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51 Access to on-dock rail; included groups such as AASHTO committees, TRB commit- Connection time/distance to nearest limited-access high- tees, and AMPO. Not including forwarded e-mails, a rough way or mainline rail head; and estimate suggests that some 1,500 individuals received an invi- Average cost of dray operations. tation to participate. In total, 92 respondents completed the Air cargo data: survey, with the following overall distribution: Aircraft parking; Airfield capacity; State agencies (32 percent), Warehouse capacity; MPOs (22 percent), Availability/efficiency of federal inspection services; Federal agencies (11 percent), Tug distance to aircraft parking ramp; Ports (4 percent), Number of alternative access truck routes; Consultants (13 percent), and Connection time/distance to nearest limited-access high- Other (18 percent). way or central business district (CBD); Average cost of dray; and Respondents were involved in all modes of transportation, Operations. including air, rail, truck, pipelines, and water. The research team contacted 22 online survey respondents for follow-up The 2009 North American Freight Flows Conference: interviews. Understanding Changes and Improving Data Sources, Irvine, General trends and observations from the survey responses CA (128), included a number of presentations that further and follow-up interviews include the following: highlighted data needs and uses by a wide range of freight Not surprisingly (given that respondents were typically data users, including national-level agencies, states, regions, and the private sector. Worth noting were presentations that planners), most respondents indicated that they use freight reported on the increasing use of fine-resolution Global Posi- data to support the production of public-sector transpor- tation planning documents. However, respondents also tioning System (GPS) data from truck fleets for the produc- reported using freight data for a variety of other applications. tion of maps and reports documenting truck travel times, Examples of freight data applications reported included delays, and O-D patterns. Also worth noting was the increas- the following: ing importance of accurate, appropriate data to support the Customs processing; growing demands for efficiency and productivity in the sup- Development and economic incentives; ply chain, particularly considering trends such as the increas- Economic analysis and impact; ing use of intermodal arrangements, the need to restructure Energy and climate change; distribution networks to maximize efficiency and minimize Environmental impacts and air quality conformance; miles, and the need to make freight transportation more envi- Goods movement; ronmentally neutral. Hazardous material handling; Incident response; Surveys and Interviews Industry and state needs; International trade; For completeness, the research team conducted three sets of Logistics management; surveys to gather information about freight data uses and needs: Marketing and seeking grant funding; A general survey, which focused on planners and analysts; a Planning and forecasting; shipper survey; and a motor carrier survey. In general, the pur- Performance measurement; pose of the surveys was twofold: (1) to gather general informa- Policy development; tion about freight data practices and needs to confirm and/or Regional and national system functionality analysis; expand the observations from the literature review and (2) to Roadside safety inspection; identify potential responders for more in-depth follow-up tele- Routing and dispatching; phone interviews. Readers should be aware that the surveys and Safety analysis; follow-up interviews were not random or scientific samples. Transportation infrastructure analysis, design, and con- struction; Transportation operations; Planners and Analysts Truck volumes for highway assignments; and The purpose of this activity was to gather information from Workforce development and training. government planners, analysts, and other similar freight- These trends add weight to the notion that the national related stakeholders. The invitation to participate in the survey freight data architecture should support the use of freight

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52 data for a wide range of applications, not just those in con- In-house roadway loop data, nection with traditional transportation planning and fore- LTL commodity/market flow database, casting activities. National Bridge Inventory, With the exception of insurance statistics (which only had NIPA, two responses), all other freight data types included in the NSDI, survey were well represented in the responses. This trend is North American Trucking Survey, consistent with the observation above in that a variety of Own operational data, freight data options are necessary to support the various busi- Own regional forecasts of commodity volumes, ness processes in which planners are involved. Respondents PIERS, also provided examples of freight data types not originally Private dataset from telematics network, included in the survey. Examples of types of freight data iden- State estimates of truck traffic, tified by respondents included the following: North American Transborder Freight Database, Business directories; TRANSEARCH Insight, Commodity inventories; VIUS, Distribution warehouse truck traffic data; VTRIS, and Economic data; WCUS. Emissions estimates; The survey and follow-up interviews provided important Employment by freight activity; feedback regarding unmet freight data needs (i.e., freight- Fuel statistics; related data that stakeholders do not currently have but GPS and GIS data; would see benefit in having). Common unmet data needs Import/export statistics; expressed by respondents included the following: Infrastructure inventories; Continuously updated or near real-time freight-related Licensed carrier data; data; Manifests and waybills; Data that can be used to shed light on the economic Mine output data; impact of freight moving through a specific area or Operational data; corridor; O-D data; Data that can be easily converted to reflect an accurate Oversize/overweight permits; number of vehicles moving through a specific area or Pavement and infrastructure conditions; corridor; Pipeline volumes; Detailed routing information; Railroad tonnage data; Freight transportation and inventory costs; Safety data; Idling statistics and accurate emissions data; Speeds, travel times, and delays; More accurate data, particularly economic data, O-D data Traffic bottlenecks; (including external flows), and travel time and delay data; Vehicle inventories; and More accurate employment data; Vehicle and traffic statistics. More affordable freight data (the complaint being that All of the freight data sources included in the survey were some private-sector databases are too expensive); represented in the responses. Respondents also provided More cooperation among agencies that collect and dis- examples of freight data sources not originally included in seminate freight-related data; the survey. Examples of freight data sources identified by More data from private industry sources, particularly respondents included the following: shippers and carriers, including vehicle-related data (e.g., Annual advisory group input, GPS data), commodity data (e.g., waybills, manifests), Annual carrier meetings, and commercial vehicle availability by establishment; CFS, More disaggregated commodity flow data (e.g., at the EDI service providers, county level); Energy Data Book, More accurate intermodal data; FAF, O-D data beyond intermodal drayage for port-related FARS, trucks; Freight transportation and logistics service, Percentage of total truck movements on local and regional General Estimates System (GES), systems that is actually port-related; GPS data from trucks, Rail traffic data; HPMS, Reintroduce VIUS;

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53 Standardized definitions and methodologies for collect- portation planning applications unless carrier movement ing and disseminating transportation-related data; data are included. For example, although a data record Truck volumes generated by local industrial and com- might characterize a commodity being transported as well mercial sites and what implications those volumes have as origin and destination locations, the route data compo- on determining the most desirable routes; nent may be missing if the carrier is not integrated into the Up-to-date national and state freight data; and shipper's data transactions. Updated international O-D data. Although each private company interviewed functioned dif- ferently, most companies keep freight-related data for sim- ilar purposes. The most common reasons for keeping data Shippers included the following: The purpose of this activity was to gather general infor- Required by law or company policy to keep record of each mation from the shipper community regarding freight data shipment; uses and needs, as well as willingness to share data with Accounting purposes; other freight-related stakeholders. The research team con- Customer compliance; tacted representatives of 14 companies of various sizes, includ- Performance monitoring purposes; Forecasting purposes; ing third-party logistics, freight forwarders, manufacturers, Identifying new business opportunities (i.e., sales leads); retailers, and suppliers, and used the shipper questionnaire and as a starting point for the discussion. Several industries and Customs processing. commodities were represented, including automobile parts, Selection of freight mode of transportation is typically medical instruments, food and bakery products, chemical based on one or more of the following factors: products, retail, furniture, and household cleaning products. Freight bill payer's preference (typically, whoever pays General trends and observations from this activity include for the freight selects the routing method); the following: Physical access characteristics of shipper and receiver locations; The shipper industry collects large amounts of data, partic- Cost of the freight movement on a particular mode; and ularly on a shipment-by-shipment basis. Typical shipment Time sensitivity of the delivery process. data elements collected included the following: Many respondents indicated they could not comment on Shipper address; their companies' ability or willingness to share data for Consignee address; freight transportation planning purposes without a higher- Commodity description (some shippers provide more level executive's permission. This type of response is not commodity details than others); surprising given the competitive nature of the shipping Piece and/or pallet count; industry and the sensitive and/or confidential nature of Weight; some of the information those companies need to man- Carrier used; age on a day-to-day basis. Subsequent feedback obtained Shipment billing type (e.g., collect, prepaid, and third- at the peer exchange (see below) highlighted a number of party prepaid); strategies to address this issue, including initiating dis- Shipment bill to (i.e., party paying for the freight cussions about data sharing with industry associations to movement); provide filtered and/or aggregated data. This strategy Shipment rate; would enable individual firms to maintain confidential- Ship date; and ity and would shield them from potential Freedom of Infor- Delivery due date. mation Act requirements. A business model might also Many shippers and logistics service providers have the emerge in which data providers would forward sample capability to transmit data electronically using EDI tech- data to a designated agency on a pre-determined schedule nologies. These stakeholders use EDI regularly for load ten- for developing a commodity flow database at the national dering, tracking, and freight payment purposes. Technol- level. The data would be stripped of certain identifiers to ogy has advanced to the point where it is routine for data address privacy and confidentiality concerns. Although providers to be able to tailor the amount of data and level the data would not be available for free (since filtering, of data detail they provide to individual trading partners forwarding, storing, and processing the data would involve and carriers within the supply chain. These capabilities offer real costs), it is anticipated that the cost of collecting the unique opportunities for freight data exchange. However, data would be a fraction of the cost to conduct normal shipper-provided EDI data may not be sufficient for trans- surveys.

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54 Motor Carriers In contrast, LTL carriers stated that they were more likely to collect commodity-level detailed data. These carriers typically The research team conducted a motor carrier survey and bill customers using a rate structure based on shipment weight, follow-up interviews to gather general and detailed informa- origin, destination, and freight classification. The traditional tion from the motor carrier community regarding private- classification of LTL freight is based on NMFC codes. How- sector freight data uses and needs, as well as willingness to share ever, due to deregulation and the competitive landscape of the data with external freight-related stakeholders. The research trucking industry, there is anecdotal evidence that LTL carri- team emailed survey solicitations to 75 for-hire interstate ers are now collecting less descriptive or uniform commodity- motor carriers of various sizes, which are members of an indus- level detailed data. It is common for freight rate negotiations try council on information and technology. In total, 13 motor between LTL carriers and shippers to result in a freight-all- carriers completed the online survey. The team conducted kinds (FAK) rating structure that assigns a general freight clas- additional follow-up interviews with these respondents to sup- sification to all shipments from a shipper regardless of freight plement the survey results and glean insights into specific data commodity or type. LTL carriers also are more likely to track elements collected by motor carriers. Survey respondents rep- total tonnage, probably because carriers need to use more com- resented all major sectors of the carrier industry and had plicated profitability models, as well as labor productivity analy- the following distribution: 46 percent TL, 23 percent LTL, ses, at cross dock or terminal locations. and 31 percent specialized. Specialized carriers indicated that their collection of In general, motor carriers collect large amounts of data from commodity-level detail depends highly on the specific type of a variety of sources, such as transportation management sys- freight or carrier operations. For example, carriers that trans- tems (TMSs), engine control modules (ECMs), freight billing port a significant amount of hazardous materials are likely to and accounting, and in-cab communication systems including collect detailed commodity-level data due to strict regulatory GPS-based systems and on-board safety systems. At a high reporting requirements. Other specialized carriers (e.g., flat bed level, the research team found that data collected by motor car- or heavy equipment haulers) may levy freight charges on a per- riers that would be most relevant to a national freight data mile or flat rate basis, decreasing the need to collect shipment- architecture fell into the following categories: level detailed data such as commodity or shipment weight. A significant portion of the industry collects vehicle routing Shipment level detail and tonnage; and mileage data. However, respondents indicated a discrep- Vehicle routing and mileage; and ancy between actual routes traveled by company trucks and the Corporaterevenue,profitability,and lane (corridor) analysis. recommended routes generated by truck management software packages. Due to the high per-mile costs of operating a truck, For all three major sectors of the industry (TL, LTL, and spe- most carriers collect data on total fleet miles or average miles per cialized), shipment-level data typically collected by motor car- truck. Coupled with revenue per shipment data, carriers also riers include shipper and consignee-related data such as name, use mileage data to conduct lane and profitability analyses. address, shipper bill of lading number, freight rate or revenue Most carriers interviewed do not participate in data shar- detail, and pickup date. Less commonly collected shipment- ing programs with public- or private-sector entities. How- level data include commodity-related description, shipment ever, all but one carrier indicated a willingness to share at least weight, tare-level data (e.g., pallet, drum, and pieces), delivery some type of data for public transportation planning activi- date, and shipper or shipment reference numbers. ties. Nearly half of interviewees indicated that they would be Respondents stated that industry operating environments, willing to share data in aggregated form, while half indicated customer expectations, and freight billing practices signifi- that they were not sure if they would be willing to share data. cantly affect the collection of shipment-level data by carriers. Common concerns expressed by interviewees regarding data For example, TL carriers tend to bill customers on a per-mile sharing include the following: basis or using a flat rate. Because the amount of revenue is the same regardless of shipment volume or weight, there is little Carrier efforts to provide data must not be overly burden- incentive to collect commodity-level detailed data. TL carriers some or cause the carrier to incur additional costs. that do collect this type of data tend to collect only generalized, It is critical to maintain the confidentiality and proprietary non-standardized, and/or proprietary descriptions. Inter- nature of the data. Data requests must also include clear viewees also indicated that shipper bills of lading vary widely provisions to protect the anonymity of both carriers and in commodity-level descriptions (or contain no description at their customers. all). In addition, TL carriers responded that they are less likely Carriers would need to know in advance the specific uses to collect data on tonnage hauled or tare-level data, also attrib- of the data. In return for sharing data, carriers would like utable to industry-accepted billing practices. some type of industry benchmarking metrics.