaluation of the facility were used to define the general spatial and temporal boundaries of congestion on the facility under fair weather, non-incident conditions. A study period consisting of the weekday morning peak period (7 a.m.
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From page 107... ...
Chapter 36/Travel Time Reliability Page 36-95 Example Problems Step 3: Generate Scenarios During this step, the reliability methodology is used to create one scenario for each analysis period in the reliability reporting period. The base datasets coded in Step 2 represent the "seed" files from which the scenarios are created associated with each strategy.
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From page 108... ...
Example Problems Page 36-96 Chapter 36/Travel Time Reliability offsets the decrease in delay to the major-street through movements. This tradeoff is reflected by a small reduction of 4.5 veh-h total delay.
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From page 109... ...
Chapter 36/Travel Time Reliability Page 36-97 Example Problems the bays to the through lanes. This shift causes the average travel time for Strategy 3 to vary more widely among scenarios.
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From page 110... ...
References Page 36-98 Chapter 36/Travel Time Reliability 7.
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From page 111... ...
Chapter 36/Travel Time Reliability Page 36-99 References Washington, D.C., 2010, pp.
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