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47 Table 8.1. Daily trip rates used in factoring truck trips. SIC Description Trips/Employee 1-9 Agriculture, Forestry, and Fishing 0.5 10-14 Mining 0.5 15-19 Construction 0.5 20-39 Manufacturing, Total 0.322 40-49 Transportation, Communication, and Public Utilities 0.322 42 Trucking and Warehousing 0.7 50-51 Wholesale Trade 0.17 52-59 Retail Trade 0.087 60-67 Finance, Insurance, and Real Estate, Total 0.027 70-89 Services 0.027 80 Health Services (Including State and Local Government 0.03 Hospitals) N/A Government 0.027 Flow Unit and Time Period Conversion area corridors, the annual and total rates of internal truck growth, and the resulting 2020 truck projections. Existing truck volumes are directly forecast so no flow unit or temporal conversions were necessary. Performance Measures and Evaluation Assignment Performance measures were not developed for the TH 10 model. No assignment component is included in this model class. The existing truck flows on the TH-10 were directly factored. 8.3 Case Study The Heavy Truck Freight Model for Florida Ports Model Validation Background Trip Generation Context Not applicable. Ports are usually considered special generators of truck traffic in transportation planning models, in that they do not Trip Distribution produce or attract truck trips proportionate to the employ- Not applicable. ment or other socioeconomic variables at the port. Instead they generate truck traffic proportionate to the shipment of freight traffic through the port, which typically originates or Mode Choice terminates at an unspecified international location. It is Not applicable. important to accurately forecast the volume of truck traffic generated by port activity in order to forecast the volume of traffic on surrounding roadways, since truck traffic around Modal Assignment ports is normally 10% to 50% higher than on roadways of Not applicable. similar functional classification located in other areas. This additional traffic can be directly attributed to the operations of the port. Model Application The Florida Department of Transportation sponsored a The TH 10 Truck Trip Forecasting Model was developed series of research projects by the University of Central Florida to assess current and future truck travel demand in the corri- whose goal was to provide planners with a tool for develop- dor and was directly used for that purpose. Table 8.2 shows ing forecasts of freight traffic in the vicinity of Florida's major the annual and total rates of employment growth along study seaports, including Miami, Tampa, Jacksonville, and Port

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48 Table 8.2. Results of Truck Highway 10 forecast daily trucks. Employment Internal Truck Growth Growth 2020 Projections Location 2000-2020 2000-2020 Based On From To County Annual Total Annual Total 1999 1995a MN25 MN24 (Becker) Sherburne 1.70% 39% 1.30% 30% 866 1,165 MN25 (Becker) MN25 (Big Lake) Sherburne 1.70% 39% 1.30% 30% 862 1,350 MN25 (Big Lake) CR 14/15 Sherburne 1.70% 39% 1.30% 30% 902 1,462 CR 14/15 TH169 Sherburne 1.70% 39% 1.30% 30% 1,022 1,940 TH169 MN47 Sherburne/ 1.7% 39% 1.3% 30% 1,560 1,726 Anoka 0.80% 18% 0.40% 8% MN47 TH610 Anoka 0.80% 18% 0.40% 8% 3,019 2,763 TH610 MN65 Anoka 0.80% 18% 0.40% 8% 2,409 MN65 I35 Ramsey 0.40% 8% 0.40% 8% 1,979 I35 I694 Ramsey 0.40% 8% 0.40% 8% 1,610 Note: Gray indicates old roadway alignment. a Assumes 2000 traffic rebounds to 1995 traffic, then continues to grow. Everglades. The project was divided into three phases, and the berthing, loading, and unloading activities occur seven days first primarily focused on the Port of Miami.17 This case study a week. Significant cargo vessel activity occurs between Fri- describes the methods used in this first phase as completed in day evenings and Monday mornings. 1999. The Port of Miami, shown in Figure 8.2, is one of the largest container cargo ports in the United States. It is Objective and Purpose of the Model the largest freight port in Florida in terms of revenue and the The objectives of the Heavy Truck Freight Model for third largest in terms of tonnage. Miami's freight operations Florida Ports were as follows: are heavily influenced by the rapidly growing economies of the Caribbean and Latin American nations. To develop modeling systems for predicting truck traffic As shown in Table 8.3, truck movement at the Port of volumes; Miami takes place primarily on weekdays, peaking at any To estimate both inbound and outbound heavy truck trips; time between 9:30 a.m. and 3:30 p.m. However, vessel To use an alternative approach to estimate trips generated at ports, rather than the traditional land use approach that utilizes demographic and economic data; and To relate the volume models to the gross tonnage of truck movement. General Approach Model Class The Heavy Truck Freight Model for Florida Ports is a direct facility flow factoring class of model. Flow factoring involves simple methods intended to apply existing data to determine near future freight volumes. The research project developed equations using linear and ARIMA regressions of time series data to produce forecasts of future year truck vol- Source: Port of Miami web site, umes. The Heavy Truck Freight Model was originally devel- Figure 8.2. The Port of Miami. oped to estimate the truck trips produced from and attracted

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49 Table 8.3. Distribution of truck movements (January 1996 through July 1996). Day Total Percentage Monday 40,173 18.0% Tuesday 40,729 18.3% Wednesday 43,484 19.5% Thursday 45,585 20.5% Friday 50,844 22.8% Saturday 1,413 0.6% Sunday 581 0.3% Total 222,809 100.0% to the Port of Miami. A detailed description of the model is axles, configurations). These data were obtained by inter- provided in Sections 4.1 and 6.1. viewing local port personnel familiar with the many aspects of overall operation: personnel from administration, field Modes operations, shipping companies, private terminals, trucking companies, security, accounting, and marketing. The Heavy Truck Freight Model estimates the cargo truck The team entered the data into an electronic database and traffic moving inbound and outbound at the Port of Miami. prioritized the sources according to quality, availability, and It is restricted to container and trailer truck configurations compatibility with the purposes and intent of the model. The that transport virtually all of the port's freight. objective was to develop a model with a minimum of inputs that used routine data collection methods. Table 8.4 summa- Markets rizes the various types of data collected during this project. The geographic limit of the model is the street network in Terminal Company's Truck Data. Four terminal oper- Downtown Miami. The model estimates daily volumes of ating companies collected all the heavy truck gate movements large inbound and outbound container and trailer trucks for at the port. Some of the data were not separated by inbound specified timeframes. and outbound movements. Since inbound and outbound traffic is modeled separately, these data were not suitable for Framework developing the model, but were used in a general overview. The Heavy Truck Freight Model is a port-generated cargo Gate Pass Data. Since the terminal company truck data truck estimation model. It does not include any other freight were not broken down to hourly bi-directional data, data was modes, and it is not part of a larger freight or passenger needed from other sources that recorded entry and exit times. demand model. However, because ports often are considered The Port of Miami collects and stores gate pass cards that special generators, the model can be used to estimate the pro- record entering and exiting times of trucks, general vehicle duction and attraction of truck trips from the port for inclu- configurations, the terminal operating companies visited, sion as a part of a statewide or regional model. and the inbound gross weights of the vehicles. Gate pass data provided hourly volumes. Flow Units Videotape Counts. Port Boulevard traffic was video- The model starts with the monthly imported/exported taped on three days in 1997 (Friday, October 31, Monday, freight units, and finally estimates the hourly volume of total November 3, and Thursday, November 6). The correspon- trucks. ding truck gate passes maintained by Port Security for the selected days were counted to ensure the reliability of gate Data passes as a substitute data source for traffic counting. Forecasting Data Vessel Movements. Vessel movements data were col- The University of Central Florida team first collected sam- lected along with the truck data from the gate passes and the ple truck traffic volumes by classification (type, number of terminal companies. Detailed records of vessel berthing for

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50 Table 8.4. Summary of data collected. Source of Data Resolution Period Terminal Company Gate Movements Daily Truck Movements January 1996-December 1997 Port of Miami Gate Passes Individual Truck Movements January 1997-May 1997a Video Counts Individual Truck Movements October 31, November 3, and November 6, 1997 Gantry Crane Activities Start Time and End Time January 1996-December 1997 Dock Reports Individual Vessel Arrival and January 1996-December 1997 Departure Times Trailer/Container Reports Daily Trailer/Container Totals January 1996-December 1997 Monthly Performance Reports Monthly Trailer/Container Totals October 1978-April 1998 a Only 57 days were collected. 1996 and 1997 were obtained from the daily dock reports, Statistical Monthly Trailer/Container Performance which include the entry and exit times and dates and various Reports. Monthly trailer/container performance reports other data associated with berthing. were obtained for the period October 1978 through April 1998. These data include monthly activity summaries and can Gantry Crane Activities. Gantry crane data for 1996 and be useful for determining historical trends in the trip gener- 1997 were also collected. Detailed records of crane activities ation model input for long-term forecasts. were extracted from the gantry crane activity by ship line reports maintained by the port. These data include the start time and end time of service for each vessel. Model Networks Trailer/Container Activity Report. Trailer/container A layout of the external road network surrounding the Port reports for the first six months of 1997 were obtained from of Miami is shown in Figure 8.3. This small region covers an the Port Accounting Office. These data include the number area about one mile to the west of the port and is located of freight units (trailers and containers) moved on and off within the central business district of Miami. The network each vessel. covers the following roads: Figure 8.3. Street network in the Port of Miami region.

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51 1. Biscayne Boulevard northbound and southbound, between port (the trip attraction model), while outbound refers the Port Boulevard entrance and exit. to truck trips leaving the port (the trip production 2. NE 5th Street between Biscayne Boulevard and NE 2nd model); Avenue. This is a one-way, eastbound roadway. 6. Validate the model by entering survey data not used 3. NE 6th Street between Biscayne Boulevard and NE 2nd during the model formulation process; Avenue. This is a one-way, westbound roadway. 7. Estimate gross weight of heavy truck movement gener- 4. NE 2nd Avenue between NE 6th Street and NE 5th Street. ated on Port Boulevard by applying regression model(s) This is a one-way, southbound roadway. with the monthly gross weight of cargo as the dependent variable and the cargo vessel freight unit volume; 8. Perform a time series analysis to examine long-term and Model Development Data seasonal trends applying the analysis to the monthly The project team experimented with various types of data totals of the main independent variable, cargo vessel to develop the model, ultimately determining that the daily freight unit volume (containers and trailers); number of freight units (containers and trailers) handled by 9. Determine hourly distribution of truck movements from the Port of Miami was the best-fit independent variable. gate pass data; and 10. Interpret the results to establish conclusions and make recommendations for future analysis. Conversion Data The model produces total daily heavy trucks using the total Software freight units. No specific modeling or planning software was applied to develop this model. Standard statistical software was used to Validation Data develop the regression equations and the ARIMA models. The model was validated using 29% of the total available observations. The remaining 71% were used for developing Commodity Groups/Truck Types the model. The model validation statistics are shown in the model validation section. The Heavy Truck Freight Model estimates total freight trucks. It does not segregate by commodity group or by purpose. Model Development The following methodology was used to develop truck trip Trip Generation generation model(s) for the Port of Miami: The University of Central Florida research team used a 1. Collect sample truck traffic volumes by classification process similar to trip generation to develop the factors and (type, number of axles, configurations); forecast variables in the model. The research team used dif- 2. Interview local port personnel familiar with the many ferent equations and data to estimate inbound and outbound aspects of the overall operation, including personnel traffic. Since the Port of Miami has a higher percentage of from administration, field operations, shipping compa- exports than imports, it was essential to distinguish between nies, private terminals, trucking companies, security, the inbound and outbound directions and apply the two accounting, and marketing; components accordingly. 3. Enter data samples into an electronic database, prioritiz- The Heavy Truck Freight Model predicts the daily volumes ing the sources according to quality, availability, and fea- of large inbound and outbound truck trips. As shown in sibility, with the objective of developing a model with equations 1 and 2, the inbound truck model component pre- minimum input and routine collection practices; dicts truck trips attracted to the port while the outbound 4. Determine the independent variables for formulating model component predicts truck trip produced by the port models to correlate the volume of freight truck move- activities. The dependent variables are the daily inbound and ment with internal port activity, focusing on Port Boule- outbound loaded truck volumes, and the independent vari- vard, the only road available for port access; ables are the total number of exported and imported freight 5. Develop the trip generation model by applying regres- units. sion analysis, with Port Boulevard's daily directional The team also developed equations for forecasting future truck volumes inbound and outbound as the depend- year inbound and outbound freight units, which are required ent variables. Inbound refers to truck trips entering the to estimate future year truck trips. The team developed two

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52 time series models, as shown in equations 3 and 4, and two Table 8.5. Inbound loaded freight trucks regression models, as shown in equations 5 and 6. regression model statistics. INTK 1.197 * (EXPFU) (1) OUTK = 310.079 + 0.698 * (INPFU) (2) Summary Statistics Ln (IMPFUm) = 0.0135 + Ln (IMPFUm-1) Regression Statistics - 0.218 (Ln(IMPFUm-9) - Ln (IMPFUm-10)) (3) Multiple R 0.8855865 Ln (EMPFUm) = 0.01275 + Ln (EMPFUm-1) R Square 0.7842635 - 0.18 (Ln(EMPFUm-9) - Ln (EMPFUm-10)) (4) Adjusted R Square 0.7316319 IMPFU = Exp (8.771 + 0.009506 (Month Index)) (5) Standard Error 303.59594 EMPFU = Exp (8.767 + 0.00885 (Month Index)) (6) SSE/Mean 0.2392403 Observation 20 where: INTK = Inbound loaded freight truck volume; OUTK = Outbound loaded freight truck volume; IMPFU = Total imported freight unit; These two models are adequate to represent the relation- EXPFU = Total exported freight unit; ship between the number of loaded truck movements and the Month Index = 1, 2, 3, 4, 5, etc.; and number of freight units. m = current month. To validate the Heavy Truck Freight Model, the team used a total of 20 observations (71% of the total available observa- tions) to fit the regression component and eight observations Trip Distribution (29% of the total available observations) to validate the com- The model does not include a trip distribution step. pleted model. The team used a paired t-test to compare the total number of loaded freight trucks predicted by the model equations and their actual values. The results of these tests for Commodity Trip Table both the inbound and outbound models are shown in Tables Since the model estimates the trip ends of a special gener- 8.7 and 8.8, respectively. There is no significant difference ator, it does not develop any trip tables. between the predicted values and the observed values for both models at the 95% confidence level. Mode Split Model Application The model estimates total trucks; the mode split step is not available in the model. The most important application of the model is to fore- cast the daily and hourly truck movements for the future year. The following steps are needed to forecast daily truck Flow Unit and Time Period Conversion volumes. Assignment 1. Forecast Monthly Imported/Exported Freight Units. Fore- No assignment step was necessary in this model. cast imported and exported monthly freight units using time series ARIMA and regression equations. Model Validation Table 8.6. Outbound loaded freight trucks regression model statistics. This is a flow factoring model, which does not include separate trip generation, trip distribution, mode choice, and traffic assignment steps. This section describes the model Summary Statistics validation statistics available in the research report. Regression Statistics Tables 8.5 and 8.6 present the inbound and outbound linear Multiple R 0.82805933 regression models summary statistics. The R-squared values R Square 0.68568225 for the inbound (attraction) and outbound (production) mod- Adjusted R Square 0.66822015 els indicate that the Heavy Truck Model explains almost 80% Standard Error 203.248744 of the variability in the number of inbound loaded truck move- SSE/Mean 0.20846025 ments, and almost 70% of the variability in the number of Observation 20 outbound loaded truck movements (dependent variable).

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53 Table 8.7. Statistical comparison between the observed total number of inbound loaded freight trucks and the predicted values by the attraction regression model. Paired t-Test Actual Predicted Mean 1,148 1,225 Variance 417,489 417,474 Observations 8 8 Pearson Correlation 0.81 Hypothesized Mean Difference 0 Df 7 T Stat -0.55 P (T<=) One-Tail 0.30 T Critical One-Tail 1.89 P (T<=) Two-Tail 0.60 T Critical Two-Tail 2.36 2. Forecast Weekly Imported/Exported Freight Units. Forecast 5. Forecast for Each Day of the Week Within Each Group. the total number of weekly imported and exported freight Estimate the daily number of inbound and outbound units by multiplying the monthly number of freight units loaded freight trucks by multiplying the regression model from Step 1 by the average percent of each week of the results for the number of loaded trucks for each group by month. the average of truck movement percentage for each day of 3. Forecast for Each Group of Days. Forecast for each group of the week. days by multiplying the weekly number of freight units 6. Forecast Hourly Truck Volumes. Estimate the total hourly resulting from Step 2 by the average percentage of each volume of trucks by using the results from Step 5 and mul- group. tiplying these figures by the percentages of trucks for each 4. Forecast Loaded Trucks for Each Group of Days. Forecast hour. the total number of loaded trucks generated by the Port of Miami for each group of days for each direction by Performance Measures and Evaluation applying the attraction and the production models developed. Not developed for this model. Table 8.8. Statistical comparison between the observed total number of outbound loaded freight trucks and the predicted values by the production regression model. Paired t-Test Actual Predicted Mean 1,004 906 Variance 57,150 104,258 Observations 8 8 Pearson Correlation 0.86 Hypothesized Mean Difference 0 Df 7 T Stat 1.61 P (T<=) One-Tail 0.08 T Critical One-Tail 1.89 P (T<=) Two-Tail 0.15 T Critical Two-Tail 2.36