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From page 13... ... 2 C20 Project on freight model improvement, will consider long-term model improvements to develop over the next 10 years or more. From the list of research topics developed by the research team, the research panel selected the following three topics that address critical gaps in existing freight demand models: • Chaining of freight activities, • Temporal and seasonal impacts, and • Modal diversion consistent with the geographies of public agencies. Read the entire page →
From page 14... ... Better Methods to Consider Nonfreight Trucks Expanding on a paradigm developed by Hunt and Stefan,2 trucking activity in a model area can be considered to have the following four components: 1. Interregional freight, typically trips with at least one trip end external to the region that is being modeled. Read the entire page →
From page 15... ... Although it might be possible to develop this information using time series of equipment and/or labor availability together with time series data of commodity flow, such datasets are not readily available. Where times series of commodity flow databases do exist (e.g., Commodity Flow Survey [CFS] Read the entire page →
From page 16... ... The truck operators that are the customers of the GPS vendor must include firms that primarily offer services within the metropolitan area, in order to provide the desired information on trucking operations within metropolitan areas. The historical data provided by the GPS vendor must include, at a minimum, information on GPS events identified by vehicle, which includes the date, time, and location. Read the entire page →
From page 17... ... It has a land use shapefile available that could be used to process the data. • Phoenix, which has active freight and truck studies. Read the entire page →
From page 18... ... . Although not presented, these data could be processed to determine additional 18 Metro Area Land Use Number of Trucks Number of GPS Events Number of Origins Percent of Origins by Land Use Number of Origins per Truck per day Total 6,901 3,926,611 853,049 100% 9.05 Industrial 5,702 640,084 202,187 24% 3.32 Low density 4,631 230,703 44,876 5% 2.10 Other high-density employment 5,830 1,420,470 164,858 19% 3.73 Residential 4,919 773,228 176,728 21% 3.56 Los Angeles Retail and commercial 6,083 862,126 264,400 31% 3.92 Total 3,290 1,955,033 432,311 100% 10.59 Industrial 2,730 357,130 116,749 27% 4.38 Low density 2,441 241,271 39,584 9% 2.28 Other high-density employment 2,650 554,915 77,209 18% 4.14 Residential 2,298 348,463 76,076 18% 3.29 Chicago Retail and commercial 2,888 453,254 122,693 28% 3.97 Total 2,797 1,044,132 258,578 100% 8.61 Industrial 1,894 186,544 52,747 20% 3.24 Low density 2,058 273,476 36,396 14% 2.53 Other high-density employment 1,310 59,669 18,996 7% 2.75 Residential 1,917 287,941 78,102 30% 3.84 Baltimore Retail and commercial 2,343 236,502 78,937 28% 4.25 Total 2,851 1,491,659 436,758 100% 10.80 Industrial 2,446 179,345 54,718 13% 0.90 Low density 2,554 409,615 92,673 21% 4.33 Other high-density employment 2,163 146,677 43,062 10% 0.97 Residential 2,258 418,321 135,281 31% 2.72 Phoenix Retail and commercial 2,693 337,701 111,024 25% 3.49 Table 3.1. Read the entire page →
From page 19... ... This is due to the fact that some truck trips may begin in one land use type and end in another land use type. The land use activity reported by the GPS data varies by metropolitan area. Read the entire page →
From page 20... ... For example, a truck that is currently stopped at a manufacturing facility might be expected to make its next stop at another manufacturing facility. This information could be used to weight the attractiveness of truck trip distribution for individual trips, and to organize these trips into chains (tours) Read the entire page →
From page 21... ... The distance for a trip was calculated both as the airline distance between the latitudes and longitudes reported for the GPS records, as well as the difference in odometer readings reported by these GPS records. It is worth noting that the GPS odometer reading 21 Baltimore Destination Industrial Other High-Density Employment Retail and Commercial Residential Low Density 11.90% 0.80% 3.20% 2.30% 1.60% 60.40% 3.80% 16.10% 11.40% 8.20%Industrial 60.60% 11.00% 11.90% 6.80% 12.30% 0.80% 2.90% 1.40% 1.20% 0.60% 10.80% 42.10% 20.30% 17.70% 9.00% Other High-Density Employment 3.80% 42.70% 3.70% 3.70% 4.80% 3.20% 1.40% 14.30% 6.50% 2.60% 11.30% 5.10% 51.00% 23.20% 9.30% Retail and Commercial 16.10% 20.80% 53.00% 19.50% 19.80% 1.50% 1.10% 5.60% 20.50% 2.70% 6.90% 3.50% 17.50% 64.20% 8.40% Residential 6.60% 16.30% 20.70% 61.40% 20.30% 1.80% 0.60% 2.50% 2.90% 5.70% 13.10% 4.70% 18.50% 21.60% 42.10% O ri gi n Low Density 9.00% 9.20% 9.20% 8.70% 42.80% Phoenix Destination Industrial Other High-Density Employment Retail and Commercial Residential Low Density 6.30% 0.60% 2.70% 1.10% 1.40% 52.20% 5.30% 22.00% 9.40% 11.10%Industrial 52.60% 6.60% 10.80% 3.50% 6.60% 0.60% 4.00% 2.00% 1.70% 1.10% 6.70% 42.50% 21.50% 17.60% 11.60% Other High-Density Employment 5.20% 41.50% 5.10% 5.10% 5.40% 2.50% 2.10% 12.40% 5.40% 2.90% 10.10% 8.20% 48.80% 21.40% 11.50% Retail and Commercial 21.10% 21.40% 49.60% 16.50% 14.20% 1.50% 1.80% 5.10% 21.20% 3.20% 6.90% 5.50% 15.60% 65.50% 10.00% Residential 6.60% 18.40% 20.30% 64.50% 15.90% 1.40% 1.20% 2.80% 3.50% 11.80% 6.90% 5.70% 13.40% 16.70% 57.20% O ri gi n Low Density 11.90% 12.10% 11.20% 10.50% 57.90% Note: For each cell in the table: the first value, shown in bold, is the percent of the total table, the second value, shown in italic, is the percent of the origin, and the third value, shown in regular type, is the percent of the destination. Read the entire page →
From page 22... ... Although this method cannot be expected to develop factors that would apply to specific locations, the aggregation of the resulting pattern across all locations and commodities is expected to reflect an average national distribution of commodity freight flows by month and by hour. For this research topic, the first step will be to develop a method to assign the truck commodity flows to the highway network. Read the entire page →
From page 23... ... The flow pattern appears similar to that of Figure 3.1 where most flows are concentrated on major interstate highways and the trip ends are concentrated in the counties 23 9 Battelle Memorial Institute, "Chapter 4: FAF2 Truck O-D Data Disaggregation," in FAF2 Freight Traffic Analysis, FHWA, June 27, 2007, http://ops.fhwa.dot.gov/ freight/freight_analysis/faf/faf2_reports/reports7/c4_data.htm. 10 Cambridge Systematics, Inc., Development of a Computerized Method to Subdivide the FAF2 Regional Commodity OD Data to County-Level OD Data, prepared for FHWA, January 2009, unpublished. Read the entire page →
From page 24... ... Rather than processing the flows at the SCTG2 level, the flows were grouped to the nine classes of commodities used in the 2002 CFS. For these CFS commodity groups, the ton-miles of flow were calculated from the assigned link volumes and the distances of those links. Read the entire page →
From page 25... ... Apply Factors to the Commodity Flows on the Network For each of the 623 links that have complete hourly factors developed from counts, those factors were applied to the 25 Figure 3.2. Disaggregated FAF2 truck ton flows (all commodities) Read the entire page →
From page 26... ... CFS1 01 to 05 Agriculture Products and Fish 398,154 CFS2 06 to 09 Grains, Alcohol, and Tobacco Products 210,034 CFS3 10 to 14 Stones, Nonmetallic Minerals, and Metallic Ores 382,451 CFS4 15 to 19 Coal and Petroleum Products 222,452 CFS5 20 to 24 Pharmaceutical and Chemical Products 268,038 CFS6 25 to 30 Logs, Wood Products, and Textile and Leather 330,683 CFS7 31 to 34 Base Metal and Machinery 437,008 CFS8 35 to 38 Electronic, Motorized Vehicles, and Precision Instruments 87,758 CFS9 39 to 43 Furniture, Mixed Freight, and Miscellaneous Manufactured Products 245,299 Total 2,581,876 Truck Ton-Miles -- 2002 CFS Table 2a 1,261,813 Table 3.4. Annual ton-miles traveled by each CFS commodity group. Read the entire page →
From page 27... ... Figure 3.5. VTRIS stations with valid monthly factors. Read the entire page →
From page 28... ... Develop Average National Adjustment Factors The hourly flows for each location were used to develop a national hourly summary of freight flows. The resulting hourly freight flows were aggregated to the nine CFS commodity groups. Read the entire page →
From page 29... ... For those industries with the most variation, since the reported data is shipment value not tons, it is conceivable that the variation is due to fluctuations in com29 Figure 3.7. Monthly distribution of truck commodity flows. Read the entire page →
From page 30... ... . Note: Shaded rows indicate those industries with a standard deviation of 10 percent or greater. Read the entire page →
From page 31... ... In the absence of local data showing specific local variations, any policy considerations of commodity truck flows that have been converted from annual to daily flows based on averages need not be concerned about seasonal variations in those commodities. Similarly, when policy decisions need to consider the hourly variation of commodity flows, absent any specific local information, the hourly distribution of commodity flows according 31 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dollars (in Millions) Read the entire page →
From page 32... ... Forecasting models most often deal with average weekday conditions. From Figure 3.11, the average daily flow is 85 percent of the average weekday flow. Read the entire page →
From page 33... ... Variables in Freight Mode Choice A literature review was conducted to determine variables that would be important in the mode-choice decision for freight. Although not intended to be exhaustive, the variables that were determined to be important in the mode-choice decision for freight are • Characteristics of the mode, including capacity, trip time, reliability, cost; • Characteristics of the goods, including shipment size, shelf life, density, value; • Characteristics of the shipper, including production processes and shipper size; • Characteristics of the receiver, including receiver size and other consumption processes such as operating hours; and 33 Read the entire page →
From page 34... ... 34 Category Utility Variable Corresponding Variable to be Used in Revealed-Preference Utility Estimation Modal Characteristics Capacity Modal Constant Trip Time Modal Distance/Impedance Reliability Modal Constant Equipment Availability Modal Constant Customer Service and Handling Quality Modal Constant Modal Cost Modal Distance/Impedance Goods Characteristics Shipment Size Commodity Total Tons Package Characteristics Commodity Modal Constants Shipment Shelf Life Commodity Modal Constants Shipment Value Commodity Value per Ton Shipment Density Commodity Modal Constants Shipper Characteristics Production Processes Industry Employment at Origin Shipper Size Industry Employment at Origin Receiver Characteristics Consumption Requirements Industry Employment/Population at Destination Receiver Size Industry Employment/Population at Destination Other Logistic Characteristics Inventory Costs Commodity Modal Constants Loss and Damage Costs Commodity Modal Constants Service Reliability Costs Commodity Modal Constants Length of Haul Truck Distance Shipment Frequency Commodity Total Tons Table 3.6. Freight mode-choice variables. Read the entire page →
From page 35... ... Finally, the separation of metropolitan areas into their state portions was intended to aid in developing summaries of freight flows at the state level. The reported FAF2 flows between FAF2 regions in the same metropolitan area but in different states will involve short distances over which modal choice decisions most likely reflect production or logistic processes unique to the commodity and not decisions that should be considered in an RP survey. Read the entire page →
From page 36... ... Additionally, the estimation method only provides an indication that these modal constants are significant relative to an assumed zero value for the base mode, which was chosen to be "truck." It provides no indication of what the absolute modal constant is for that mode because there is no ability to estimate the modal constant for the base truck mode. For example, the estimation that the rail modal constant is significant might indicate that any rail capacity, rail reliability, rail equipment availability, or rail customer service and handling quality are important considerations in mode choice but that does not indicate the relative importance of each, nor does it indicate the absolute utility for any of these, only the total relative utility compared to that of the truck mode. Read the entire page →
From page 37... ... Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Constant 0 0 -10.4 -7.72 -4.91 -14.21 -4.37 -25.83 -4.17 -23.41 -5.82 -18.15 Distance -0.00423 -0.85 -0.00188 -0.41 -0.00123 -0.44 -0.00397 -0.85 -0.00127 -0.27 -0.00418 -0.74 dist * log(kton) Read the entire page →
From page 38... ... Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Constant 0 0 -6.40 -37.05 #N/A #N/A -4.91 -47.38 -1.51 -37.81 -6.23 -36.67 Distance -0.00886 -3.89 -0.00601 -2.81 #N/A #N/A -0.00608 -2.91 -0.01050 -5.14 -0.00931 -3.25 dist * log(kton) Read the entire page →
From page 39... ... This allows the variation of distance, which was the most significant explanatory variable singly and in combination with the other variables, to be plotted and examined against observed mode shares in the RP data. For the agricultural products commodity group, Figure 3.12 shows the results of varying distance on the mode-choice estimates (shown as curves) Read the entire page →
From page 40... ... The results of varying distances on the mode-choice estimates are shown as curves and the observed mode shares are shown as stacked column bars. Again, the model has a good statistical fit, but it does not appear to match observed mode shares. Read the entire page →
From page 41... ... Results of revealed-preference mode-choice estimation with two distance classes. (continued on next page) Read the entire page →
From page 42... ... Commodity Truck Truck and Rail Water Rail Water and Rail Air Statistics Group Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Coeff t-stat Constant 0 0 -7.78 -11.97 #NA #NA -7.19 -15.13 -4.71 -23.48 -8.66 -8.58 Distance <500 -0.00659 -18.56 -0.00287 -2.00 #NA #NA -0.00158 -1.48 0.00179 3.3 -0.00005 -0.02 Distance >500 -0.00057 -7.94 -0.00024 -1.71 #NA #NA -0.00107 -8.71 -0.00006 -2.52 0.00028 1.61 dist * log(kton) Read the entire page →
From page 43... ... Additionally with fewer records available, this estimation for the remaining commodities in this group cannot successfully develop coefficients for the distance across products with shipment size and value, as logarithms of total tons and total value per ton. As shown in Figure 3.17, for the distances below 500 miles, the observed flows do appear to correspond to the estimated mode share, but again this may be largely due to the dominance of truck mode share over this distance range. Read the entire page →
From page 44... ... Results of revealed-preference mode-choice estimation with two distance classes (STCG 14 and remainder of stone and ore commodity group) Read the entire page →
From page 45... ... 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Pr ob ab ili ty truck rail water/rail Truck Rail Water/Rail Figure 3.16. Mode share by distance for CFS Commodity Group 3, SCTG 14 (estimated with two distance classes and observed) Read the entire page →

From page 13...

... 2 C20 Project on freight model improvement, will consider long-term model improvements to develop over the next 10 years or more. From the list of research topics developed by the research team, the research panel selected the following three topics that address critical gaps in existing freight demand models: • Chaining of freight activities, • Temporal and seasonal impacts, and • Modal diversion consistent with the geographies of public agencies.