Analytical Travel Forecasting Approaches for Project-Level Planning and Design (2014) / Chapter Skim
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P A R T 2 Guidelines
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59 The Project-Level Forecasting Process Project-level traffic forecasting can be complex, given the amount of data and analysis required, but the process follows established and accepted methodologies. Nonetheless, many issues can arise along the way, including communication, the role of engineering judgment, and forecasting accuracy.
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60 4.1.2 Scope Before the forecast is given to the traffic forecasting analyst, the scope is set by the forecast requestor. See Figure 4-1 for an example traffic forecast request form.
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61 In general, traffic impact studies, site development analyses, interchange justification studies, and capital improvement programming (prioritization) require near-term forecasts, on the order of a 2-to-10-year horizon, while design and planning studies may require time horizons up to 30 years or more.
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62 For new developments such as shopping centers, office parks, and neighborhoods, a study area can be defined in terms of travel time or distance. For corridor projects, a study area can be defined by a buffer around the corridor segment and key adjacent intersections.
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63 natives and/or scenarios. For purposes of these guidelines, alternatives will involve geometric or operational changes to be forecasted while scenarios will be considered to be nongeometric, non-operational, project differences such as major changes in land use.
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64 Source: NCHRP Report 255, Figures A-50 and A-51 (1)
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65 areas can often be completed quickly and at a relatively low cost. For example, the Ohio DOT likes to see certified small project traffic forecasts completed within 2 months.
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66 • Communication -- good communication is critical to this process and permeates every step. 4.2.1 Forecast Preparation Forecast preparation consists of collecting needed data to support the forecasting.
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67 4.2.2.1 Forecasting Development and Mini Case Study 1: Turning Movements Background Traffic forecasts involving intersections generally require the calculation of peak-hour turning movements for the current (or opening year) and future design year.
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68 An example of using the NCHRP Report 255 (1) trend line analysis spreadsheet is included in Appendix G-1.
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69 4.2.3.1 Data Analysis The data analysis is mostly performed with spreadsheets and with travel demand models. The traffic forecaster serves as a quality control and quality assurance (QC/QA)
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70 cast, assumptions that were made, explanation of the tools and methods that were used, and the results. Common elements contained in a traffic forecast report include the following: • Table of contents, • Request for forecast, • Project description/purpose of forecast, • Data types and sources, • Forecasting parameters, • Discussion of tools and methods, • Results, • Supporting data/information, and • Glossary.
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71 intersection turning movement counts, and vehicle classification counts) , population and employment summaries and projections, descriptions of roadway characteristics, land use and development plans, and planned/programmed projects that will influence future travel patterns and demand.
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72 The results section should provide maps, tables, graphs, and diagrams to accompany the text. The writing should be clear and concise, yet contain adequate detail to support forecasts as they are presented.
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73 very effective, but the temptation to include too much detail should be avoided. Street names and turning volumes should be clearly labeled.
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74 • The appropriate use of models when there may be a lack of sensitivity (or incorrect sensitivity) to a critical issue in the decision; • The requirement that the analyst (planner, modeler, or engineer)
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75 sensitivity to a critical decision variable. An example of lack of sensitivity might be a travel model that omits carpools as a possible mode.
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76 forecasting methodology. For example, a decision-maker might want to know the fraction of lane miles worse than level of service (LOS)
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77 forecasts tend to focus on the quality of traffic volumes and speeds. This focus can be justified because (1)
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78 Pe rc en t D ev ia tio n Base Year Count Maximum Desirable Deviation Approximate Error in a Count 60% 50% 40% 30% 20% 10% 0% 10,0000 20,000 30,000 40,000 60,00050,000 70,000 80,000 90,000 100,000 Figure 4-14. Maximum desirable error for link volumes.
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79 4.4.2.5 Limits for Model Error -- Volume Accuracy Standards and Best Practical Experience for Regional Models Regional models may or may not produce traffic forecasts with sufficient accuracy for project purposes. MPOs often publish statistics as to how well they fit ground data.
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80 dictate otherwise, it is reasonable to ignore road segments with low counts during validation. Standards on full-day, bidirectional volumes may also be applied to directional volumes over a full day.
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81 sensitivity between traffic volumes and fuel price might be worth knowing so that confidence limits can be reasonably established for the forecast. 4.4.2.9 Time-Series Model Accuracy Statistical software packages used for estimation time-series models provide goodness of fit statistics that may be used to judge the accuracy of the model, at least to the extent that the model correctly represents historical patterns.
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82 implying the accuracy of a travel forecast in planning documents is 50%, corresponding to the "probable error." A traffic forecast could be given as XXXX ± YYYY, where XXXX is the forecast and YYYY is the probable error, taken to be RMSE multiplied by 0.6745. However, a 95% confidence limit might be appropriate for decision-making when the cost of a wrong decision is high.
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83 4. Link-level adjustments for over/under estimation against base year traffic counts should not exceed 15%.
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84 C H A P T E R 5 5.1 Understanding the Model 5.1.1 Model Component Checklist -- Getting Started In areas where models are available for use in project-level traffic forecasts, the model components should include a checklist of items or steps to be performed or reviewed prior to beginning an analysis. This checklist consists of components from basic, intermediate, and advanced models.
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85 • Distance, • Capacity or saturation flow rate, • Intersection control (stop sign, roundabout, signal, etc.) , • Intersection approach geometry (left, through, right, shared lanes)
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86 The example gives a rough outline of the steps necessary to determine a validation standard for a project that cannot use the half-lane rule: 1. Determine the traffic variable that most influences the decision.
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87 optimistic. At Granato's site, average deviations from the true AADT ranged from 2.1% to 10.9%, depending upon the year, for 48-hour counts with borrowed factors and a true AADT equal to approximately 10,000 vehicles per day (VPD)
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88 tracting exit volumes, until the next known count location is reached. For this straightforward application, the balancing is constrained by the ATR stations and a pro rata distribution of the difference is applied to the ramp volumes.
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89 The GEH model was created for hourly traffic volumes. For daily traffic volumes, a simplistic approximation can be applied based on an assumption that peak-hour traffic is about 10% of the daily traffic flow: 0.2 0.4 + 0.22 2 G = M MC MC M C D − + where GD = daily traffic volumes, M = traffic model volume, ADT, and C = traffic count (ADT)
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90 intersection counts, for the following reasons inherent to collecting traffic count data: 1. Traffic counts taken at different times and/or on different days and 2.
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91 While the row total for the subject approach is now equal to the departure volumes from the adjacent key intersection, the departure volumes likely will not be equal. A second iteration is performed in which the turning movements that contribute to the departure volumes for the subject link (column)
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92 East North West South Totals East 50 1,410 30 1,490 Initial Turninng Movement Matrix North 660 320 10 990 West 1,590 30 20 1,640 South 110 10 80 200 Totals 2,360 90 1,810 60 East North West South Totals East 0 44 1,230 26 1,300 East leg approach (row) cells multiplied by 1300/1490 North 660 0 320 10 990 West 1,590 30 0 20 1,640 South 110 10 80 0 200 Totals 2,360 84 1,630 56 East North West South Totals East 0 44 1,230 26 1,300 North 543 0 320 10 873 West 1,307 30 0 20 1,357 South 90 10 80 0 180 Totals 1,940 84 1,630 56 East leg departure (column)
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93 East North West South Totals East 50 1,410 30 1,490 Initial Turninng Movement Matrix North 660 320 10 990 West 1,590 30 20 1,640 South 110 10 80 200 Totals 2,360 90 1,810 60 East North West South Totals East 0 44 1,230 26 1,300 East leg approach (row) cells multiplied by 1300/1490 North 660 0 320 10 990 West 1,590 30 0 20 1,640 South 110 10 80 0 200 Totals 2,360 84 1,630 56 East North West South Totals East 0 44 1,230 28 1,302 North 543 0 320 11 874 West 1,307 30 0 21 1,358 South 90 10 80 0 180 Totals 1,940 84 1,630 60 South leg departure (column)
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94 This would include collecting updated traffic counts, including counts at the mid-block intersections. 5.4.3 Spatial Interpolation of Traffic Counts In some cases, it may be necessary to develop a forecast for a roadway segment for which there are no existing traffic counts or for which the traffic counts are too old to be considered useful, yet it is desirable to compare a base year model estimate to a "synthesized" count.
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95 The service volume tables can be created manually using software that implements the HCM methodology. Using the selected representative inputs, the input volumes are varied, and resulting LOS thresholds are identified.
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96 define the end of the links. Link-based capacity is computed using Equation 18-15 from the HCM2010: c = Ns g C where c = capacity (vehicle/hour)
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97 the data are required inputs for methods used to compute capacity, free flow speed, or other parameters needed for developing traffic forecasts and whether there are suitable representative or default values that can be used in place of actual data. Given typical limitations in budget and/or schedule, the analyst should develop, once identified, a prioritization of the missing data elements so that resources can be focused on filling in the missing data or developing suitable defaults.
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98 zonal trip interchanges that pass through the selected link(s) / zone(s)
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99 Additional graphical and visualization tools can be referenced in the document Showcasing Visualization Tools in Congestion Management by FHWA (71)
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100 5.5.2.4 Desire Lines Desire lines illustrate the flow of trips from one or more origins to one or more destinations based on lines connecting the origin(s) and destination(s)
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101 5.5.3 Model Post-Processing Post-processing of model outputs is a common and accepted practice in project-level traffic forecasts. Frequently, spreadsheet-based tools are used to refine or smooth model output data that results in balanced trips throughout the study area that match observed behavior.
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102 adjust the model data for these assumptions. One such example is the conversion of traffic volumes from average weekday volumes to average daily or vice versa.
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103 3,600 0.095 37,900 vehicles day= This daily capacity actually is the extrapolation of capacity (on an hourly basis) to a 24-hour equivalent, based on measured or assumed peaking characteristics.
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104 period(s)
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105 application and looking toward parallel processing to speed up computations. Computers that contain processing clusters of 1,024 or more are available, but the computer configuration that has the greatest immediate potential for travel demand models, at this writing, is the multicore desktop.
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106 Outputs from most travel models need to be checked and further refined to be used for highway project planning and design. This process of model checking, refining, and adjustment is an important part of traffic forecasting procedures.
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107 Factoring procedures are the simplest, but may be limited in their applicability. They require base year turning movement counts, base year turning movement assignments, and future year turning movement assignments.
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108 Geography is site, corridor, and small area. Typical time horizons are short range, interim, and long range.
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109 capacity-related adjustments that need to be made due to the project. 6.1.6.2 Technique Configuration Typically, screenline refinement is performed in a spreadsheet after gathering all the inputs mentioned above.
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110 Column Variable Defini on 1 Road/Link The name/route number of each facility bisected by the screenline and/or the link numbers from network. 2 Min Diff Minimum Count/Model Ra o for using differences, below this use ra os alone.
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111 6. Most recent count data (optional)
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113 6.2 Factoring Procedure -- Ratio Method 6.2.1 Abstract Factoring procedures are used to predict future year turning movements based on the relationship between base year turning movement counts and base year model turning movement assignments. The assumption is that future turning movements will be similar in nature to existing turning movements.
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114 the turning movement counts, assignments, and forecasts all should be for the same general time period (PM peak, for example)
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115 6.3.5.2 Choosing between the Ratio and Difference Methods While both ratio and difference methods are categorized as factoring procedures, these methods can produce dramatically different results, especially where model errors might exist. Furthermore, most models are not capable of generating accurate turning movements unless they account for turning movement delay.
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116 6.4.3 Background The iterative procedure -- directional method was previously documented in NCHRP Report 255. The method has been automated through spreadsheets and other computational software and has been applied by numerous transportation agencies and consultants.
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117 Figure 6-2. Directional turning volume iterative procedure.
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118 The computational steps (Steps 1 through 5) are described below.
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119 Future Year 300 500 600 600 Djf Base Year 250 390 470 390 Djb 500 400 0 80 120 200 450 300 60 0 100 140 250 200 110 40 0 50 800 600 80 270 250 0 O O Destination (D) Outflows O ri gi n (O )
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120 The matrix cells are populated by multiplying the future year link inflows (Oif) by the corresponding turning movement percentage (Pijf)
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121 Step 1 (Construct the initial turning matrix) as illustrated in Figure 6-6.
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122 6.5 Iterative Procedure -- Non-Directional Method 6.5.1 Abstract The iterative non-directional method is intended for general planning purposes where approximate non-directional intersection turning movements are desired. The method relies on an initial estimate by the analyst of total turning percentages at the intersection.
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123 Where the intersecting streets have significantly different volumes (for example, a principal arterial intersecting with a residential collector) , the volume difference for opposing legs will typically need to be increased.
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124 . 2 Approach Volume Total Int Volume = then, .
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125 (i.e., through movements) and, if the number of iterations is sufficient, ultimately will yield the average of the two volumes for opposing approaches.
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126 In Step 3 (Perform the initial allocation of turns) , the turning volume on each is allocated to the other legs based on the proportion of turning movements of those other legs.
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127 differences between initial input link volumes (from Step 1) and the computed link volumes (following Step 4)
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128 The final adjusted turning volumes using both the difference and ratio methods are shown in Figure 6-18. For this example, it can be concluded that the difference method produces slightly better results, as the percent difference between the adjusted intersection leg total volumes and the initial input volumes is smaller.
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129 Geography is site, corridor, and wide area. Typical time horizons are short range, interim, and long range.
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130 6.7.2 Context Typical applications are intersection design, intersection capacity analysis, site impact studies; traffic signal timing, and interchange studies. Geography is site, corridor, and wide area.
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131 are not available or when future land use changes (suburbanization of a rural area, for example) are anticipated to alter travel patterns, travel models can be used to refine the estimation of D
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132 where DF = future year traffic directional distribution, DB = base year traffic directional distribution, WTF = future year work trip directional distribution, and WTB = base year work trip directional distribution. Consideration should be given to the proportion of work travel to total travel in the future and whether or not it may constitute the same proportion as in the base year.
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133 When compared to the mainline count at the downstream end of the section, the running total is 3,100 vehicles per day higher. Each ramp volume is adjusted in proportion to the sum of the ramp volumes.
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134 where GD = daily traffic volumes, M = traffic model volume (ADT) , and C = traffic count (ADT)
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135 over this extended period. The distribution arises from a number of factors that influence travel times, including congestion, severe weather, incidents, work zones, and special events.
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136 6.10.4 Why This Technique Impedance in the form of travel time, alone, will likely underestimate the negative aspects of the trip because it does not incorporate information about the variability of travel time. Drivers will be attracted to routes that are both faster and more reliable.
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137 Results of a traffic assignment may be interpreted in the same way as if reliability had not been included. 6.10.7 Illustrative Example Consider the network in Figure 6-25 with eight links that can carry traffic between points A and B along a freeway.
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138 Optional input data are historic or modeled OD table. Related techniques are screenline refinements with traffic counts, screenline refinements with additional network details, refinement with OD table estimation, windowing to forecast traffic for small areas, and turning movement refinement.
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139 estimated OD table should not differ much from the average trip length of the seed OD table. An estimated OD table can be obtained that corrects for issues in the traffic assignment step, but is not appreciably better than the seed OD table.
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140 STEP 4 . Run Estimation and Inspect Results for Reasonableness Trial estimations with loose convergence criteria are recommended.
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141 anced everywhere. The estimated OD table RMS deviation from the seed table (excluding U-turns)
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142 The raw travel forecast, by definition, does not contain the project. The travel model output needs to be appropriate for the time period of analysis for the project, for example, a single PM peak hour.
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143 STEP 6 (OPT iOnal)
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144 desirable, particularly near the southwest corner of the network where the project is located. The software available to the planner uses a method called weighted least squares.
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145 C H A P T E R 7 The process of refining the spatial detail of a highway traffic model involves a variety of techniques that (1) increase the spatial resolution of the model itself, (2)
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146 modeled trip purpose; peak-hour speeds; time-of-day (TOD) factors; and directional split factors.
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147 generator or a lane widening over a short road segment. It is important to make the subarea large enough to achieve a good approximation to changes in trip making attributable to the project.
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148 STEP 4 . Setup and Run Travel Model on Focused Network Different travel forecasting software packages require different setups, but the general concept is the same.
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149 quadrant within the larger QTown metropolitan area. The office park is expecting 315,000 sq ft of floor area and 2,000 employees.
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150 peak and 69 trips are leaving the site. All 860 trips are distributed from the site to off-site zones using a singly constrained gravity model.
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151 Disadvantages of windowing to forecast traffic for small areas are that it is highly empirical, requires specialized OD table estimation software, and requires a seed OD table that is unlikely to be based on behavioral principles. Case study is #2 - Milwaukee/Mitchell Network Window.
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152 STEP 2 . Obtain Traffic Counts (and Turning Movement Counts)
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153 • Assign the seed OD table to the window's network and check the percentage of turning movements throughout. Adjust turn impedances, if necessary, to achieve a reasonable number of turns.
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154 nearby count stations. The direction of the highest flow rate is important.
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155 pute similar probabilities, the unweighted impedance for each turn must be about 7 (see earlier discussion) on average, but left turns should be 4 more than right turns.
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156 Geography is corridor, intersection, and site (subarea)
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157 Figure 7-9. Issues associated with merging a static macroscopic model with a microsimulation model.
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158 may not be entirely accurate to say that multiresolution models should only go from mesoscopic to microscopic resolution, the fact that there is a big discrepancy in flows between alternative types of conversions is something that should not be overlooked. It can be seen from the image above that integration between macroscopic and microscopic would show overloading during off-peak periods and underloading during the peak period.
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159 are likely to be impacted by the proposed strategy or policy. For Interstate projects, it is suggested that the model network should extend up to 1.5 miles (or at least one interchange)
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160 In Figure 7-11, the configurations shown on the left-hand side may be engineered to be a inline multiresolution model system. 7.3.6.2 Steps of the Technique STEP 1 .
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161 world conditions should be simulated, and model outputs and measures of performance should be compared against real-world data to ensure that the model is capable of reflecting actual operating conditions on the subnetwork of interest. Although a distinction can be drawn between calibration data and validation data, they may be treated as a group for most practical purposes.
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162 the base year, then its ability to capture traffic dynamics under alternative scenarios is subject to question. Various parameters and input data configuration may need to be adjusted to ensure that the model replicates real-world conditions for the base year.
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163 network, modal, spatial-temporal, and socioeconomic data should be already available as part of the model systems operating at different geographical scales; all of this data should be available to any study that seeks to integrate model platforms. Related techniques are multiresolution modeling, subarea analysis, and windowing.
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164 that there would be spatial and temporal impacts on longdistance, regional, and local passenger and freight trips. The integration of statewide, regional, and local travel models makes it possible to account for these impacts, while benefiting from the spatial and temporal detail offered by a local travel model.
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165 identifying discrepancies and inconsistencies across models, and corrective action should be taken to eliminate all such discrepancies. The model system as a whole should be subject to a round of calibration and validation checks even if each individual model component (in isolation)
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166 • Independent Systems of Data and Networks. At the other end of the model configuration spectrum, it is possible to develop models at different geographic scales without necessarily mapping one to the other.
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167 to a broader geographic scale. Similarly, network conflation procedures should be implemented so that geographic correspondence between links and nodes of networks at different levels is clear.
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168 discrepancies worthy of further investigation. For example, suppose a statewide model suggested that there are 1,000 trip productions in a statewide model zone for which there is regional travel demand model coverage.
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169 7.5.5 Words of Advice All empirically derived E-E OD tables need to be scrutinized for problems prior to being used for forecasting. Methods based exclusively on professional judgment should be used only as a last resort.
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170 identification rate at the destination. Furthermore, it is prudent to assume that identification rates can differ depending upon the location of the detector, regardless of whether the location is considered an origin or a destination.
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171 E-E net counts should be checked for consistency. The total of all E-E traffic entering the region should equal the total of all E-E traffic leaving the region.
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172 in Table 7-2. In addition, tube counters were placed at the external stations for the same hour.
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173 Table 7-4. Sampled E-E trips after being uniformly expanded and subjected to optimal row and column factors.
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174 C H A P T E R 8 Transportation planning has been steadily shifting from an emphasis on capacity expansion to a broad variety of issues, such as travel demand management, social equity and environmental justice, quality of life, energy sustainability, and environmental concerns. Four-step travel demand models are generally inadequate for these complex subjects.
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175 and interactions, intra-household interactions, and interdependencies among activities and trips in a chain or tour. By providing detailed information about activity-travel patterns at highly disaggregate levels of resolution, activity-based travel demand models are able to address emerging policy issues of interest including multimodal investments, dynamic pricing strategies, alternative work arrangements, land use effects, environmental justice, and demographic and technological shifts.
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176 on the subsequent location that the person wishes to visit. Trips in the same chain or tour will generally entail the use of a consistent mode; a person cannot abandon his or her car or acquire a car in the middle of a tour.
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177 to collecting information from an activity-based perspective as opposed to a trip-based perspective (ask the respondent: "What did you do next? " instead of "Where did you go next?
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178 • Vehicle Ownership Models. Vehicle ownership and availability continues to be a key driver of travel demand and mode choice behavior.
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179 of interest at the population level are usually available from the census for different levels of geography such as block, blockgroup, and census tract. In some cases, synthetic populations may be generated at the TAZ level, in which case such population-level marginal distributions must be procured at the TAZ level.
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180 standpoint. The synthetic population should be representative of the overall population in a region.
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181 so that the final set of OD tables sent to the traffic assignment model are comprehensive in their coverage. Alternatively, if chronologically sorted trip lists output by the activity-based travel demand model are being fed to a DTA model, then OD tables of other trips not counted by the activity-based travel demand model should be disaggregated into trip lists using appropriate smooth temporal distributions of travel.
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182 For large metropolitan areas, the output lists generated by an activity-based travel demand model can be large and appropriate computational resources with sufficient memory and datahandling capabilities should be in place. The outputs can be used to map activity-travel flows in a region, study temporal patterns of behavior, map the presence of individuals (population)
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183 under a variety of scenarios. The traditional static traffic assignment procedures constitute an operational implementation of Wardrop's (first)
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184 travel are mitigated. ITS such as signal optimization and adaptive control mechanisms can impact travel times experienced by travelers, and the mechanisms (e.g., signal timing)
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185 implementation of DTA models. DTA models may be configured such that they run in an iterative fashion until dynamic user equilibrium (DUE)
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186 to generate TDSPs for zero-demand OD pairs only when a subsequent iteration calls for the model to do so. DTA models are capable of reflecting the movements of multiple traveler classes through the network.
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187 Finally, a DTA model requires that traffic volume, travel time and cost (generalized cost) , and speed data be compiled.
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188 STEP 6 . Apply the Model to the Project of Interest The final step in the process is the application of the model to the scenario or project of interest.
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189 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Ra o of D ai ly Tr ip s Hour of the Day Passenger Car Passenger Truck Figure 8-3. I-80, Gary, Indiana, hourly count factors, average daily counts 2010.
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190 the AM and PM peaks are more suppressed. Also, the midday valley is not as pronounced.
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191 5. Shift Over-Capacity Traffic onto Shoulder Hours.
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Figure 8-7. Peak-spreading example, identifying first iteration peak hour.
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193 volume in Hour 17, 100 vehicles are moved into Hour 16 and 100 vehicles are moved into Hour 18. The shifted volume is shown in the short dark bars in Fig ure 8-9.
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Figure 8-10. Peak-spreading example, identifying second iteration peak hour.
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Figure 8-12. Peak-spreading example, second iteration traffic shifting.
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Figure 8-14. Peak-spreading example, final traffic distribution after all iterations.
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197 The impact of peak spreading on the diurnal distribution of traffic can be more easily seen in Figure 8-15. This chart compares the diurnal distribution of traffic before peak spreading is applied to the diurnal distribution of traffic and after peak spreading is applied.
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198 Heavy commercial vehicles tend to be more active throughout the midday. Peak-spreading methods that attempt to spread total traffic and use the resulting factors on vehicle class diurnal distributions will result in highly distorted peaking characteristics.
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199 TOD factoring. Also, pre-assignment TOD factoring allows the creation of dynamic OD tables for use in DTA.
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200 include survey data or default tables, as described in this step and previous steps.
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201 time slice between 5:10 PM and 5:20 PM. Three hours are involved in this interpolation, hours beginning at 4 PM, 5 PM, and 6 PM.
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202 Software may perform TOD factoring automatically. For any given hour, the process is to apply the "from" (production to attraction)
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203 D factors developed from historical traffic counts are used to convert the daily link volume to directional design hourly volume (DDHV)
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204 varies from state to state. For example, Kentucky uses the average weekday peak-hour directional counts to develop D factors for each functional class (104)
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205 which the K factor and/or D factor are developed is consistent with the future operation of the project site.
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12:00 AM 12:59 AM 1.07 0.47 1.63 1.12 1.48 1.28 1.19 0.64 1:00 AM 1:59 AM 0.79 0.29 1.11 0.70 0.98 0.89 0.85 0.40 2:00 AM 2:59 AM 0.70 0.23 0.90 0.54 0.73 0.64 0.73 0.31 3:00 AM 3:59 AM 0.76 0.26 0.79 0.42 0.59 0.54 0.74 0.31 4:00 AM 4:59 AM 1.10 0.30 0.89 0.36 0.59 0.37 1.01 0.32 5:00 AM 5:59 AM 2.20 1.16 1.34 0.76 0.87 0.68 1.93 1.06 6:00 AM 6:59 AM 4.16 2.93 2.19 1.43 1.38 1.17 3.57 2.56 7:00 AM 7:59 AM 5.69 6.27 3.13 2.65 1.94 1.88 4.91 5.37 8:00 AM 8:59 AM 5.26 5.75 4.28 4.07 2.86 2.88 4.85 5.24 9:00 AM 9:59 AM 5.04 4.95 5.43 5.51 4.29 4.77 5.00 5.00 10:00 AM 10:59 AM 5.27 5.24 6.46 6.63 5.57 5.93 5.47 5.48 11:00 AM 11:59 AM 5.60 6.01 7.04 7.56 6.62 7.06 5.92 6.31 12:00 PM 12:59 PM 5.89 6.66 7.15 7.85 7.71 8.86 6.28 7.03 1:00 PM 1:59 PM 6.12 6.72 7.05 7.73 7.89 8.70 6.46 7.05 2:00 PM 2:59 PM 6.78 7.63 7.06 7.81 7.95 8.56 6.96 7.75 3:00 PM 3:59 PM 7.60 8.65 7.07 7.66 8.09 8.52 7.60 8.51 4:00 PM 4:59 PM 7.88 9.22 6.94 7.52 8.03 8.17 7.78 8.90 5:00 PM 5:59 PM 7.54 8.45 6.54 6.95 7.67 7.70 7.43 8.19 6:00 PM 6:59 PM 5.63 5.96 5.76 6.23 6.77 6.41 5.79 6.04 7:00 PM 7:59 PM 4.32 4.49 4.74 5.15 5.65 5.56 4.53 4.68 8:00 PM 8:59 PM 3.57 3.38 4.02 4.00 4.57 3.93 3.75 3.52 9:00 PM 9:59 PM 2.99 2.46 3.57 3.18 3.48 2.65 3.13 2.57 10:00 PM 10:59 PM 2.31 1.54 2.83 2.45 2.56 1.77 2.41 1.67 11:00 PM 11:59 PM 1.71 0.98 2.08 1.74 1.72 1.08 1.75 1.08 Interstate Collector Average Day Interstate Collector Interstate Collector Hour Begins Hour Ends Interstate Collector Weekday Arterial 6.72 7.07 8.29 8.30 7.91 5.96 4.49 Saturday Sunday 0.58 1.44 3.21 6.09 5.53 5.12 5.55 6.31 6.74 7.37 7.25 7.22 7.04 3.50 2.63 1.77 1.16 1.27 0.80 0.57 0.46 0.53 0.87 1.61 2.60 3.91 5.50 6.76 7.60 0.59 0.39 0.30 0.33 7.87 7.79 2.74 1.99 Arterial Arterial 1.51 0.94 0.67 0.49 0.46 0.67 1.23 1.86 2.99 5.11 6.11 6.54 6.78 6.29 5.28 4.36 3.59 7.60 2.08 1.25 Arterial 0.77 0.50 0.38 0.36 0.56 1.28 2.79 5.21 5.06 5.16 5.77 6.50 7.03 7.60 6.69 5.76 4.43 3.12 8.44 8.31 8.06 1.28 6.08 4.72 3.71 2.80 1.93 6.97 7.20 8.12 8.08 7.74 Table 8-8. Total traffic diurnal distribution factors by functional class: urban area, small: <200,000 population.
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CBD Other CBD Other CBD Other CBD Other 12:00 AM 12:59 AM 0.95 0.81 0.71 0.61 1.72 1.80 1.38 1.35 1.84 2.38 1.72 1.58 1.14 1.10 0.91 0.80 1:00 AM 1:59 AM 0.65 0.47 0.46 0.36 1.13 1.17 0.87 0.83 1.20 1.54 1.09 0.97 0.77 0.66 0.58 0.48 2:00 AM 2:59 AM 0.57 0.41 0.40 0.31 0.92 1.01 0.70 0.69 0.97 1.51 0.83 0.79 0.66 0.60 0.49 0.41 3:00 AM 3:59 AM 0.61 0.31 0.46 0.34 0.76 0.62 0.59 0.56 0.70 0.76 0.62 0.62 0.64 0.39 0.49 0.39 4:00 AM 4:59 AM 0.96 0.43 0.77 0.60 0.85 0.52 0.65 0.69 0.65 0.54 0.56 0.67 0.91 0.45 0.73 0.62 5:00 AM 5:59 AM 2.10 0.98 1.80 1.32 1.31 0.74 1.03 1.01 0.92 0.64 0.82 0.91 1.87 0.91 1.59 1.23 6:00 AM 6:59 AM 4.67 2.67 4.05 3.63 2.26 1.36 1.86 2.01 1.53 1.09 1.45 1.62 4.03 2.34 3.49 3.22 7:00 AM 7:59 AM 7.17 5.90 6.40 6.70 3.34 2.37 2.91 3.26 2.13 1.76 2.16 2.55 6.16 5.02 5.49 5.83 8:00 AM 8:59 AM 6.16 5.79 5.75 6.60 4.38 3.51 3.99 4.59 3.11 2.73 3.15 3.82 5.61 5.19 5.24 6.05 9:00 AM 9:59 AM 5.13 4.96 5.18 5.60 5.26 4.75 5.16 5.72 4.45 4.18 4.68 5.37 5.07 4.85 5.12 5.59 10:00 AM 10:59 AM 5.10 5.19 5.36 5.49 6.10 5.87 6.21 6.53 5.65 5.49 5.97 6.76 5.28 5.31 5.54 5.76 11:00 AM 11:59 AM 5.37 6.22 5.76 5.92 6.74 6.79 7.02 7.07 6.53 6.43 6.64 7.19 5.67 6.32 6.02 6.20 12:00 PM 12:59 PM 5.59 7.10 6.11 6.33 7.00 7.27 7.33 7.32 7.60 7.94 8.27 8.11 5.98 7.22 6.50 6.64 1:00 PM 1:59 PM 5.78 6.95 6.25 6.40 6.94 7.33 7.34 7.22 7.75 8.25 8.33 8.02 6.14 7.14 6.62 6.68 2:00 PM 2:59 PM 6.32 6.75 6.70 6.74 6.95 7.29 7.26 7.07 7.72 8.04 8.09 7.58 6.55 6.96 6.92 6.88 3:00 PM 3:59 PM 7.22 7.18 7.46 7.44 6.99 7.24 7.24 7.17 7.74 7.80 7.88 7.42 7.25 7.25 7.47 7.41 4:00 PM 4:59 PM 7.86 7.91 8.05 7.82 6.92 7.01 7.16 6.92 7.73 7.62 7.68 7.22 7.74 7.77 7.89 7.65 5:00 PM 5:59 PM 7.97 8.27 8.14 8.18 6.62 6.68 6.90 6.63 7.36 7.14 7.33 6.80 7.74 7.95 7.89 7.84 6:00 PM 6:59 PM 5.69 6.06 6.10 6.05 5.91 6.21 6.27 5.88 6.53 6.43 6.38 6.01 5.80 6.11 6.15 6.02 7:00 PM 7:59 PM 4.10 4.72 4.42 4.33 4.77 5.49 5.20 4.91 5.45 5.50 5.37 5.05 4.33 4.89 4.62 4.47 8:00 PM 8:59 PM 3.34 3.89 3.48 3.42 4.00 4.69 4.23 4.05 4.46 4.46 4.14 4.10 3.55 4.05 3.65 3.57 9:00 PM 9:59 PM 2.88 3.18 2.82 2.71 3.68 4.22 3.71 3.55 3.51 3.48 3.09 3.10 3.04 3.34 2.96 2.85 10:00 PM 10:59 PM 2.19 2.27 2.00 1.89 3.10 3.38 2.90 2.89 2.66 2.55 2.27 2.29 2.35 2.44 2.15 2.06 11:00 PM 11:59 PM 1.61 1.57 1.36 1.24 2.34 2.67 2.07 2.08 1.82 1.74 1.46 1.49 1.72 1.73 1.47 1.37 Collector Arterial Sunday Average Day Interstate Collector Arterial Interstate Collector Saturday Arterial Interstate Hour Begins Hour Ends Interstate Collector Arterial Weekday Table 8-9. Total traffic diurnal distribution factors by functional class: urban area, medium: 200,000–1,000,000 population.
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CBD Other CBD Other CBD Other CBD Other 12:00 AM 12:59 AM 0.96 1.22 0.78 0.59 1.95 2.55 1.70 1.54 2.32 3.27 2.24 1.95 1.22 1.61 1.07 0.84 1:00 AM 1:59 AM 0.61 0.75 0.48 0.38 1.26 1.75 1.10 0.96 1.50 2.32 1.48 1.28 0.78 1.05 0.67 0.53 2:00 AM 2:59 AM 0.51 0.58 0.37 0.30 1.00 1.38 0.83 0.71 1.20 1.81 1.11 0.95 0.64 0.81 0.52 0.41 3:00 AM 3:59 AM 0.53 0.57 0.37 0.33 0.79 1.23 0.62 0.46 0.85 1.61 0.79 0.57 0.59 0.76 0.45 0.37 4:00 AM 4:59 AM 0.85 0.79 0.61 0.59 0.83 1.10 0.59 0.50 0.73 1.29 0.64 0.47 0.83 0.89 0.61 0.56 5:00 AM 5:59 AM 2.13 1.74 1.70 1.16 1.29 1.36 0.92 0.84 0.97 1.35 0.76 0.73 1.90 1.67 1.49 1.07 6:00 AM 6:59 AM 5.11 4.23 4.17 2.72 2.30 2.16 1.72 1.65 1.63 1.92 1.25 1.34 4.40 3.75 3.53 2.44 7:00 AM 7:59 AM 7.27 6.31 6.58 5.92 3.32 3.02 2.74 3.02 2.14 2.40 1.91 2.13 6.25 5.52 5.57 5.16 8:00 AM 8:59 AM 6.61 6.24 6.08 6.05 4.33 3.90 3.95 4.39 2.93 2.94 2.99 3.23 5.95 5.61 5.46 5.58 9:00 AM 9:59 AM 5.27 5.43 5.04 5.82 5.07 4.79 5.05 5.81 4.18 4.09 4.35 4.94 5.14 5.20 4.97 5.74 10:00 AM 10:59 AM 4.86 5.18 4.96 5.78 5.76 5.42 6.03 6.62 5.44 5.28 5.67 6.15 5.03 5.20 5.18 5.93 11:00 AM 11:59 AM 5.01 5.40 5.39 6.55 6.39 5.98 6.80 7.23 6.35 5.64 6.46 6.95 5.32 5.49 5.69 6.68 12:00 PM 12:59 PM 5.20 5.72 5.81 7.08 6.73 6.35 7.20 7.40 7.16 6.33 7.87 8.39 5.59 5.85 6.22 7.26 1:00 PM 1:59 PM 5.39 5.77 5.93 6.95 6.69 6.42 7.18 7.27 7.39 6.81 8.00 8.02 5.76 5.95 6.32 7.11 2:00 PM 2:59 PM 6.02 6.07 6.31 7.20 6.75 6.43 7.11 7.07 7.47 6.91 7.87 7.80 6.26 6.19 6.58 7.25 3:00 PM 3:59 PM 7.05 6.66 7.05 7.97 6.80 6.40 7.05 7.19 7.50 6.64 7.67 7.78 7.07 6.63 7.12 7.86 4:00 PM 4:59 PM 7.78 7.07 7.85 7.94 6.70 6.22 6.89 6.92 7.52 6.42 7.56 7.61 7.63 6.91 7.69 7.79 5:00 PM 5:59 PM 7.98 7.45 8.33 7.60 6.52 6.12 6.68 6.54 7.17 6.34 7.18 7.28 7.72 7.18 7.99 7.44 6:00 PM 6:59 PM 6.11 6.12 6.52 5.66 6.04 5.92 6.16 5.96 6.52 6.28 6.40 6.25 6.14 6.10 6.46 5.77 7:00 PM 7:59 PM 4.27 4.72 4.80 4.20 4.97 5.31 5.24 4.89 5.54 5.56 5.49 5.16 4.49 4.87 4.93 4.38 8:00 PM 8:59 PM 3.37 3.77 3.88 3.29 4.14 4.55 4.45 3.97 4.69 4.76 4.56 4.12 3.60 3.96 4.02 3.45 9:00 PM 9:59 PM 2.97 3.30 3.17 2.66 3.88 4.17 3.99 3.48 3.77 3.98 3.54 3.18 3.16 3.47 3.32 2.81 10:00 PM 10:59 PM 2.41 2.77 2.28 1.97 3.61 3.94 3.37 3.12 2.96 3.44 2.54 2.30 2.61 2.97 2.45 2.14 11:00 PM 11:59 PM 1.73 2.14 1.53 1.28 2.88 3.51 2.61 2.43 2.07 2.60 1.67 1.44 1.91 2.35 1.69 1.43 Interstate Collector Sunday Arterial Average Day Interstate Collector ArterialArterial Interstate Collector SaturdayWeekday Hour Begins Hour Ends Arterial Interstate Collector Table 8-10. Total traffic diurnal distribution factors by functional class: urban area, large: >1,000,000 population.
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From page 207... ...
Interstate Arterial Collector Interstate Arterial Collector Interstate Arterial Collector Interstate Arterial Collector 12:00 AM 12:59 AM 1.43 0.72 0.57 1.83 1.34 1.16 1.59 1.50 1.38 1.50 0.89 0.75 1:00 AM 1:59 AM 1.12 0.49 0.36 1.37 0.84 0.69 1.16 0.95 0.87 1.15 0.59 0.46 2:00 AM 2:59 AM 0.99 0.43 0.31 1.14 0.63 0.50 0.95 0.68 0.62 1.00 0.48 0.37 3:00 AM 3:59 AM 1.02 0.51 0.38 1.04 0.55 0.44 0.80 0.50 0.43 0.99 0.52 0.40 4:00 AM 4:59 AM 1.31 0.93 0.84 1.15 0.71 0.58 0.83 0.52 0.47 1.22 0.85 0.76 5:00 AM 5:59 AM 2.12 2.28 2.19 1.52 1.27 1.22 1.01 0.83 0.85 1.88 1.97 1.90 6:00 AM 6:59 AM 3.58 4.54 4.36 2.25 2.18 2.09 1.45 1.38 1.34 3.11 3.85 3.70 7:00 AM 7:59 AM 4.89 6.63 6.55 3.24 3.22 3.17 2.10 2.01 1.99 4.29 5.64 5.56 8:00 AM 8:59 AM 4.95 5.55 5.58 4.43 4.35 4.41 3.10 3.06 3.22 4.63 5.10 5.14 9:00 AM 9:59 AM 5.23 5.24 5.25 5.63 5.58 5.71 4.39 4.89 5.23 5.17 5.25 5.31 10:00 AM 10:59 AM 5.64 5.41 5.44 6.63 6.58 6.75 5.72 5.92 6.22 5.80 5.63 5.71 11:00 AM 11:59 AM 5.92 5.67 5.71 7.08 7.14 7.29 6.67 6.53 6.65 6.18 5.97 6.04 12:00 PM 12:59 PM 6.02 5.91 6.05 7.01 7.22 7.42 7.24 8.07 8.49 6.32 6.34 6.52 1:00 PM 1:59 PM 6.26 6.13 6.24 6.91 7.13 7.35 7.55 8.07 8.36 6.53 6.49 6.64 2:00 PM 2:59 PM 6.63 6.68 6.78 6.89 7.12 7.26 7.76 8.06 8.05 6.82 6.90 6.99 3:00 PM 3:59 PM 7.04 7.53 7.63 6.83 7.19 7.26 7.90 8.03 7.94 7.13 7.54 7.62 4:00 PM 4:59 PM 7.25 8.02 8.15 6.66 7.10 7.18 7.84 7.97 7.83 7.26 7.89 7.98 5:00 PM 5:59 PM 7.07 7.98 8.16 6.21 6.79 6.84 7.42 7.76 7.72 7.01 7.79 7.93 6:00 PM 6:59 PM 5.68 5.95 6.17 5.44 6.02 6.03 6.56 6.71 6.48 5.77 6.05 6.19 7:00 PM 7:59 PM 4.47 4.21 4.37 4.58 4.91 4.89 5.53 5.54 5.51 4.63 4.45 4.57 8:00 PM 8:59 PM 3.71 3.30 3.41 3.89 4.05 3.97 4.49 4.35 4.23 3.84 3.52 3.58 9:00 PM 9:59 PM 3.13 2.62 2.59 3.35 3.42 3.34 3.49 3.16 2.95 3.21 2.79 2.73 10:00 PM 10:59 PM 2.54 1.94 1.77 2.76 2.71 2.58 2.60 2.17 1.98 2.57 2.06 1.91 11:00 PM 11:59 PM 1.99 1.34 1.14 2.13 1.95 1.88 1.88 1.35 1.20 1.99 1.42 1.25 Weekday Saturday Sunday Average Day Hour Begins Hour Ends Table 8-11. Total traffic diurnal distribution factors by functional class: rural area.
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From page 208... ...
210 urban collector street located in a large urban area is approximately 5.92% of the daily traffic at the site. The hourly factor tables are presented in Tables 8-8 through 8-11.
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From page 209... ...
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Interstate 1.079 1.090 1.032 0.985 0.875 1.041 1.060 1.046 1.012 1.051 Arterial 1.013 1.000 0.984 0.955 0.875 1.071 1.268 0.988 0.965 1.169 Collector 1.009 0.999 0.985 0.967 0.895 1.075 1.250 0.990 0.971 1.162 Interstate 1.058 1.088 1.054 0.983 0.863 1.029 1.080 1.046 1.009 1.055 Arterial 0.993 0.974 0.959 0.940 0.879 1.135 1.384 0.966 0.949 1.259 Collector 0.962 0.956 0.927 0.937 0.897 1.167 1.422 0.946 0.936 1.294 Interstate 0.997 0.974 0.948 0.929 0.881 1.151 1.341 0.962 0.946 1.246 Arterial - CBD 0.997 0.967 0.950 0.939 0.879 1.113 1.432 0.964 0.947 1.273 Arterial - Other 1.000 0.975 0.952 0.943 0.880 1.079 1.376 0.967 0.950 1.228 Collector 0.994 0.959 0.939 0.934 0.912 1.173 1.410 0.971 0.963 1.292 Interstate 1.002 0.970 0.943 0.938 0.893 1.140 1.321 0.964 0.950 1.231 Arterial - CBD 0.985 0.948 0.935 0.931 0.909 1.155 1.456 0.950 0.942 1.305 Arterial - Other 0.997 0.960 0.943 0.938 0.892 1.092 1.368 0.960 0.948 1.234 Collector 0.984 0.951 0.934 0.976 0.874 1.145 1.387 0.965 0.947 1.266 Urban - Small Urban - Medium Urban - Large Rural Table 8-12. Day-of-the-week factors.
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From page 210... ...
January February March April May June July August September October November December Interstate 1.144 1.103 0.985 1.002 1.005 0.946 0.935 0.985 1.054 1.011 1.002 1.056 Arterial 1.127 1.052 0.982 0.981 0.987 0.989 1.008 0.999 1.018 0.998 1.022 1.090 Collector 1.092 1.038 0.964 0.970 0.980 0.992 1.034 1.017 1.030 1.001 1.031 1.113 Interstate 1.125 1.075 0.968 0.987 1.020 0.982 0.969 1.011 1.074 1.028 0.982 1.026 Arterial 1.107 1.027 0.982 0.995 0.988 1.023 1.057 1.001 1.033 1.033 1.077 1.113 Collector 1.161 1.137 0.951 0.999 1.037 0.962 1.012 0.946 0.993 1.023 1.039 1.148 Interstate 1.088 1.051 0.999 1.023 1.022 1.001 1.011 1.015 1.060 1.018 1.017 1.053 Arterial - CBD 1.124 1.010 0.959 0.987 1.044 1.033 1.099 1.014 1.060 0.992 1.075 1.102 Arterial - Other 1.084 1.015 0.981 1.011 1.017 1.009 1.035 1.017 1.048 1.029 1.039 1.051 Collector 1.011 0.931 0.929 0.941 1.010 1.107 1.174 1.164 1.179 1.093 1.051 1.055 Interstate 1.097 1.051 0.982 0.997 1.012 1.012 1.048 1.015 1.032 1.004 1.017 1.068 Arterial - CBD 1.054 0.970 0.980 1.007 1.033 1.065 1.081 1.056 1.058 1.056 1.077 1.060 Arterial - Other 1.078 1.023 0.984 0.994 0.989 0.998 1.043 1.025 1.032 1.013 1.049 1.072 Collector 1.066 1.018 0.966 0.972 0.999 1.051 1.090 1.086 1.107 1.043 1.014 1.001 Urban - Small Urban - Medium Urban - Large Rural Table 8-13. Monthly factors.
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From page 211... ...
213 • Step 3. Estimate the future year vehicle classification.
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From page 212... ...
(a) Rural freeway (b)
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From page 213... ...
215 (c) Urban freeway (d)
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From page 214... ...
Figure 8-18. Non-freeway hourly factor distribution comparison.
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From page 215... ...
217 (a) Rural freeway (b)
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From page 216... ...
218 (c) Urban freeway (d)
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From page 217... ...
Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.359 1.509 1.446 1.295 1.013 1.103 1.140 1.399 1.238 1.122 1.266 February 1.267 1.434 1.394 1.223 0.979 1.102 1.116 1.329 1.258 1.112 1.217 March 1.166 1.249 1.188 1.114 0.903 0.934 0.903 1.183 1.126 0.921 1.065 April 1.186 1.290 1.217 1.111 0.892 1.013 0.964 1.201 1.140 0.987 1.096 May 1.047 1.260 1.230 1.077 0.851 0.941 0.933 1.153 1.093 0.939 1.049 June 1.050 1.145 1.089 0.990 0.812 0.846 0.823 1.067 1.016 0.837 0.965 July 0.966 1.076 1.050 0.913 0.748 0.813 0.788 0.985 0.935 0.803 0.908 August 1.099 1.208 1.141 1.038 0.839 0.900 0.881 1.123 1.066 0.892 1.015 September 1.086 1.310 1.313 1.169 0.913 1.024 0.994 1.219 1.158 1.009 1.116 October 1.153 1.250 1.185 1.085 0.853 0.986 0.905 1.170 1.107 0.948 1.060 November 1.167 1.142 0.942 1.115 0.916 0.837 0.775 1.092 1.047 0.807 0.985 December 1.073 1.064 1.001 1.073 0.968 1.009 1.048 1.049 1.040 1.031 1.034 Weekly 1.135 1.245 1.183 1.100 0.891 0.959 0.939 1.095 1.048 1.015 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.360 1.234 1.134 1.206 1.033 1.665 2.055 1.238 1.062 1.862 1.384 February 1.179 1.064 1.081 0.990 1.018 1.469 1.848 1.078 1.066 1.664 1.236 March 1.075 0.970 0.969 0.961 1.033 1.452 1.697 0.996 1.003 1.579 1.165 April 1.035 0.963 0.916 0.946 0.938 1.400 1.647 0.966 0.961 1.524 1.121 May 1.072 0.987 0.942 0.908 0.890 1.331 1.593 0.979 0.961 1.468 1.103 June 0.921 0.907 0.885 0.855 0.845 1.226 1.381 0.891 0.882 1.307 1.003 July 1.004 0.905 0.930 0.856 0.847 1.253 1.432 0.904 0.897 1.333 1.032 August 1.004 0.984 0.938 0.918 0.915 1.327 1.489 0.962 0.953 1.410 1.082 September 1.104 1.004 0.978 0.920 0.913 1.335 1.607 1.001 0.983 1.471 1.123 October 0.967 0.987 0.907 0.864 0.918 1.306 1.504 0.932 0.930 1.409 1.065 November 0.998 0.923 0.933 1.146 0.885 1.211 1.465 1.003 0.881 1.340 1.080 December 1.079 0.987 0.892 0.971 1.002 1.614 1.834 0.945 0.962 1.731 1.197 Weekly 1.067 0.993 0.959 0.962 0.937 1.382 1.629 0.941 0.924 1.570 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.253 0.989 0.841 0.908 1.061 1.415 1.551 1.002 1.026 1.486 1.146 February 1.099 0.903 0.808 0.827 0.913 1.238 1.346 0.909 0.908 1.299 1.019 March 1.116 0.908 0.810 0.854 1.015 1.355 1.385 0.924 0.942 1.385 1.063 April 1.108 0.925 0.806 0.868 0.956 1.380 1.483 0.928 0.934 1.430 1.075 May 1.145 0.969 0.833 0.859 0.955 1.325 1.503 0.953 0.954 1.420 1.084 June 1.081 0.932 0.820 0.860 0.944 1.301 1.413 0.923 0.927 1.362 1.050 July 1.194 0.971 0.876 0.899 0.993 1.406 1.523 0.966 0.978 1.465 1.123 August 1.136 0.972 0.854 0.888 0.998 1.416 1.468 0.963 0.970 1.446 1.105 September 1.170 1.017 0.874 0.880 0.971 1.389 1.543 0.984 0.981 1.466 1.120 October 1.075 0.920 0.824 0.845 0.952 1.340 1.463 0.917 0.926 1.411 1.060 November 1.131 0.955 0.869 1.016 1.135 1.466 1.500 0.997 1.060 1.487 1.153 December 1.206 0.989 1.668 1.030 1.105 1.741 1.625 1.425 1.310 1.692 1.337 Weekly 1.143 0.954 0.907 0.895 1.000 1.398 1.483 0.974 0.972 1.563 Passenger Car Single Unit Trucks Combinaon Trucks Table 8-16. Day-of-the-week and monthly variation of volume by vehicle class.
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Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.136 1.148 1.123 1.103 1.047 1.275 1.452 1.127 1.112 1.365 1.183 February 1.077 1.104 1.091 1.022 0.905 1.086 1.251 1.074 1.040 1.169 1.076 March 1.037 1.031 1.022 0.980 0.871 1.006 1.190 1.019 0.990 1.098 1.019 April 1.022 1.038 1.032 0.961 0.861 1.004 1.135 1.013 0.983 1.070 1.007 May 1.040 1.040 1.026 0.970 0.848 0.998 1.159 1.017 0.984 1.079 1.012 June 1.026 1.027 1.008 0.968 0.861 0.973 1.118 1.007 0.979 1.046 0.997 July 0.999 0.982 0.983 0.927 0.834 0.958 1.103 0.972 0.945 1.030 0.969 August 1.029 1.036 1.019 0.975 0.856 0.983 1.144 1.017 0.984 1.062 1.006 September 1.072 1.041 1.042 0.978 0.851 0.991 1.172 1.033 0.998 1.082 1.021 October 1.045 1.047 1.034 0.986 0.850 0.991 1.158 1.029 0.993 1.075 1.016 November 1.062 1.035 0.989 1.021 0.903 1.040 1.202 1.028 1.003 1.122 1.036 December 1.084 1.070 1.035 1.015 0.939 1.092 1.320 1.051 1.029 1.207 1.079 Weekly 1.052 1.050 1.034 0.992 0.886 1.033 1.200 1.029 1.000 1.114 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 0.983 0.910 0.895 0.885 1.000 2.460 4.363 0.920 0.936 3.418 1.643 February 0.971 0.888 0.953 0.901 0.877 2.069 3.710 0.929 0.918 2.894 1.481 March 0.879 0.829 0.822 0.823 0.870 2.051 3.411 0.839 0.846 2.733 1.384 April 0.799 0.768 0.771 0.761 0.827 2.277 3.185 0.775 0.785 2.743 1.341 May 1.032 0.805 0.794 0.752 0.778 1.900 3.219 0.861 0.847 2.562 1.326 June 0.864 0.821 0.817 0.820 0.853 1.929 3.020 0.834 0.840 2.458 1.303 July 0.936 0.839 0.859 0.819 0.899 1.925 2.827 0.863 0.870 2.373 1.301 August 0.874 0.843 0.873 0.821 0.846 1.948 3.337 0.855 0.853 2.627 1.363 September 1.060 0.834 0.817 0.786 0.912 1.909 3.118 0.876 0.885 2.512 1.348 October 0.829 0.802 0.806 0.784 0.806 1.855 2.867 0.806 0.806 2.373 1.250 November 0.850 0.818 0.833 0.985 0.942 2.098 3.439 0.873 0.887 2.764 1.424 December 1.032 0.978 0.927 0.945 1.039 2.442 4.247 0.976 0.989 3.345 1.659 Weekly 0.926 0.845 0.847 0.840 0.888 2.072 3.395 0.866 0.870 2.726 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 0.995 0.912 0.871 0.891 1.051 2.519 3.428 0.920 0.947 2.978 1.524 February 1.299 1.004 1.054 1.165 0.959 2.156 3.069 1.129 1.094 2.610 1.529 March 0.891 0.852 0.836 0.842 0.942 2.013 2.785 0.855 0.873 2.391 1.309 April 0.833 0.797 0.807 0.782 0.845 2.826 2.268 0.806 0.815 2.547 1.308 May 1.016 0.839 0.820 0.808 0.876 1.878 2.435 0.874 0.874 2.148 1.239 June 0.855 0.801 0.824 0.799 0.844 1.734 2.135 0.822 0.826 1.934 1.142 July 0.946 0.821 0.848 0.820 0.872 1.827 2.256 0.858 0.862 2.040 1.199 August 0.914 0.861 0.864 0.851 0.891 1.870 2.207 0.876 0.879 2.038 1.208 September 1.023 0.813 0.806 0.772 0.854 1.702 2.300 0.857 0.859 1.986 1.181 October 0.817 0.786 0.760 0.758 0.816 1.731 2.158 0.782 0.789 1.945 1.118 November 0.793 0.749 0.750 0.889 0.877 1.718 2.233 0.797 0.813 1.977 1.144 December 1.174 1.059 0.961 1.013 1.167 2.575 3.435 1.060 1.082 3.011 1.626 Weekly 0.963 0.858 0.850 0.866 0.916 2.046 2.559 0.866 0.875 2.266 Passenger Car Single Unit Trucks Combinaon Trucks Table 8-16. (continued)
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Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.096 1.107 1.073 1.056 0.975 1.198 1.435 1.083 1.062 1.312 1.134 February 1.001 1.045 1.011 0.951 0.876 1.125 1.319 1.009 0.984 1.221 1.047 March 1.017 1.009 0.996 0.974 0.906 1.091 1.261 1.000 0.982 1.176 1.036 April 1.026 1.033 1.016 0.972 0.912 1.138 1.293 1.014 0.994 1.214 1.056 May 1.045 1.043 1.024 0.970 0.881 1.108 1.263 1.018 0.991 1.185 1.048 June 1.011 1.015 0.997 0.960 0.884 1.092 1.253 0.996 0.972 1.168 1.030 July 0.967 0.958 0.968 0.911 0.861 1.065 1.212 0.955 0.936 1.137 0.992 August 1.014 1.017 0.987 0.965 0.870 1.082 1.251 0.997 0.973 1.166 1.027 September 1.075 1.044 1.038 0.994 0.902 1.155 1.328 1.038 1.011 1.241 1.077 October 1.017 1.024 1.005 0.965 0.884 1.123 1.264 1.003 0.979 1.194 1.040 November 1.009 0.983 0.938 1.014 0.926 1.114 1.232 0.987 0.975 1.172 1.031 December 1.036 1.028 0.980 0.979 0.930 1.131 1.349 1.006 0.991 1.239 1.062 Weekly 1.026 1.026 1.003 0.976 0.901 1.118 1.288 1.011 0.990 1.179 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.003 0.947 0.893 0.900 0.924 2.050 3.635 0.937 0.935 2.829 1.479 February 0.886 0.871 0.848 0.824 0.821 1.790 3.103 0.864 0.856 2.441 1.306 March 0.911 0.863 0.859 0.851 0.871 1.904 3.113 0.872 0.872 2.508 1.339 April 0.887 0.863 0.854 0.830 0.870 1.919 3.248 0.862 0.865 2.578 1.353 May 1.006 0.847 0.833 0.802 0.796 1.804 3.145 0.872 0.858 2.466 1.319 June 0.864 0.843 0.848 0.824 0.824 1.796 3.058 0.844 0.840 2.402 1.294 July 0.895 0.812 0.859 0.798 0.842 1.820 2.897 0.846 0.848 2.352 1.275 August 0.882 0.861 0.841 0.836 0.826 1.765 2.957 0.857 0.852 2.357 1.281 September 1.047 0.866 0.858 0.825 0.829 1.806 3.155 0.897 0.883 2.477 1.341 October 0.866 0.843 0.831 0.812 0.823 1.768 2.904 0.837 0.834 2.337 1.264 November 0.868 0.828 0.842 1.024 0.906 1.827 3.001 0.891 0.893 2.413 1.328 December 1.002 0.966 0.911 0.949 0.996 2.060 3.532 0.959 0.967 2.792 1.488 Weekly 0.926 0.868 0.857 0.856 0.861 1.859 3.146 0.879 0.875 2.508 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.073 0.948 0.880 0.897 0.978 1.849 2.361 0.951 0.957 2.095 1.284 February 0.982 0.877 0.810 0.816 0.883 1.590 1.963 0.877 0.879 1.775 1.132 March 0.996 0.862 0.824 0.856 0.940 1.670 2.031 0.885 0.896 1.849 1.168 April 1.007 0.892 0.843 0.863 0.953 1.743 2.166 0.904 0.915 1.953 1.209 May 1.094 0.907 0.837 0.843 0.906 1.640 2.166 0.923 0.921 1.905 1.199 June 1.014 0.895 0.853 0.862 0.925 1.646 2.128 0.906 0.910 1.871 1.189 July 1.030 0.882 0.855 0.853 0.926 1.626 2.037 0.908 0.915 1.828 1.173 August 1.014 0.900 0.843 0.864 0.919 1.647 2.041 0.907 0.911 1.844 1.175 September 1.131 0.935 0.863 0.868 0.933 1.715 2.217 0.946 0.942 1.965 1.237 October 0.985 0.892 0.848 0.861 0.903 1.692 2.092 0.894 0.896 1.892 1.182 November 0.981 0.867 0.838 0.992 1.018 1.729 2.023 0.919 0.939 1.875 1.207 December 1.114 0.967 0.880 0.937 1.042 1.845 2.291 0.978 0.992 2.066 1.296 Weekly 1.035 0.902 0.848 0.876 0.944 1.699 2.126 0.913 0.919 1.935 Passenger Car Single Unit Trucks Combinaon Trucks (c) Urban freeway
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PassengerCar Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.113 1.099 1.072 1.067 1.002 1.195 1.495 1.091 1.074 1.347 1.149 February 1.001 1.021 0.996 0.955 0.889 1.082 1.303 0.994 0.973 1.193 1.035 March 0.992 0.985 0.983 0.954 0.880 1.085 1.364 0.979 0.960 1.226 1.035 April 0.995 0.988 0.978 0.944 0.876 1.060 1.274 0.976 0.957 1.167 1.016 May 1.001 0.961 0.959 0.920 0.844 1.018 1.203 0.960 0.937 1.111 0.987 June 0.973 0.968 0.960 0.926 0.863 1.061 1.271 0.958 0.939 1.165 1.003 July 0.997 0.972 0.971 0.926 0.875 1.067 1.253 0.966 0.949 1.161 1.008 August 0.991 0.993 0.979 0.951 0.878 1.077 1.287 0.977 0.957 1.181 1.022 September 1.063 0.996 0.987 0.961 0.876 1.090 1.311 1.003 0.978 1.202 1.041 October 1.010 1.005 0.992 0.962 0.880 1.078 1.315 0.994 0.970 1.197 1.034 November 1.023 1.005 0.980 1.066 0.932 1.127 1.362 1.019 1.002 1.246 1.071 December 1.057 1.063 1.012 1.019 0.989 1.167 1.412 1.038 1.029 1.291 1.103 Weekly 1.018 1.005 0.989 0.971 0.899 1.092 1.321 0.986 0.967 1.217 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.028 0.914 0.887 0.916 0.964 2.424 5.362 0.942 0.947 3.898 1.785 February 0.890 0.868 0.859 0.816 0.841 2.095 4.525 0.859 0.856 3.311 1.556 March 0.878 0.843 0.898 0.834 0.830 2.031 4.110 0.864 0.857 3.074 1.489 April 0.869 0.802 0.793 0.799 0.834 2.136 4.493 0.814 0.818 3.305 1.532 May 0.974 0.887 0.823 0.788 0.783 1.994 3.809 0.875 0.855 2.902 1.437 June 0.849 0.843 0.839 0.823 0.821 1.815 3.234 0.838 0.835 2.501 1.318 July 0.897 0.818 0.859 0.795 0.835 1.905 3.554 0.843 0.843 2.728 1.380 August 0.850 0.836 0.846 0.829 0.814 1.907 3.717 0.840 0.835 2.809 1.400 September 0.982 0.789 0.784 0.761 0.780 1.929 3.808 0.833 0.823 2.869 1.405 October 0.830 0.803 0.806 0.775 0.773 2.004 4.012 0.806 0.798 3.007 1.429 November 0.855 0.811 0.848 0.987 0.890 2.149 4.552 0.878 0.881 3.350 1.585 December 0.973 1.077 0.924 0.958 1.061 3.028 4.851 0.986 1.002 3.940 1.839 Weekly 0.906 0.858 0.847 0.840 0.852 2.118 4.169 0.864 0.859 2.876 Month of Year Monday Tuesday Wednesday Thursday Friday Saturday Sunday Mon-Thu Weekday Weekend Monthly January 1.191 1.039 1.052 1.086 1.123 2.739 4.495 1.096 1.102 3.627 1.818 February 1.057 0.959 0.985 0.967 0.944 2.627 3.539 0.993 0.983 3.093 1.582 March 0.971 0.890 0.933 0.925 0.925 2.345 3.162 0.930 0.929 2.756 1.450 April 0.943 0.849 0.833 0.811 0.830 1.845 2.773 0.861 0.856 2.310 1.269 May 1.012 0.929 0.867 0.820 0.803 1.932 2.798 0.910 0.889 2.368 1.309 June 0.839 0.818 0.833 0.795 0.800 1.743 2.370 0.823 0.818 2.060 1.171 July 0.983 0.855 0.911 0.842 0.865 1.804 2.648 0.897 0.892 2.229 1.273 August 0.994 0.908 0.928 0.906 0.872 1.923 2.850 0.937 0.923 2.388 1.340 September 1.123 0.919 0.934 0.883 0.916 2.063 3.212 0.967 0.957 2.638 1.436 October 1.037 0.944 0.951 0.938 0.910 2.055 2.893 0.967 0.955 2.465 1.390 November 1.069 0.955 1.012 1.173 1.074 2.258 3.549 1.054 1.058 2.902 1.584 December 1.263 1.301 1.139 1.201 1.321 3.091 4.408 1.230 1.249 3.757 1.960 Weekly 1.040 0.947 0.948 0.946 0.948 2.202 3.225 0.931 0.930 2.606 Passenger Car Single Unit Trucks Combinaon Trucks Table 8-16. (continued)
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223 C H A P T E R 9 This chapter covers special purpose applications of traffic forecasting beyond the basics of traffic forecasting that are primarily based on traffic volumes. This chapter includes the following: • Basic highway design traffic forecasting products; • Interpolation of traffic forecasts; • Improving the vehicle mix accuracy of forecasts or data extrapolations; • Special needs of ESALs; • Special needs of benefit-cost analysis; • Special needs of toll/revenue forecasts; • Special needs of work zone congestion: diversion and delay forecasts; • Special needs of environmental justice; and • Special needs of traffic impact studies.
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224 years, one should interpolate between two of the same conditions, for example, a base year build scenario and a future year build scenario. Another method that can be used is to interpolate the trip tables and then run them through the assignment process.
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225 year on the same road. This is particularly true if the functional classification of the road is not expected to change significantly in the forecast year.
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226 or may cover a smaller area such as a specific county. The table can be made more complex by including greater specificity in vehicle classification by distinguishing between buses, single unit trucks, and combination trucks rather than using the twoclass system shown below, or tables can be made simpler by, for example, distinguishing only between freeway and nonfreeway roads.
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227 these guidelines in determining how to address vehicle classification data in their forecasts. 9.3.2 Vehicle Mix Considerations in Air Quality Analysis Vehicle mix plays a crucial role in air quality analysis.
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228 designed pavement might fail early while overly conservative design might prove to be expensive. Pavement design is an excellent area in which to allow the use of ranges of results to give designers latitude.
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229 Figure 9-2. Florida ESAL methodology.
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230 Notes: ADT, T%, and A/T based on traffic station information. Everything else comes from 2006 Aggregated ESAL report from Kentucky Transportation Center.
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231 9.4.5 Mechanistic-Empirical Pavement Design Guide The new MEPDG promulgated by AASHTO is meant to eventually replace the use of ESALs. The reasons for using MEPDG are greater flexibility and more accuracy, as explained in Mechanistic-Empirical Pavement Analysis and Design Educational Module (130)
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232 9.5 Special Needs of Benefit-Cost Analysis Benefit-cost analysis provides information useful to making a decision about whether or not to invest in a transportation improvement. Transportation economists consider highway investments that demonstrate a benefit-cost ratio greater than 1.0 as economically efficient.
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233 9.5.3 Regionally Significant Transportation Improvements Travel demand models, and network assignment models in particular, are the tool of choice to analyze the impacts of larger scale highway improvements. Most highway network assignment models are designed to achieve travel time equilibrium, which means that travelers from an origin to a destination cannot improve their travel time by taking a different path.
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234 costs exceed benefits ($31,053 versus $25,914) implying that constructing the project as configured is not justified from on transportation efficiency grounds.
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235 The relationship between independent variables and toll share can be described graphically in a toll diversion curve. Figure 9-7 presents a hypothetical curve showing the relationship between travel time savings and toll share, assuming a toll increase of $2.00.
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236 Work zone impacts can range from being minor, such as a late-night lane closure on a road segment with ample capacity, to being severe, such as a full freeway closure over a weekend. Impacts can include substantial queuing and delays on the highway under construction; blocking of off-ramps due to queues; increased traffic volumes and delays on nearby streets due to detoured and diverted traffic; various environmental impacts; various community impacts; and driver confusion, frustration, and loss of productivity.
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237 • Many drivers passing through rural and short-term work zones have poor information as to delays and alternative routes, in spite of the best efforts of the agency. • There are strong biases toward staying on originally planned paths.
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238 • Accessibility to non-motorized (walking and bicycling) facilities; • Proximity to jobs, schools, health care, and so forth; and • Right-of-way impacts/displacement.
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239 C H A P T E R 1 0 This chapter examines methods other than travel models to develop project-level traffic forecasts. It presents various options in step-by-step procedures that include the method's background, the context of the technique, words of advice, disadvantages/issues of the method, strategies to minimize the impacts of the disadvantages/issues, execution steps of the method, and illustrative examples.
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240 In this case, n is the number of periods from the arbitrary starting point of the analysis, a is the period-to-period increment of traffic volume, and b is the estimated volume for the arbitrary starting period. Growth factor and linear trend methods are elementary.
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241 All data items need to be coordinated. That is, they must be referenced to the same starting date and there must be complete data in all of the time series for all dates for which there is volume data.
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242 Central moving averaging removes periodic variation in a time-series, so the remaining data series reflects only longterm trends. Box-Cox transformations create more consistent variation throughout the data series.
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243 Time series are usually ordered with earliest dates first. Each variable gets its own column and each time period gets its own row.
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244 10.1.7.1 Linear Trend Model Table 10-2 contains the central moving averages for the illustrative example, computed from the current month, all 6 months behind and all 5 months later. Table 10-3 contains the seasonal adjustment factors.
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245 ment factor. So it can be said that the forecasted traffic is 13,954 ± 116 vehicles per month.
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246 new facility. These five steps are presented in the remainder of this section.
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247 Fnopass = no-passing zone factor -- = 1.00 for level terrain, = 0.97 – 0.07 *
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248 2. Determine the distance between the OD pair using the existing route (de)
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249 by observing the effects of system changes locally or elsewhere. Many elasticities are available in the transportation literature.
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250 10.3.6 Executing the Technique 10.3.6.1 Special Data Preparation An elasticity may be obtained by comparing before and after conditions when there is a change in a fundamental property of the system, by doing a statistical analysis of a time series, or by adopting a value from another location. There are different ways to compute an elasticity from the same data, so when adopting an elasticity from another location it is important to understand how that elasticity was originally calculated.
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251 consider cutting service as well as increasing fares, so it is important to control for any service cuts. The Federal Transit Administration provides data on total system revenues and total unlinked trips.
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252 And the additional effect of the change in fare is 0.30 log 1.75 log 1.50 log 4,330 4,134 2 1Q log [ ]
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253 lane distributions) , and travel time variability.
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254 The 1994 update to the 1985 edition of the HCM introduced a refined set of speed-flow relationships for basic freeway sections where average passenger car speed is a function of traffic flow rate. This set of curves was developed through research under NCHRP Project 3-45.
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255 10.4.4 Freeway Weaving Segments Freeway weaving segment capacity is determined to occur at that point where breakdown of the segment occurs and is controlled by one of two conditions: • When the average density of the segment reaches 43 pc) / mi/ln; or • When the total weaving demand flow rate exceeds 2,400 pc/h for two lanes or 3,500 pc/h for three lanes.
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256 the average speed. Average speed as a function of free flow speed and flow rate is obtained from the speed-flow curves under base conditions, as shown in Figure 10-6.
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257 percentage of time that vehicles must travel in platoons due to the inability to pass slower vehicles; • For Class II highways, LOS is based on PTSF; and • For Class III highways, LOS is based on the percent of free flow speed (PFFS) , which represents the ability of vehicles to travel at or near the posted speed limit.
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258 – Are the mode choice models the same (logit/factored trip table) and do the models include transit components?
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259 STEP 3 . The Newer Counts Form This form allows counts to be keyed in or imported from a spreadsheet.
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260 C H A P T E R 1 1 This chapter presents both real-world and theoretical examples of project-level traffic forecasting applications. A suburban arterial, a "network window" approach for a small area in a much larger metropolitan area, a small city application, an high-occupancy vehicle (HOV)
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261 • Selecting Link/Zone Analysis, and • Windowing and Model Refinements with OriginDestination (OD) Matrix Estimation.
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262 and residential areas, it will be necessary to develop intersection turning movement forecasts that will reflect anticipated conditions for which assessments of the current traffic signal system can be made. An important component of the turning movement forecasts is the directional distribution, D
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263 scheme. A summary of AM peak-hour turning movement counts for the intersection is provided in Figure 11-2.
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264 existing turning movement volumes are multiplied by the ratio of the future year travel demand model turning volume divided by the base year travel demand model turning volume. The calculations and results are shown in Figure 11-4.
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265 analysis zone (TAZ) 174 in the travel demand model (see Figure 11-6)
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266 Figure 11-6. Proposed development within the travel demand network and TAZ structure.
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267 the estimated number of residences served by the cross street or the type and size of development (if the side street was an access drive to the development)
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268 developed from cordon survey data or from assumptions of driver behavior over short distances. No cordon survey data were available for this application, so the table was developed based on assumptions of driver behavior.
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269 %RMSE 24 422 174 87 347 190 INT# 42 142 64 284 1 KY 80 Bypass 221 : 3326 409 138 : 3335 238 36.3% 41 401 115 432 117 787 546 232 607 601 130 789 X 209 685 X INT# 352 34 358 171 2 KY 80 Business 280 : 3386 155 177 : 3286 66 38.6% 124 139 244 98 86 1180 117 160 912 206 Note: Southbound Le Turn Movement Prohibited at this loca on 75 805 88 156 777 93 INT# 120 68 122 104 3 W Columbia St.
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270 The calculated root-mean-square (RMS) error for all of the estimated turning movements was 31.6% of the mean traffic count.
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271 Alternatives, consisting of one or more ramp closures along with one or more lane closures, can be evaluated for their impacts on the local traffic system. Beyond its intended purpose, this model could be used for traffic impact studies, lane widening, intelligent transportation systems (ITS)
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272 11.2.6 Special Data Preparation Steps The seed OD table was created by a gravity model with impedances as a function of the minimum number of turns between an origin and a destination. Historic traffic counts indicated that there were fewer left turns than right turns.
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273 11.2.8 Full Weekend Closure of I-43/894 Evaluation WisDOT needed to install a new culvert across I-43/894 between 27th Street and the Mitchell Interchange. A decision was made to completely close the freeway in both directions from late Friday night to early Monday morning.
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274 shows the before and after delays for the intersection of the 27th St. off ramp, and the intersection of 27th St.
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Figure 11-12. Region of Charleston, South Carolina.
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276 between Glenn McConnell Parkway and Savannah Highway (US 17) , west of downtown Charleston.
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Figure 11-15. "No-Build" scenario select link flow map.
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278 Figure 11-16. "Build" scenario select link flow map.
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279 Figure 11-17. Difference in select link volumes.
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280 For comparison, analysis of the new construction project as the selected link can be conducted to evaluate the spatial distribution of the new roadway project users. Figure 11-18 shows graphically the behavior of traffic traversing the new roadway project, where the new roadway project selected link is shown in darker gray near the left-center of the figure.
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281 Drive by time of day. The following data are used for the purposes of this case study: • Study area size -- small urban area, • Roadway facility type -- collector, • Day of week -- average day, • AM peak hour -- 8:00–9:00, • PM peak hour -- 5:00–6:00, and • Table 8-8.
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282 11.4 Case Study #4 -- Activity-Based Model Application for Project-Level Traffic Forecasting/ Analysis: HOV to HOT Lane Conversion 11.4.1 Introduction This case study demonstrates the development and application of an activity-based travel demand model for project-level traffic forecasting and analysis. The specific project is an HOV to HOT lane conversion scenario in the Atlanta metropolitan area.
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283 11.4.3 Running the Activity-Based Travel Demand Model Running the activity-based travel demand model required the generation of a synthetic population for the entire region of Atlanta. The synthetic population is generated using the population synthesizer embedded in the ARC activity-based travel model system.
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284 Figure 11-20 shows the patterns of population change in the region predicted by the population synthesizer. It can be seen that much of the growth occurs in outlying areas while the central areas show more modest growth over the 25-year time period.
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285 11.4.4 Managed Lanes Results The activity-based travel model system was applied by ARC to evaluate link attributes under the managed lane scheme. Table 11-13 presents an overview of the results depicting selected link attributes for the managed lanes and the general purpose lanes.
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286 trend seen among shared-ride trips is largely a downward pattern, suggesting that shared trips were probably enjoying uncongested travel times even in the AM peak period before the HOV lanes were converted to HOT lanes. Thus, after the conversion to HOT lanes, these trips do not realize appreciable benefits; the distribution of shared-ride trips by minutes of time saved shows smaller travel time savings than the distribution for drive-alone trips.
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From page 285... ...
287 the tolling strategy (over the 25-year forecast period)
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From page 286... ...
Figure 11-25. Blue Water Bridge location map.
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From page 287... ...
289 is somewhat distant from the city of Detroit, its location is ideal for shipping from most other parts of Michigan and many points west, such as into the heart of southern Ontario, including the Toronto metropolitan area. I-94 and I-69 meet just west of the Bridge, and they connect to Ontario Highway 402, also known in Canada as the King's Highway.
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From page 288... ...
290 Figure 11-29. Scatterplot of westbound truck traffic and Michigan population.
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291 Figure 11-31. Scatterplot of westbound truck traffic and fuel price.
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292 years, but in the later years appear to correlate negatively instead of positively. NAFTA and the attacks of 9/11 can be handled as dummy (0, 1)
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From page 291... ...
293 tions or by statistical add-ins. Statistical add-ins are recommended because they provide statistics on goodness of fit, such as t-scores and R-square statistics.
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From page 292... ...
294 It is important to inspect variables for the logically correct sign as well as having a significant t-score. The Michigan population has the correct sign in all models, but incorrect signs are observed for the Ontario population, U.S.
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295 0 100 200 300 400 500 600 700 Fe b 84 Fe b 85 Fe b 86 Fe b 87 Fe b 88 Fe b 89 Fe b 90 Fe b 91 Fe b 92 Fe b 93 Fe b 94 Fe b 95 Fe b 96 Fe b 97 Fe b 98 Fe b 99 Fe b 00 Fe b 01 Fe b 02 Fe b 03 Fe b 04 Fe b 05 Fe b 06 Fe b 07 Fe b 08 Fe b 09 Fe b 10 Figure 11-36. Transformed data series (b 0.5)
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From page 294... ...
296 of every month between the last data point and the desired future month. Since this model is forecasting a transformed variable, the forecast will need to be untransformed before being reported or used.
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Figure 11-38. Approximate area covered by the freeways in the microsimulation.
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298 Mainline and ramp traffic counts were obtained from Portland State University's Portland Transportation Archive Listing (PORTAL)
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From page 297... ...
299 Figure 11-41. Comparison of assigned volumes to ground counts for the refined 2-hour AM OD table.
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Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.