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122 This document provides guidance on using the FREEVAL-RL (FREeway EVALuationâReliability) computational engine, which implements the freeway reliability analysis methodol- ogy developed in SHRP 2 Project L08. FREEVAL-RL is a Microsoft (MS) Excelâbased computational engine coded in the Visual Basic for Applications platform. The tool has a graphical user interface to facilitate data entry and navigation through the tool. The core computational engine of the tool is an enhanced version of FREEVAL-2010, which is the com- putational engine for the freeway facilities methodology in Chapter 10 of the Highway Capacity Manual 2010 (HCM2010) (Transportation Research Board of the National Academies 2010). Some modifications and enhancements to the core computational engine of FREEVAL-2010 have been made to make the tool and method ready for reliability analysis. These changes have been documented in a separate working paper. Overview of FREEVAL-2010 Base Method The base computational engine, FREEVAL-2010, is a comput- erized, worksheet-based environment designed to faithfully implement the operational analysis computations for under- saturated and oversaturated directional freeway facilities in the HCM2010. It incorporates the freeway segment proce- dures outlined in Chapters 11, 12, and 13 of the HCM2010 for basic freeway segments, weaving segments, and merge and diverge segments, respectively. It also contains cell transmis- sion modelâbased algorithms for oversaturated freeway facil- ities, and it is able to track queue accumulation and dissipation over multiple segments, as well as multiple time periods. This oversaturated flow procedure is a critical requirement for freeway reliability analysis, which is expected to contain many congested scenarios. The FREEVAL-2010 tool allows a maximum of 70 freeway segments to be analyzed for a maximum duration of up to twenty-four 15-min time intervals (6 h). The engine can generally handle any facility that falls within these temporal and spatial constraints. However, it is highly recommended that the total facility length not exceed 9 to 12 mi to ensure consistency between demand variability over time and facil- ity travel time. Further, the first and last facility segments should be uncongested in the first and last time intervals to allow all queues to form and clear within the facility and study period. This practice assures that the performance measures account for the full extent of congestion and delay. These aspects are discussed in detail in HCM2010, Chapters 10 and 25. In conformance with the HCM2010, all analyses are carried out using U.S. customary units. FREEVAL-2010 is organized as a sequence of linked MS Excel worksheets and can be used autonomously to analyze individual freeway segments or an entire directional facility. The user must define different freeway segments and enter all necessary input data that are required in the individual seg- ment chapters. These include segment length, number of lanes, length of acceleration and deceleration lanes, heavy and recreational vehicle percentages, and the free-flow speed. The latter can also be calculated in FREEVAL-2010 from the segment or facility geometric attributes. Consistent with Chapter 10, FREEVAL-2010 covers under- saturated and oversaturated conditions. For oversaturated analysis periods, traffic demands, volume served, and queues are tracked over time and space, as discussed in detail in HCM2010, Chapter 25. In addition to characterizing over- saturated conditions, the most significant difference from the segment-based chapters is that FREEVAL carries out all cal- culations using 15-min flow rates (expressed in vehicles per hour). It therefore does not use a peak hour factor. To repli- cate the example problem results found in the segment chap- ters, peak hour factorâadjusted flow rates must be entered in FREEVAL directly. Heavy-vehicle adjustments (using general terrain factors or directly input for specific grade segments) are automatically handled by the methodology. A p p E n d i x A FREEVAL Userâs Guide
123 The computational engine is further designed to allow the user to revise input data following the completion of an analy- sis. This feature is intended to perform quick sensitivity or âwhat ifâ analyses of different demand scenarios or geometric changes to the facility. However, the user is cautioned to ensure that all prior inputs are maintained when using FREEVAL for extensive scenario evaluation. FREEVAL-2010 is not a com- mercial software product. It relies on the voluntary commit- ment of the Transportation Research Board Committee on Highway Capacity and Quality of Service to address software bugs that may emerge in the course of its use and to incorpo- rate methodological changes over time. The next section focuses on the step-by-step coding pro- cess of the FREEVAL-RL tool. The analysis process starts with a basic input of project summary information and continues with a detailed input of the facility seed file in a process simi- lar to FREEVAL-2010, as well as detailed input in the freeway scenario generator (FSG). Each of these components is dis- cussed in detail. The appendix concludes with a discussion of the automated output generated by FREEVAL-RL. FREEVAL-RL Coding process This section provides step-by-step guidance for the use of the FREEVAL reliability (FREEVAL-RL) tool. There are five major steps to run the tool: 1. Project ID information; 2. Seed file management; 3. Scenario management; 4. Running scenarios; and 5. Viewing results. In the first step, the user inputs general descriptive infor- mation about the project. Information gathered in this step is used to name the associated files. In the second step, seed file management, the user can create either a new seed file or view or edit an existing file. This step is conceptually similar to the existing process of coding a freeway facility in the FREEVAL- 2010 computational engine, as the user enters detailed geo- metric and operational characteristics of the seed file. In the third step, FREEVAL-RL invokes the FSG to generate the vari- ous scenarios. The FSG is a separate computational engine that is automatically invoked and exchanges information with the FREEVAL-RL tool. In Step 4, the user runs the generated sce- narios, which can be a time-intensive process, as many sce narios are automatically executed in a batch-run process. Step 5 gener- ates a summary output report or provides access to a more detailed output matrix file. Each step is activated by clicking the associated button in the main menu. The main menu can be accessed by clicking Go to The Main Menu in the Intro worksheet of FREEVAL. This button can always be used in the middle of the process to display the FREEVAL-RL main menu (Figure A.1). The FREEVAL-RL tool requires the use of Visual Basic macros, which may need to be allowed or enabled depending on the settings in the MS Excel program. Those not familiar with MS Excel security options can refer to the MS Excel 2010 Figure A.1. FREEVAL-RL Intro worksheet.
124 Figure A.2. FREEVAL-RL main user form, Step 1. Figure A.3. Step 1, User form. Security Options Quick Guide of this appendix for quick guidance. Step 1. Enter Project Summary The FREEVAL-RL analysis process starts with entering proj- ect summary information. Start by clicking Enter Project Summary on the main user form, as shown in Figure A.2. After selecting the Enter Project Summary button, four input boxes appear (see Figure A.3): 1. File Name Header: This entry will be used as the file header name for all files generated in the project. For example, if the user enters âI-999,â all the file names associated with this project that are generated by the computational engine will begin with the string of characters âI-999.â 2. Summary Report Header: This entry is depicted as the header in the summary report discussed later in this appendix. 3. Analyst: Information about the analyst. 4. Analysis Date: Date when the analysis is performed. After entering all desired information, select OK to return to the main menu. Step 2. Seed File Management In this second step the user creates, edits, or views the seed file, which is a single, representative FREEVAL input file. Click Create The Seed File to start a new seed file (see Figure A.4). If a seed file has already been created, click View/Edit The Seed File to access the file; this button will be inactive if the seed file has not been created. If the user elects to create a new seed file, another menu form will appear to collect the necessary information to create Figure A.4. Seed file management user form.
125 the seed file. This user form, shown in Figure A.5, requires the following entries: 1. Study Period Start Time: Here, hours and minutes are entered separately, with hours expressed in a 24-h format (e.g., 6 p.m. = 18) and minutes rounded to the nearest 15 min (e.g., 00, 15, 30, or 45). 2. Study Period End Time: Time of day when the analysis ends, expressed in two-digit hour and two-digit minute format (hh:mm). Note that the total study period duration cannot exceed 6 h. Also, the start and end times should be for the same calendar day. 3. Analysis Year: The year in which the reliability analysis is performed. 4. RRP Start Date: Reliability reporting period (RRP) start day in two-digit month and two-digit day format (MM/DD). 5. RRP End Date: RRP end date in two-digit month and two-digit day format (MM/DD). Note that the RRP end date must follow the RRP start date in the same year. 6. Seed Demand Day in RRP: The date represented by the demand volumes used in the seed file is entered. Similar to other RRP-related information, this date should be entered in the two-digit month and two-digit day format (MM/DD). Note that the seed demand date must be able to be extrapolated to the RRP if they do not overlap. Say, for example, the RRP consists of the summer months only, and the seed data were collected in the winter months. Appro- priate demand adjustment factors (DAFs) from winter to summer must be made available to the analyst in order to be able to reconstruct the summer demand patterns. 7. Number of HCM Segments: Total number of facility HCM segments is entered in this field. 8. Terrain: Please consult the FREEVAL-2010 user guide for exercising this option. 9. Ramp Metering: Please consult the FREEVAL-2010 user guide for exercising this option. 10. Jam Density: Proposed jam density of the facility is selected in this combo box. 11. Capacity Drop in the Queue Discharge Mode (%): This is a recent enhancement to the HCM2010 freeway facil- ities methodology. It indicates the percentage capacity drop when operating the queue discharge mode after breakdown (demand/capacity > 1). This range varies from 0% to 10%. After this step, the structure of the seed file will be con- figured permanently. Therefore, in the preceding input box the user must confirm that the provided information is final in order to proceed with the current input settings (see Figure A.6). Figure A.5. Seed file creation user form.
126 After the user clicks Yes in the input box, as shown in Fig- ure A.6, the seed file is created using the total number of HCM segments and analysis (i.e., 15-min) periods. At this stage, the user enters the detailed facility data for the differ- ent analysis periods. In order to complete seed file data entry, two substeps are required related to coding the segment type and segment data entry. These substeps (see Figure A.7) are explained in detail in the following sections. Step 2A. Code Segment Types Here the user enters each HCM segment type (see Figure A.8). Note that the number of columns has been reduced to match the number of segments defined by the user. The proper way to define the appropriate number of segments is explained in HCM2010, Chapter 10, including the requirement that the first and last segments of the facility should be coded as basic segments. The number of input worksheets generated matches the number of (15-min) analysis periods entered earlier. Using drop-down menus, the user defines each segment as a basic, on-ramp, off-ramp, weaving, or overlapping ramp segment following HCM conventions (see HCM2010, Chapter 10). After identifying all segment types, the user clicks the Step 2-A: Segment Types Entered button. After this action, a macro will automatically black out all unneeded data entry cells. Step 2B. Segment Data Entry Next, the user enters data for each segment and each analysis period in sequence (see Figure A.9). The common inputs needed for all segments are length (feet), number of lanes, free-flow speed (miles per hour), segment demand (number of vehicles per hour), percentage trucks, and percentage rec- reational vehicles. The user can use several adjustment factors that may affect the operations of the facility. These factors are discussed in a later section. For all ramp and weaving seg- ments, the user further needs to enter the ramp demand flows and can adjust the heavy-vehicle percentages as desired. An analysis period corresponds to a 15-min period, and as a result all volume inputs should be in the form of 15-min demand flow rates (in vehicles per hour). No peak hour fac- tor adjustment is necessary. After entering all input for the first analysis period, the user proceeds to the remaining analysis periods to enter the corre- sponding input data. For all subsequent analysis periods, some inputs are automatically copied from the ât = 1â worksheet. However, the engine generally allows the user to override these automatically generated entries. Demand volumes always need to be entered for all analysis periods. After completing all inputs for all analysis periods and checking for correctness, the user clicks Run The Seed File/Go to The Main Menu. If this is the first time the seed file has been run, clicking the Run The Seed File/Go to The Main Menu button will Figure A.6. Data entry verification input box. Figure A.7. Step 2 substeps: Step 2A (segment type coding) and Step 2B (segment data entry).
127 automatically execute the seed file, after which the main menu user form will be displayed. If the seed file has already been run (e.g., View Mode), the main menu is opened with- out running the seed file. The seed file is automatically saved when the run process is finished, using as a header the string specified in the initial project information dialog box. For more information regarding how to code any HCM2010 seed file and for detailed interpretation of the output, refer to the FREEVAL-2010 user guide available in Volume 4 of the HCM2010. Optional Step. Revise Input Data As an optional step, the user can revise inputs in the seed file by clicking View/Edit The Seed File in the second step and clicking Revise Input Data in the Results Summary worksheet (see Figure A.10). Clicking Revise Input Data opens a dialog box similar to Create The Seed File user form. Please use caution in this step because the total number of time periods and number of HCM segments should definitely remain fixed (see Figure A.11). Figure A.8. Step 2A: entering facility segment types. Figure A.9. Step 2B: segment data entry.
128 Figure A.10. Revise input data. Figure A.11. Revise input data user form.
129 Step 3. Scenario Management The goal of this section is to provide guidance for users who wish to generate scenarios for reliability analysis. Scenario generation is the third step in the reliability analysis using FREEVAL-RL. By clicking the Scenarios Management button in the FREEVAL-RL main menu, the user is prompted to locate the FSG file. This separate MS Excel file should be located within the same working directory as the FREEVAL-RL file. Sepa- rate copies of FREEVAL-RL and FSG can be saved in separate folders for each reliability analysis. The user either can open an empty FSG file or use an earlier version (in which the file name starts with the project name) that has been previously customized for a facility. When an appropriate FSG file is selected, the user is directed to the FSG file by clicking Open. The FSG process consists of five steps in which the user enters the different types of information needed to generate the recurring and nonrecurring congestion scenarios for FREEVAL-RL on the following worksheets: 1. Start worksheet; 2. Demand pattern worksheets; 3. Weather probability worksheet; 4. Incident probability worksheet; and 5. Detailed scenario worksheet. The five steps should be followed in order; otherwise, the scenario generation process could fail. Each step is discussed in detail. Step 3A. Start Worksheet As a first step, the user must locate the appropriate seed file for the FSG to extract the necessary information for developing and generating scenarios. Click Step 1: Read Seed File in the Start worksheet. The seed file is the FREEVAL-RL file that the user has created in the previous steps. Figure A.12 shows the schematic of the Start worksheet. As a general rule, in all FSG worksheets yellow-highlighted cells represent input data cells that can be entered or altered by the user. Proceed to the next step by clicking Step 2: Demand Pattern Configuration. Step 3B. Demand Pattern Worksheets In this step, the time-dependent demand patterns are defined in the RRP. It consists of two worksheets. In the first, the Figure A.12. Schematic of the Start worksheet in the FSG.
130 overall demand pattern input is displayed in a calendar for- mat to show the configuration of demand patterns for the subject facility. In this step, the user configures similar sea- sons, months, and weekdays that will be combined within the same demand pattern. In the second worksheet, the user can configure the daily and monthly DAFs based on daily and monthly variability of traffic demand for the subject facility or by using national defaults for urban and rural freeways. Step 3B.1. DemanD pattern Configuration WorkSheet If no demand pattern has been coded previously, then the calendar at the middle of the screen will be empty. Figure A.13 shows a previously coded Demand Pattern worksheet. Each number in parentheses (and cell color) following a calendar date represents a unique demand pattern by day of week and month of year to be analyzed. Time-of-day demand varia- tions have already been incorporated as input into the seed file in various 15-min analysis periods. By clicking the Edit Demand Pattern button on the left of the screen, the user is directed to a form that enables the defi- nition or redefinition of the different demand patterns, as shown in Figure A.14. This form consists of two sections. The left portion is for configuring the demand patterns across days of the week; the right section is for configuring the demand patterns across months of the year. To define a demand pattern, ordinal numbers starting from 1 are used to assign each day to a demand pattern. For example, if all weekdays are assumed to have the same demand pattern, the value â1â is entered for each weekday. If, on the other hand, Mondays, Tuesdays through Thursdays, and Fridays have dif- ferent patterns, then 1 is entered for Mondays; 2 for Tuesdays, Wednesdays, and Thursdays; and 3 for Fridays, as shown in Figure A.14. On the right side of the form, the same data entry logic applies for designating demand variability across months. Here, the user can combine different months of the year into the same demand pattern. Defaults are available for using a different pattern for each month or for combining months into four seasons or two seasons. The selection of daily and monthly demand pattern combination should be informed by local traffic data on the subject facility. By clicking the Apply Default Demand Pattern button located on the upper section of the form, appropriate demand patterns based on national defaults are inserted. By clicking Accept Demand Pattern and Continue, the user is directed back to the Step 2A worksheet. The user can now exclude specific days (e.g., holidays or special event days) from the analysis by clicking Edit Excluded Days in the Demand Pattern worksheet. The Add or Delete Excluded Days form pops up for the user to enter calendar dates to be excluded from the reliability calculations (see Figure A.15). If an error Figure A.13. Demand Pattern worksheet in the FSG.
131 is made, a day can be added using the Add Excluded Day functionality. Step 3B.2. DemanD WeekDay-month WorkSheet The user can assign demand adjustments (called multipliers) for the facility in the table provided in the weekday-month demand multiplier worksheet. All adjustments in this table are based on the ratio of the cell value to the annual average daily traffic (AADT) for the facility being analyzed. If such values do not exist locally, then the user can select tabulated national default values for either urban or rural freeway facil- ities. In the implementation of the method, the demand for each scenario is adjusted from the seed file values. This process is best explained using a numerical illustration. Say the user coded in the seed file traffic demands that repre- sent Thursdays in January. Figure A.16 shows that Thursdays in January have a demand multiplier equal to 1.052. This means that the seed file demands are 5.2% higher than the AADT. To generate demand volumes for Fridays in the spring (Demand Pattern 6 in Figure A.13), for which the multiplier is computed at 1.198 (a weighted average based on the high- lighted numbers in Figure A.16), the demand volumes for each analysis period in the seed file are multiplied by the ratio 1.198/1.052, or 1.139. It goes without saying that for demand patterns with multipliers below 1.052 (e.g., Mondays, Tues- days, and Wednesdays in the fall in Figure A.16), the resulting ratio would be less than 1.0. Finally, by clicking Insert Facility Specific, the table will be cleared and the user can enter locally derived demand multi- pliers for the facility. Step 3C. Weather Probability Worksheet This worksheet, which requires four categories of informa- tion, is designed to capture all necessary weather information for the generation of scenarios that include weather events and their impacts. Figure A.17 depicts a sample weather worksheet screenshot with data shown for Raleigh, North Carolina. The upper table in Figure A.17 shows the temporal proba- bilities for different weather categories for each month. Each cell represents the ratio of the number of hours in which a weather event occurred divided by the number of hours in the study periods falling in each month. For example, the 5.911% value (light snow in January) represents the ratio (percentage) Figure A.14. Demand Pattern configuration form in the FSG. Figure A.15. Add or Delete Excluded Days form in the FSG.
132 of hours in the 2 to 8 p.m. study period in which light snow occurred in January in Raleigh to the total number of hours in January between 2 and 8 p.m. These estimates are based on 10 years of meteorological historical data extracted for 101 metro politan areas in the United States. The user can directly download the probabilities by clicking the Raleigh, NC button shown in Figure A.17, selecting the metro area from the pull-down menu, and then clicking Extract Long Term Regional Weather Data for Specified Location. The user can, of course, override any cell values and directly enter facility- specific weather probabilities when those are available. The lower table documents the key operational character- istics of each weather category. The first row pertains to the mean duration of each weather type with respect to the loca- tion of the facility. If the FSG weather database is selected to fill the upper table (for the nearest metropolitan area), then the mean duration for weather types for the selected metro- politan area will be automatically filled in the first row. The second and third rows in the lower table require weather event inputs for the capacity adjustment factor (CAF) and free-flow speed adjustment factor (SAF), respectively. These factors are used in FREEVAL-RL to model weather event Figure A.16. Demand weekday-month worksheet in the FSG. Figure A.17. Weather worksheet in the FSG.
133 impacts on facility operations. The CAF and SAF tables are currently filled with national default values in the HCM2010, but these values can be overridden by the user. The last row in the lower table enables the specification of a DAF for each weather category. There are no default values here, but local conditions may provide guidance as to the level of demand adjustments associated with the more severe weather condi- tions. For example, the analyst may code a DAF less than 1.0 for a heavy snow event to reflect the fact that many drivers may avoid travel during those severe-weather events. Step 3D. Incident Probability Worksheet The incident probability worksheet characterizes incident events in terms of probability of occurrence, duration, and severity on the freeway facility. The worksheet is divided into two main sections. The first section, Option A, pertains to those cases for which incident logs are not available or are of insufficient quality to enable direct calculation and entry of incident probabilities. The second section, Option B, allows the user to code facility-specific incident probability data if available. Conceptually, both options result in a table of inci- dent probabilities by incident type and by month of the year. But while Option B requires a data-rich environment with detailed records of incidents, Option A is available for any facility, including ones with no incident data at all. option a: Data-poor inCiDent proBaBility eStimation The intent of Option A is to estimate incident probabilities for a facility with little or no incident field data available. Fig- ure A.18 shows the upper portion of the Option A section of the incident worksheet; the lower portion is identical with Option B, which is discussed next. Option A is used when the analyst has little or no incident data. In order to estimate these incident probabilities, three different paths are available. The first path is to determine if incident rates (per 100 million vehicle miles traveled [VMT]) are available for the facility. If so, then those rates can be entered on a monthly basis. Allowance is made to enable the user to vary incident rates per month, if such information is available. If not, the second path is to use monthly crash rates for the facility, which are easier to col- lect, and then use an estimated local incident-to-crash ratio to estimate monthly incident rates. A default factor of the ratio of incidents to crash rates is provided, but it can be overridden by the user. Figure A.18. First (upper) portion of Option A section of the incident worksheet.
134 If neither crash nor incident rate data are available, then a third path is to generate crash rates using the Highway Eco- nomic Requirements System (HERS) model. This option is available by clicking the Calculate Crash Rate Based on HERS Model button shown at the bottom of Figure A.18. If the HERS model is used to generate the crash rates, the user must first provide input on the portion of the AADT that occurs in the typical study period. This input is readily calculated from a known distribution of hourly factors. This worksheet is designed in a flexible way that adjusts the probability estimation process so that it is compatible with the userâs available data. When a path is selected (assume using the HERS model), then the FSG changes the back- ground colors of the appropriate cells so that the user can identify the cells that need data entry. Figure A.19 presents the remaining input data portion of Option A in the incident worksheet. The distribution of different incident types along with their mean duration and standard deviation are entered as shown in Figure A.19. Note that the sum of the percentages should add up to 100%. If the distribution of incident types is not available, the user can select national default values in the fourth table by clicking Insert National Default Data. The temporal probabilities of different incident types are then automatically generated by clicking the Calculate Inci- dent Probabilities button at the bottom of the Option A sec- tion of the incident worksheet. The resulting tables are shown in Figure A.20. option B: DireCt Data entry for Data-riCh faCilitieS As an alternative to estimating incident probabilities, the user can directly enter the monthly incident probabilities by type in the table shown in Figure A.20 (the Option B section of the incident worksheet). If no field data are available, then Option A above should be used to complete this table. The first table (upper) in Figure A.20 contains month-by- month incident probabilities for six incident types (no inci- dent, shoulder closure, one-lane closure, two-lane closure, three-lane closure, and four-lane closure). Clearly, not all incident configurations are feasible for all facilities, and some may automatically be ignored (e.g., four-lane closures on a three-lane-per-direction facility). In that case, the FSG auto- matically reallocates the specified probability to the next category of lower severity. Note that the FREEVAL-RL tool does not allow a full facility closure. The bottom table in Figure A.20 enables entries for incident- specific CAFs per open lane of the freeway. This entry should be determined for different incident types and based on the number of lanes available on the facility. A note of caution: the CAFs shown in Figure A.20 include the frictional effect of the incident impact only. They do not account for the capacity loss due to lane closure. For example, with a single- lane closure on a two-lane segment, the total capacity avail- able would be 0.50 Ã 0.7 = 35% of the initial segment capacity. Users should also enter the SAF and the expected DAFs based on local conditions. There are no national default values for either parameter at this time. In the CAF, SAF, and DAF tables, all unnecessary cells are blacked out. The user only needs to provide (optional) infor- mation for cells with a yellow background. By completing the steps in the incident worksheet, all the necessary information has been entered to produce the sce- narios. For this purpose, the user is directed to the Detailed Scenario worksheet to generate the scenarios. Step 3E. Detailed Scenario Worksheet In the final step, the various input data from the previous steps are used to generate analysis scenarios. A scenario is a Figure A.19. Second (lower) portion of Option A section in the incident worksheet.
135 unique combination of demand, weather, and incident char- acteristics that is applied as matrices of DAFs, free-flow SAFs, and CAFs to the seed file data. As a general rule, the number of scenarios generated depends on a variety of factors, includ- ing the number of demand patterns selected, the diversity in weather activities, and the maximum number of lanes on any segment of the facility. Because incidents, weather, and demand are taken to be independent events, the number of total possible scenarios typically will be in the thousands. To economize on run time, some very unlikely (low- probability) scenarios can be eliminated from consideration before running the core computational model. In this case, the user should specify a percentage threshold for filtering such scenarios. This process can be repeated by varying the threshold value and observing the trade-off between the resulting number of scenarios and the fraction of the distri- bution coverage (upper-right button in Figure A.21). Once a threshold is selected, clicking Step 5A: Generate/Update Sce- narios will generate the scenarios, and the summary table will be populated with the summary information of the generated scenarios. Figure A.21 shows the schematic of the detailed scenarios worksheet in the FSG. Figure A.20. Option B section of the incident worksheet.
136 As a last step, the adjustment factor file for FREEVAL-RL is invoked by clicking Step 5B: Generate FREEVAL Input file and Exit. The FSG then generates the FREEVAL input (adjustment) file, exits the FSG, and returns the user to the FREEVAL-RL main menu. At this point, the FSG will generate a file that contains tables with DAFs, SAFs, CAFs, and the number of lanes for each segment in each analysis time period. For a large facil- ity (70 segments) with a long study period (6 h, i.e., 24 time periods), each of these tables will be in the form of a 70 Ã 24 matrix. A different set of tables is generated for each of what could be thousands of scenarios, which can result in a rela- tively large data file. Consequently, it may take a few minutes to complete this step. It is important to note that no actual freeway facility analy- sis runs have been performed at this time. The FSG output file now serves as the input file for the FREEVAL-RL batch run performed in the next step. The analyst should make any desired changes to the sce- nario file (e.g., modified demand, weather, incident inputs, or excluded scenarios) at this time, before running the much more time-consuming next step in FREEVAL-RL. Step 4. Run Scenarios When all scenarios have been generated using the FSG tool, its output is automatically transferred to FREEVAL-RL. A green check mark will appear beside the Scenario Management button, and the Run Scenarios button will be enabled (see Fig- ure A.22). After clicking Run Scenarios, the tool will run the core computational engine in batch mode to execute all the scenarios generated by the FSG. Each scenario is analyzed, and its output is saved in a separate worksheet. When all the scenarios are processed, the FREEVAL-RL file is automatically saved as a Detailed Output file, and a Figure A.21. Detailed Scenario worksheet in the FSG. Figure A.22. Scenario Management step is finished and Run Scenarios step is active.
137 Summary Output file is saved, both in the same directory of the FREEVAL-RL. The Summary Output file consists of detailed information about each individual scenario and its respective output summary. When the runs are completed, five files will be created in the FREEVAL-RL directory as follows (suppose the default file name entered in the first step is âI-40-Projectâ): 1. I-40-Project_FREEVAL-RL.xlsm is the seed file and main menu access. 2. I-40-Project_Freeway Scenario Generator.xlsm is the sce- nario generator file associated with the seed file scenarios. (See Item 1.) 3. FREEVAL_input.xlsm is a file for internal use only; it is read by FREEVAL-RL to execute all the scenarios. It con- tains all data generated by the FSG. 4. I-40-Project_ComprehensiveOutput.xlsm contains detailed output of the last scenario (only) in FREEVAL-RL in addition to facility detailed output for each scenario and analysis period. The one-page summary report is also part of this file, which is described in more detail below. This file may require a larger amount of space than other files. 5. I-40-Project_SummaryOutput.xlsm is the summary out- put file, which has only one worksheet with all run out- puts in tabular format. Refer to the table titled Summary Output (Matrix) Description at the end of Appendix A for more information on the variables contained in this file. Step 5. View Results In the last step of the procedure, the user may view the sum- mary output file by clicking View Summary Output or view a condensed one-page summary report by clicking View Summary Report (see Figure A.23). A listing of the variables contained in the Summary Output is provided in the table at the end of Appendix A. A screenshot of a summary report is shown in Figure A.24, and a more detailed description of the various sections is available below. In general, all statistics in the summary report are weighted by the VMT of traffic served in the respective scenarios. In other words, the estimates of travel time and other perfor- mance measures are weighted by the amount of traffic served in each scenario. A scenario with more demand will therefore weigh more heavily in the overall results than a low-demand scenario. The motivation for weighing all results by the VMT is to approximate the performance as experienced by the traveler. For example, the VMT-weighted distribution of the travel time index (TTI) reflects the distribution as observed by drivers, with scenarios that affect more drivers (higher VMT) contributing more probability than low-VMT scenarios. These results are therefore different from the results that would be obtained from simply calculating an arithmetic average of the TTI across all scenarios. The header of the output report is automatically populated with âReliability Analysis Summary Report forâ and then the name of the facility as entered by the user. The facility descrip- tion block of the output report gives basic information about the length of the facility, the number of scenarios, and the number of scenarios with weather or incidents, or both. Fig- ure A.25 shows the next block containing the overall reliabil- ity statistics for the facility. The basic performance measures contained in Figure A.25 are as follows: Mean TTI: The average, VMT-weighted TTI on the facility. PTI: The planning time index for the facility, which is defined as the 95th percentile of the VMT-weighted cumulative TTI distribution. This measure is useful for estimating how much extra time travelers must budget to ensure an on- time arrival and for describing near-worst-case conditions on urban facilities. 80th percentile TTI: This facility performance measure is also VMT weighted. This measure has been found to be more sensitive to operational changes than the PTI, which makes it useful for comparison and prioritization purposes. Misery index: This measure is defined as the average of the highest 5% of the TTI distribution divided by the free-flow facility travel time. This measure is useful as a descriptor of near-worst-case conditions on rural facilities. Standard Deviation: This measure is the standard deviation of the VMT-weighted TTI distribution. Semi-Standard Deviation: This measure is a one-sided stan- dard deviation, with the reference point at free-flow speed instead of the mean. It provides the variability distance from free-flow conditions. Percent of VMT at TTI > 1.33: This measure is a failure crite- rion that approximates the approximate number of trips at a speed 33% lower than the free-flow speed, which approx- imately coincides with the speed at capacity for freeways with a 70-mph free-flow speed. Figure A.23. View results buttons.
138 Figure A.24. One-page summary report. 4 80 1.0 192 334 54 1.25 Standard Deviation 5.10 1.77 5.58 1.32 51.73% 3.30 25.97% 1.2 3.48 1.8 13.2 2.9284% 0.0004% 97.07% 100.00% 2.6% 97.4% 100.00% 96.28% Facility Length (miles) Number of Incident Scenarios Reliability Analysis Summary Report for Sample Facility Facility Description Number of Segments Number of Weather Scenarios Number of Total Scenarios Numb. of Incident + Weather Scen. Facility Reliability Performance Measures Mean TTI PTI Semi-Standard Deviation 80th percentile TTI Percent of VMT at TTI > 1.33 Misery index Percent of VMT at TTI > 2 Probability Distribution Function Cumulative Distribution Function Percent Contribution to Total Vehicular Hours of Delay (VHD) O ve ra ll V HD Di st rib uti on N et In cr em en ta l VH D Di st rib uti on Analysis Details for Reliability Reporting Period Recurring Congestion (Demand Only) Non-Recurring Congestion Only Maximum d/c Ratio Maximum d/c Ratio Maximum TTI Maximum TTI Total % Unserved VMT in RRP Total % Unserved VMT in RRP % Time with Queuing % Time with Queues % Time without Queues % Time without Queues Percent of VMT in Recurring-Only Percent of VMT in All Periods 0% 10% 20% 30% 40% 50% 60% 1 3 5 7 9 11 13 15 17 19 21 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 1. 5 2 2. 5 3 3. 5 4 4. 5 Demand Only Weather O nly Incident Only Both Demand Only Weather O nly Incident Only
139 Percent of VMT at TTI > 2: This failure criterion approximates the approximate number of trips at a travel time twice the free-flow travel time, which coincides with an average speed of half the free-flow speed. In addition to the statistics shown in Figure A.25, a series of output graphs for the facility reliability analysis are avail- able (see Figure A.26). These graphs show the probability den- sity function and cumulative distribution function of the facility TTI, with both distributions being VMT weighted. The cumulative distribution function further highlights the 80th and 95th percentile TTIs. Figure A.26 also shows the percentage distribution of delay by the various sources of congestion using two aggregation methods. The left chart shows the overall distribution of vehi- cle hours of delay (VHD) across all scenarios. The chart is a VMT-weighted breakdown of congestion sources for the total delay on the facility. It should be noted that the weather and incident VHD estimates in this case include some delay that would have occurred from demand impacts within the weather and incident scenarios. The chart on the right is an alternative way of showing the VHD distribution that isolates the incremental delay. Con- ceptually, the VHD for each incident and weather scenario is 1.25 Standard Deviation 5.10 1.77 5.58 1.32 51.73% 3.30 25.97% Facility Reliability Performance Measures Mean TTI PTI Semi-Standard Deviation 80th percentile TTI Percent of VMT at TTI > 1.33 Misery index Percent of VMT at TTI > 2 Figure A.25. Facility reliability statistics. Probability Distribution Function Cumulative Distribution Function Percent Contribution to Total Vehicular Hours of Delay (VHD) O ve ra llV HD Di st rib ut io n N et In cr em en ta l VH D Di st rib ut io n 0% 10% 20% 30% 40% 50% 60% 1 3 5 7 9 11 13 15 17 19 21 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 1. 5 2 2. 5 3 3. 5 4 4. 5 Demand Only Weather Only Incident Only Both Demand Only Weather Only Incident Only Figure A.26. Travel time distribution and output charts.
140 reduced by the amount of VHD that would have occurred from recurring sources of congestion (i.e., demand variability only). A final part of the output includes additional statistics that separate recurring (demand-only) and nonrecurring (weather and incidents) scenarios, as shown in Figure A.27. Figure A.27 shows the maximum demand-to-capacity (d/c) ratio and maximum TTI for each of the two groups of sce- narios. It also shows the amount of time with and without queuing. Finally, it shows the percentage of VMT that is repre- sented by the two groups and gives a sense of any unserved VMT in the RRP. In addition to creating this standard output report, the user may decide to perform customized calculations, and a separate summary output file is provided for that purpose. The table at the end of Appendix A contains the listing and definitions of all variables included in that output. MS Excel 2010 Security Options Quick Guide Macros facilitate many tasks for MS Excel users. Many are created with Visual Basic for Applications and are written by software developers. However, some macros pose a potential security threat. A person with malicious intent can introduce a destructive macro in a document or file that can spread a virus on computers. MS Excel does not enable macros automatically (i.e., by default). In the 2010 version, an option for enabling macros is provided in the welcome notification. Figure A.28 shows the button that enables macros in the MS Excel 2010 version. Please note that the FREEVAL-RL computational engine uses multiple macros embedded behind the user interface. The user must enable macros in order to run the computational engine. 1.2 3.48 1.8 13.2 2.9284% 0.0004% 97.07% 100.00% 2.6% 97.4% 100.00% 96.28% Analysis Details for Reliability Reporting Period Recurring Congestion (Demand Only) Non-Recurring Congestion Only Maximum d/c Ratio Maximum d/c Ratio Maximum TTI Maximum TTI Total % Unserved VMT in RRP Total % Unserved VMT in RRP % Time with Queuing % Time with Queues % Time without Queues % Time without Queues Percent of VMT in Recurring-Only Percent of VMT in All Periods Figure A.27. Output details for recurring and nonrecurring congestion. Figure A.28. Enabling macros before running FREEVAL-RL.
141 Reference Highway Capacity Manual 2010. Transportation Research Board of the National Academies, Washington, D.C., 2010. Summary Output (Matrix) description Entry Description Scenario Number Scenario Number Parent Scenario Number A scenario with normal weather, no incident with an identical demand pattern as the current scenario is called the Parent Scenario. For weather and incident scenarios, the weather only and incident only scenarios are called subparent scenarios. Each reliability scenario therefore has (only) one parent scenario. This attribute is useful when estimating additional delay due to weather and/or incidents relative to the demand-only parent scenario. Analysis Period Analysis period No. (varies from 1 to number of analysis periods in study period) Probability Probability of a scenario (from FSG) Demand Adjustment Factor A multiplicative factor of demand relative to the base scenario Weather Type Weather condition description in the scenario Weather Event Start Time Start time of the weather event (either start or middle of the study period) Weather Event Duration (min) Duration of the weather event in minutes Weather Event CAF Capacity adjustment factor due to the weather event Weather Event SAF Speed adjustment factor due to the weather event Incident? A Boolean value indicating the presence of an incident in the study period: 0 for no incident, 1 for incident Incident Start Time Start time of the incident (start or middle) Incident Duration (min) Duration of the incident in minutes Incident Segment Number Segment number where the incident occurs Segment Number of Lanes Total number of lanes on the incident segment Number of Closed Lanes Total number of lanes closed due to the incident Per Open Lane Incident CAF Capacity adjustment factor applied to each of the open lanes as due to the incident Incident SAF Speed adjustment factor of the incident (defaulted at 1.0) TTI Facility travel time index in the analysis period Max d/c Ratio Maximum demand-to-capacity ratio for all the segments in the analysis period Queue Length (ft) Queue length at the end of the analysis period Total Denied Entry Queue Length (ft) Queue length of vehicles unable to enter the facility at the first segment Total On-Ramp Queue Length Queue length of vehicles on on-ramps Average Travel Time per Vehicle (min) Average travel time experienced by each vehicle traveling the facility in the analysis period Free-flow Travel Time (min) Facility travel time experienced by each vehicle if it traveled at free-flow speed Freeway Mainline Delay (min) Delay experienced per vehicle. Calculated by subtracting free-flow travel time from average travel time per vehicle. System DelayâIncludes On-Ramp (min) Total delay of the analysis period is the summation of mainline delay and all on-ramp delays. VMTD Demand Vehicle miles traveled as if all demand had been served in the analysis period VMTV Volume Vehicle miles traveled of the vehicles actually served during the analysis period VHT travel/interval (h) Vehicle hours traveled by all served vehicles during the analysis period VHD delay/interval (h) Vehicle hours of delay experienced by all served vehicles during the analysis period Space mean speed = VMTV/VHT (mph) Space mean speed at the analysis period calculated by dividing served vehicles miles traveled by total vehicles hours of travel Facility Average Density (pc/mi/lane) Average density on the facility in passenger cars per mile per lane Density-Based Facility LOS Facility level of service based on the facility average density Demand-Based Facility LOS Facility level of service based on demand