4.1  Introduction

The FAF² freight flow O-D coverage is limited to 131 freight analysis zones that include

114 CFS freight O-D zones and 17 major ports, border crossings, and freight ports. The FAF² commodity flow data are benchmarked to 2002 and forecasted to 2035. However, this aggregate level of FAF² data limits the ability to conduct reasonable freight assignment modeling to quantify the freight demand on each highway transportation link that covers approximately 450,000 miles of roadway. Given the current geographical aggregation of FAF² data (some states, e.g., Montana, Idaho, Wyoming, and a few others, have only one O-D data point), the use of FAF² data for network assignment was minimal.

• The network-level flow assignment was required to conduct several key applications, including
• The identification of regional and national significant freight corridors
• Roadway preservation and capacity need assessments
• Freight bottleneck assessments
• Assessment of freight diversion through “what if” modeling of policy decisions and their effects on network supply and improvements
• Assessment of dedicated truck lanes
• Evaluation of the impact on national freight movement of natural and manmade disasters.

Further disaggregation of FAF² O-D data was carried out using the proportional allocation techniques at county or sub-county levels to facilitate a reasonable freight assignment.

4.2  FAF² O-D Data Disaggregation Process

The FAF² O-D data for 131 FAF² traffic analysis zones (TAZs)were provided to Battelle for traffic analysis in two generic forms:  domestic activity and international activity. The O-D pairs for international movement were further divided into cross-border movements (US-Canada and US-Mexico) and port movements. Cross-border movements were defined as O-D pairs originating from FAF² TAZs adjacent to Canada or Mexico and destined to other FAF² TAZs and vice versa. Similarly, for ports, the O-D pairs originated from or were headed toward a FAF² TAZ containing one or more ports or gateways. Most O-D data disaggregating techniques were based on proportioning allocation by land use, or by employment or economic activity data that reflect the intensity of production and consumption for that particular TAZ. For domestic
O-D movements, this assumption is reasonable; however, for international movement, the choice of a particular border, port, or gateway is a matter of its logistic efficiency, including its capacity to process goods. The FAF² O-D data disaggregations for subsequent freight modeling assignments is later carried out in the following steps:

• Develop FAF² freight tonnage O-D to Truck Trip
• Develop and generate freight trip activity centers
• Disaggregate FAF² O-D truck trips to freight activity centers.

4.2.1  Develop Allocate FAF² Freight Tonnage O-D to Vehicle Classes

Prior to disaggregating the FAF² O-D database from 131 Freight Analysis Zones (FAZs) to smaller FAZs, the FAF² tonnage data were converted into an equivalent trip table using the truck payload conversion factors described in Chapter 3. The development of a truck trip O-D matrix for disaggregation was based on all commodities combined. The FAF² freight traffic analysis goal is to estimate the equivalent truck traffic generated by all commodities combined and then assign the equivalent truck traffic to the FAF² network. The conversion of freight tonnage to equivalent truck trips between two FAF² O-D pairs was carried out in the following steps:

• Allocate freight tonnage to individual vehicle classes
• Apply a ton to truck conversion algorithm to estimate numbers of loaded trucks
• Estimate empty truck population by vehicle group
• Estimate total trucks by summing loaded and empty truck populations for each vehicle group.

The tonnage to truck payload conversion factor was developed by using commodity-specific payload, truck class, truck body type distribution, and the state T&SW regulation to convert each commodity tonnage to equivalent trucks (Single Unit, Truck plus Trailer, Tractor plus Trailer, Tractor plus Double Trailer, Tractor plus Triple Trailer, and Empty Truck). However, travel distance between two O-D pairs is also an important factor in determining how much of the total tonnage will most likely be carried by these vehicle groups. It is most unlikely that a weight-out commodity will travel a long distance by single unit truck (e.g., Los Angels to Chicago). Therefore, prior to applying the conversion factor, an allocation distribution matrix by traveled distance was developed using the 2002 Vehicle Inventory and Use Survey (VIUS) Data. The VIUS variable “Range of Operation” was used for this analysis. The allocation table by distance is shown in Table 4.1.

Table 4.1:  Tonnage Allocation Distribution Matrix by Distance

Distance (Miles)	SU	CS	TT	DBL	TPL
<=200	0.1	0.75	0.02	0.12	0.01
>200	0	0.88	0	0.099	0.021

 

FAF² O-D tonnage by each of the 131 O-D pairs was first assigned to five vehicle groups using the distance allocation factor and the corresponding distance between FAF centroid. The allocated tonnage was then converted to equivalent loaded trucks by each vehicle group and nine body types using the ton to truck equivalent factor illustrated in Table 3.3. The number of empty trucks was estimated by multiplying the percent distribution of empty trucks reported in Table 3.4. The overall process is illustrated in Figure 4.1.

 

Figure 4.1:  FAF² Ton to Truck Data Conversion Process

 

4.2.2  Develop and Generate Freight Trip Activity Centers

Classical traffic demand analysis requires the establishment of traffic analysis zones (TAZs) or boundaries from which the trips will originate and end via a prior established network. Typically these zones are derived from U.S. census tract, ZIP code polygon boundaries, polygon boundaries defined by the Bureau of Economic Analysis (BEA), township or county boundaries, or other user-defined polygon boundaries. The size and density of a TAZ depends upon the type of trip demand modeling. Typically, a denser TAZ is required for micro planning (urban) and a less dense zone is required for macro planning (national).

For FAF² freight demand analysis, the U.S. county boundaries were defined as the maximum extent of a polygon boundary for a FAZ. Each FAZ identifies one or more freight activity centers. The concept of a FAZ is similar to the TAZ but considers freight trips only. County boundaries are used as FAZ boundaries solely based on the fact that each county is a subset of a FAF² zone. Typically, FAF² O-D data would be loaded onto a representation of the U.S. highway network for the purposes of mapping and subsequent analysis, using a single point of geographic O-D within each county. While this approach may suffice for the majority of counties, it results in an unsatisfactory representation of freight movements over U.S. highways within heavily used metropolitan areas and international gateways. Using a single freight activity generation and attraction point for each of these counties and gateway zones causes too much freight to be assigned to too few highway routes. Creating multiple freight start and end points within such counties is an obvious method for providing a more reasonable representation, including a better mapping of such flows.

The ubiquitous nature of truck freight pickup and delivery operations means that such freight is often generated by, and collected at, a wide variety of geographic locations within a county. This may include large daily volumes of truck activity at seaports, at river docks, at truck-rail intermodal terminals, and at airports, as well as at major manufacturing plants and at wholesale, retail, warehousing and redistribution, and extractive industry sites. The technical problems addressed in the development of freight activity centers are described in details in the FAF¹ technical report on Freight Analysis Framework Highway Capacity Analysis (http://ops.fhwa.dot.gov/freight/documents/CapMeth/) and is broken down into four general areas.

1. Identifying and weighting high-volume freight activity sites within U.S. counties
2. Developing a method for suitably aggregating these spatially separated activity sites into a small set of traffic activity centers
3. Adding any additional sub-activity centers (special freight generators)
4. Assigning each of these activities to the nearest node on the U.S. FAF² freight network.

This process converts the 131 CFS O-D FAZ activity centers to 3,784 FAF² activity centers including node connectivity at border crossings, truck/rail intermodal facilities, and inland water and port facilities. Each of the activity centers is represented by a network node for trip generation and attraction purposes. Out of these 3,784 freight activity centers, there are 3,447 domestic centers, 99 international border crossing ports, and 238 inland water and sea ports. The domestic centers contain urban truck generator clusters and truck/rail intermodal facilities. This disaggregation of FAF FAZ zones to the FAF² network nodes ensures that freight shipments originate or end at major business activity areas in a county or truck/rail and port locations via intermodal connectors, or U.S. land border crossing locations. It also ensures that FAF² highway segments return with freight flows for subsequent segment level performance analysis. Figure 4.2 illustrates how Los Angeles county and FAF gateway zone 116 in the Pacific Northwest are further disaggregated to county major business activity centers, deep water ports, and border crossings.

4.2.3  Disaggregate FAF² O-D Truck Trips to Freight Activity Centers

A disaggregated database with production and attraction freight trips for each of the activity centers developed under section 4.2.1 was developed for subsequent network level freight assignment. The production for a given FAF² zone is the number of freight trips that originate from the FAF zone, and attraction is the number of freight trips that are received by (or destined to) the zone. The numbers of freight trucks produced by and attracted to each of the freight activity centers that constitute a given FAF² zone were distributed proportionately to each activity center using the following equation:

 

          (4.1)

 

Where

(T)_c(s) =   freight trucks/tons produced or attracted in activity center c(s)

(T)_s    =   freight truck/tons produced or attracted in FAF zone (s), which comprises a set of freight activity centers

E_c(s)    =   percent share of freight activity by the freight center C_(s) for FAF² zone (s)

E_s       =   total freight activity within the FAF² zone (s).

 

Figure 4.2:  FAF² Disaggregated Freight Activity Centers used for Assignment

 

Three separate E_c(s) freight activity share tables were developed:  domestic, sea, and border-crossing. A Microsoft SQL database procedure was used for disaggregation for each of these three FAF² O-D databases. First a look-up factor table was developed that contained the share of freight developed earlier. As typical of most applications of allocating resources among various sub-zones [traffic generator within the same FAZ], the factors for an original cell were required to add up to one (1) for both rows and columns. The factor table indicates how the cells of the matrix should be divided and the spatial relationship that each county holds for a given FAF² zone.

The following generic algorithm was used to develop the freight share table.

For domestic:

          (4.2)

Where

b_c(s) = number of establishments reported by 2005 CPB data in activity center c within zone (s)

b_s = number of establishments reported by 2005 CPB data in zone (s) with activity center c

v_c(s) = estimated 2002 HPMS truck VMT in activity center c within zone (s)

v_s = estimated 2002 HPMS truck VMT in zone (s) with activity center c.

For International Border Crossings:

          (4.3)

Where

t_c(s) = estimated 2006 southbound truck movement for Canada and northbound truck movement for Mexico as reported by U.S. Customs for border crossing c within zone (s)

t_s = estimated 2006 southbound truck movement reported by U.S. Customs for all border crossings in zone (s) with border crossing c.

For inland water and sea ports:

          (4.4)

Where

A_c(s) = estimated 2002 truck traffic for all intermodal connectors/or highway links leading to port c within zone (s)

A_s = estimated 2002 truck traffic for all intermodal connectors/or highway within zone (s) with port c.

 

During the development of the freight share lookup table it was recognized that using only industry establishment-based freight shares can significantly underestimate the influence of other spatial interaction on subzonal freight share. This is due to freight logistics and local/regional freight activity measured in terms of truck counts i.e., cross-border trucks, truck vehicle miles traveled (VMT). Therefore, existing HPMS truck VMT were included in the development of freight share look up table so that a comparable network flow could be achieved after post-assignment (network distribution). Though most of the current practices rely on a traditional gravity model (more suitable for passenger O-D) to disaggregate O-D trips, there is hardly any basis for assuming a distance decay function (trip distribution is an inverse function of route distance) for freight transport typically used in a gravity model. The probability that a particular commodity is transported from a production zone to a consumption zone depends on the relative scale of the demand of each competing zone, and the generalized cost of the production of one unit of that commodity plus the logistic cost of transporting it from production to the consumption zone. An example of a freight share lookup table structure is illustrated in Table 4.2. Table 4.3 illustrates secondary disaggregation from CFS FAZ to FAF network freight activity nodes. The disaggregation also assumed that freight activity-to-freight activity disaggregation of FAF² commodity data is subject to FAF² region-to-region control total (conservation of flow). The overall disaggregation process is illustrated in Figure 4.3 that resulted three FAF² truck trip O-D matrices:

• Domestic O-D matrix (3,444 by 3,444 matrix cells)
• International Border Crossings matrix ( 99 by 99 matrix cells)
• Inland and Sea Ports matrix (238 by 238 matrix cells).

Table 4.2: CFS Zone to CFIPS FAZ Disaggregated Freight Share

CFS Zone	CFIPS	Freight Share
		Border Crossing	Inland/ Sea Port	Domestic
70	36003	0	0	0.01547
70	36007	0	0	0.07371
70	36009	0	0	0.03054
70	36011	0	0	0.02715
70	36013	0	0.4325	0.05385
70	36015	0	0	0.03194
70	36017	0	0	0.01791
70	36019	0.473374641	0	0.03099
70	36023	0	0	0.02081
70	36025	0	0	0.02096
70	36031	0	0	0.01996
70	36033	0.021239494	0	0.01876
70	36039	0	0	0.0224
70	36041	0	0	0.00334
70	36043	0	0	0.02146
70	36045	0.35911983	0	0.04182
70	36049	0	0	0.00973
70	36053	0	0	0.02645
70	36065	0	0	0.07885
70	36067	0	0	0.18561
70	36075	0	0.285	0.03548
70	36077	0	0	0.026
70	36089	0.146266035	0.2825	0.03518
70	36097	0	0	0.00578
70	36101	0	0	0.04067
70	36105	0	0	0.03343
70	36107	0	0	0.01637
70	36109	0	0	0.03264
70	36121	0	0	0.01397
70	36123	0	0	0.00863
Total		1	1	1

 

Table 4.3:  CFIPS FAZ to FAF Network Freight Activity Node Disaggregated Freight Share

CFIPS FAZ	FAF Node ID	Freight Share
		Border Crossing	Inland/ Sea Port	Domestic
53073	8058	0	1	0
53073	8073	0	0	1
53073	9557	0.260787703	0	0
53073	10001	0.079330418	0	0
53073	10363	0.54243029	0	0
53073	10367	0.11745159	0	0

 

Figure 4.3:  Overall Disaggregation Process

 

 

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