Traffic Forecasting Accuracy Assessment Research (2020)

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III-D-1 Forecast Card Data Assumptions A P P E N D I X D Understandably, the organization, clarity, and detail of the available information varied across the historical datasets used to create the initial forecast accuracy database and the subsequent Forest Cards and Forest Card Data repository. In most cases, some assumptions were made. The assumptions noted in this appendix are specific by state and are explained in detail in each section. 1 Florida Florida DOT District 4 (D4) and District 5 (D5) data were provided in different formats. The D4 information was provided in an Excel (spreadsheet) format, and the D5 information was extracted from scanned PDF reports. There are in total 143 valid records in the D4 dataset. Table III-D-1 lists the important fields in the D4 dataset. The descriptions of the fields in Table III-D-1 are assumed based on the field name. Table III-D-1. Florida D4 field descriptions. CATEGORY FIELD DESCRIPTION GENERAL ROADWAY INFORMATION Segment/ Intersection Description of the segment or intersection where the project is carried out Roadway ID ID assigned to the roadway on which project is located County County in which project was located Roadway Segment Description of the roadway corresponding to Roadway ID DATE Date of Report Date when the report was completed Year of WP +10 (Interim) Interim/mid-design year Year of WP +10 AADT Forecast AADT in interim/mid-design year Design Year Design year Design-Year AADT Forecast AADT in design year FORECAST DATA Future Forecast Year (Opening Year) Opening year Future Forecast AADT (Opening Year) Forecast AADT in opening year COUNT STATION FDOT/County Count Station ID# ACTUAL DATA COUNTS Actual AADT Actual AADT in opening year

III-D-2 Traffic Forecasting Accuracy Assessment Research Some of the assumptions made for the Florida D4 dataset are as follows: 1. The date of the report is assumed to be same as the year when the forecast was completed. 2. In the final dataset (not pictured), the Report Finding column is used for details on the improvement type. Because this information was not available for every project, in the final dataset the Report Finding column is sometimes blank. 3. For all the projects, the forecasting agency is assumed to be the state DOT. 4. The NCHRP Method column is derived from the Method column in the Florida D4 dataset. The Method categories are reassigned into the NCHRP Method categories using the assumptions shown in Table III-D-2: Table III-D-2. Florida D4 method assumptions. Method Assumptions NCHRP Method Palm Beach Social Economic Data Population Growth Rates CGR Compound Growth Rate Traffic Count Trend LGR Linear Growth Rate Traffic Count Trend Cost Feasible Model Project-Specific Travel Model GR Growth Rate Traffic Count Trend EGR Traffic Count Trend Historical AADT Traffic Count Trend SERPM Regional Travel Model Regional Travel Model CGR (Average) Historical AADT Historical AADT+SERPM Traffic Count Trend + Regional Travel Model Model SERPM Regional Travel Model Linear Interpolation Traffic Count Trend CGR Historical Data (2005–2010) Declining Trend Analysis BEBER Population Forecast (0.5%) Traffic Count Trend + Population Growth Rates CGR/4 Methods Traffic Count Trend CGR Master Plan Traffic Count Trend Interpolation Assumed to be Linear Interpolation Traffic Count Trend Linear GR Assumed to be Linear GR (typo error) Traffic Count Trend No Report, No CGR, No K or D Factors, and no TMS. Just 18 kip. Professional Judgment GR and TAZ Mostly TAZ refers to Socioeconomic Growth Traffic Count Trend + Population Growth Rates Note: CGR = Compound Growth Rate; LGR = Linear Growth Rate; GR = Growth Rate; EGR = Exponential Growth Rate; SERPM = Southeast Florida Regional Planning Model; TAZ = Traffic Analysis Zone.

Appendix D: Forecast Card Data Assumptions III-D-3 2 Michigan The Michigan dataset was provided by the Michigan DOT in the form of both PDF reports and an Excel table. Whenever a mismatch occurred between the information in the reports and the Excel table, the information in the reports was considered legitimate. There are in total 10 records in this dataset. Table III-D-3 provides a list of important variables in the dataset. Table III-D-3. Michigan field descriptions. FIELD DESCRIPTION TAR Number Report number—specific to a project Location Details on where the project was located Urban/Rural Area type NFC Length Length of the segment Facility Name Name of the facility for which forecasts were developed Facility Type Type of the facility for which forecasts were developed (based on the categories in the forecast accuracy database) Improvement Type Type of the improvement proposed in the project. (if numeric, then it is based on the categories in the forecast accuracy database) Forecaster Agency who was responsible for forecasting (based on the categories in the forecast accuracy database) Forecaster Description Name of the person who created the forecast Methodology Method used for forecasting the AADT Post-Processing or Alternative Methodology Post-Processing or alternative method used for the forecast (based on the categories in the forecast accuracy database) Year Forecast Produced Year when the forecast was completed Base Year Base year for the forecast Forecast Year Forecast year in comma format Forecast Year Type Forecast year type respective to the Forecast Year column in comma format Forecast Units Units of final forecasts (based on the categories in the forecast accuracy database) Year of Observation Year for which the actual AADT was reported Traffic Count Reported actual AADT Count Station iD Count Station ID for which the actual AADT is reported Count Units Units of actual traffic counts Source More information on the source of the actual count Some of the assumptions made in the Michigan dataset are as follows: 1. The project years for TAR# 2293 and TAR#2573 in the Excel table were not matching with those in the reports. The project years in the reports were given preference. 2. With regard to the Improvement Types, bridge replacement, bridge repair, pavement design, crack treatment, and guard rail replacement have been considered in the Resurfacing and Minor Improvement category.

III-D-4 Traffic Forecasting Accuracy Assessment Research 3. For TAR# 2293, multiple forecasts exist for the same segment. The results from the latest forecast (Report Year 2013) have been uploaded into the NCHRP database. 4. In the database, “Horizon Year” is assumed to be referring to the Forecast Year for 10+ years. 5. If the Forecast Year Type has only one value, then the second year is assigned to a subsequent Forecast Year Type. For example, if the forecast years are “2013, 2018” and Forecast Year Type is mentioned as “1,” then 2013 is assumed to be the Opening Year (Type Code 1) and 2018 is assumed to be the Interim Year (Type Code 2). 3 Minnesota The Minnesota dataset was gathered from previous studies (Parthasarathi and Levinson 2010) in the form of an Excel table. The raw data is available at the Minnesota Historical Society Archives and is in the form of scanned PDF reports produced by the Minnesota DOT. Count maps used to get the actual AADT information also are provided in scanned PDF format. In this dataset, the dates of collection of the data fell between 2007–2009. Forecasts extending beyond this timeframe are not included in the dataset. There are in total 1,583 valid records. Table III-D-4 provides a list of important variables in the dataset. Table III-D-4. Minnesota field descriptions. FIELD DESCRIPTION id: Project identification number, generated for analysis purposes reportno: Report number provided by the Minnesota DOT reportdesc: Project description, typically obtained from the Minnesota DOT project report forecastdate: Date of the project report reportyear: Year in which project report was completed forecastyear: Year for which forecasts were created noofyrs: Number of years between the year in which the report was prepared and the forecast year projectstatus: Status indicating if the forecasts were for an existing roadway or new roadway highway: Highway for which forecasts were developed from: Segment start location to: Segment end location direction: Direction of the forecasts (Categories: EB - Eastbound, WB - Westbound, NB -Northbound, SB - Southbound) funclass: Roadways classified using the categorization in the Year 2000 Twin Cities Regional Travel Demand Model (Categories Freeway, Expressway, Divided Arterial, Undivided Arterial, Collector) highwaytype: Roadway type classified based on the type of access provided to downtowns of Minneapolis and St. Paul (Categories: Radial, Lateral) seglengthmi: Length of the segment in miles; the start and end locations define the segment segmentcity: City where the roadway segment is located; the start and end location define Segment segmentcounty: County where the roadway segment is located; the start and end location define Segment segmentdirection: Roadway direction with respect to the central cities of Minneapolis and St. Paul (Categories: East, West, North, South, Northeast, Northwest, Southeast, Southwest, Middle, Middle North, Middle South) forecastadt: Forecast average daily traffic (ADT), provided as part of the project actualadt: Actual ADT obtained for the segment, obtained from the Minnesota DOT projstat: Project status at the time of report preparation (Categories: Existing Facility, New Facility)

Appendix D: Forecast Card Data Assumptions III-D-5 Some of the assumptions made in the Minnesota dataset are as follows: 1. Records with no or zero value for the Forecast AADT were removed from the dataset. 2. Some 99% of the forecasts were produced by the state DOT, and 1% of the forecasts were produced by a consultant under contract with the state DOT. Therefore, it has been assumed that the forecasting agency for all records is the state DOT. 3. The blank cells for the variables Highway, From, To, and Seglengthmi do not necessarily mean a missing value. In most of these cases, the information is entered only in one direction. In such cases, the information in the prior record should be assumed. For example, in Figure III-D-1, the second row refers to the same segment as the first row (i.e., proposed new alignments from CR 30 to Scandia Rd.) but the traffic direction represented in the first row is Eastbound (EB) and the traffic direction represented in the second row is Westbound (WB). Figure III-D-1. Example: Missing segment values, Minnesota dataset. 4. Not much information is available in the reports for Forecast Methodology (forecast method). Since these are old forecasts, it is assumed that the forecast was made using Traffic Count Trend. 5. Records with no actualadt value means there were no available AADT counts for that year on that segment. Missing traffic count information wherever possible was added using the Count maps. 6. It is also unclear to which forecast year (Opening Year, Mid-Design Year, or Design Year) the forecast belongs. 7. If no county information is available, then the county information was found from Google Maps using the segment description. 8. Some of the records list Blaine as a county. The city of Blaine sits on the dividing line between two counties: Anoka County and Ramsey County; accordingly, these records have been reassigned to Anoka County or Ramsey County based on the location of the project. 9. When two counties are mentioned in the segmentcounty column, then only the first county has been entered in the forecast accuracy database. 4 Wisconsin The Wisconsin DOT provided the project information in an Excel table. In the database, the worksheet “Data” in the Accuracy_Sites_For_Submittal_FINAL.xls is used for the upload process. There are in total 457 valid records. Table III-D-5 lists the field descriptions for this dataset.

III-D-6 Traffic Forecasting Accuracy Assessment Research Table III-D-5. Wisconsin field descriptions. FIELD DESCRIPTION Control Number Internal ID number for Traffic Forecasting Section use Match Year The first Interim Forecast Year that matches with year (post-project) of traffic count Last Count Year Year of count based on which the forecast was based Time Period (Yrs.) Number of years between Match Year and Last Count Year Report Completed Year of forecast completion County County in which the project was located Forecaster Staff member who developed the traffic forecast Functional Class Legacy FHWA functional classifications of subject project/forecast roadways: * 1 = Rural Interstate 2 = Rural Principal Arterial 6 = Rural Minor Arterial 7 = Rural Major Collector 8 = Rural Minor Collector 9 = Rural Local 11 = Urban Interstate 12 = Urban Freeway/Expressway 14 = Urban Principal Arterial 16 = Urban Minor Arterial 17 = Urban Collector 19 = Urban Local Actual Volume Observed AADT in match year Forecast Volume Forecast AADT in match year. % Difference Actual/forecast difference ratio GEH Geoffrey E. Havers statistic (utilizing forecast volume and actual volume) Model Area? Yes/no flag indicating availability of a Travel Demand Model in subject project/forecast area Difference Percentage difference between actual and forecast volume Abs. Difference Absolute value of Difference Last Count Observed AADT in Last Count Year AGR Forecast Annual Growth Rate Site ID WisDOT [Wisconsin DOT] master traffic count database ID number of the subject traffic count / forecast site Actual AGR Actual annual growth rate (AGR) Diff. AGR Actual AGR - Forecast AGR Pos. Growth Yes/No flag indicating positive growth of Actual AGR * Table III-D-5 lists only those FHWA functional classifications that are used in the database. Before the data were uploaded into the final database, some changes were made to the data. 1. Wisconsin traffic studies distinguish forecast years into: 1st Interim Year, 2nd Interim Year and Design Year. Usually, the 1st Interim Year is either the year the facility opens to traffic or

Appendix D: Forecast Card Data Assumptions III-D-7 4. For built projects, the assumed method for forecasting is using the Regional Travel Demand Model only. In other cases, it is a combination of the Regional Travel Model and the Regression Model, which is considered in the traffic count trend method. 5. In the Wisconsin dataset, “Functional Class” entries have been reclassified into the NCHRP Functional Categories (see Table III-D-6). Records with no Functional Class code have been kept blank in the forecast accuracy database. Urban collectors are categorized as major collectors in the main database. Table III-D-6. Functional class classification assumptions used with Wisconsin dataset. Wisconsin Functional Class NCHRP Code NCHRP Description Rural Interstate System 1 Interstate or Limited-Access Facility Rural Principal Arterial 3 Principal Arterial Rural Minor Arterial 4 Minor Arterial Rural Major Collector 5 Major Collector Rural Minor Collector 6 Minor Collector Urban Interstate 1 Interstate or Limited-Access Facility Urban Freeway/Expressway 1 Interstate or Limited-Access Facility Urban Principal Arterial 3 Principal Arterial Urban Minor Arterial 4 Minor Arterial Urban Collector 5 Major Collector Urban Local 7 Local 6. In the forecast accuracy dataset, the Area Type (Urban or Rural) is decided based on the functional classification information. Functional Classes 1 to 9 are Rural and the rest are assigned as Urban. 7. A typographical error occurred in the county name for “Forest” county, which was fixed in the forecast accuracy dataset. 8. Toll type is decided based on the facility type. The category “Unknown” has been assigned for the freeways/expressways functional class. the construction year. For the forecast accuracy database, the 1st Interim Year is represented by the Match Year and is assumed to be the opening year. 2. The “Report Completed” field is assumed to be the year when the forecast was completed and not when the report was completed/submitted. 3. It is assumed that all the forecasters were state DOT members/employees.