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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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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, http://www.co.miami-dade.fl.us/portofmiami.
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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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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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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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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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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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
