National Academies Press: OpenBook

Guidance for Developing a Freight Transportation Data Architecture (2010)

Chapter: Chapter 3 - Surveys, Interviews, and Peer Exchange

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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
×
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
×
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
×
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Suggested Citation:"Chapter 3 - Surveys, Interviews, and Peer Exchange." National Academies of Sciences, Engineering, and Medicine. 2010. Guidance for Developing a Freight Transportation Data Architecture. Washington, DC: The National Academies Press. doi: 10.17226/14466.
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Page 57

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49 Introduction An important part of designing a national freight data architecture is to identify who the users are as well as their corresponding data needs. As previously mentioned, users include the community of public and private decisionmakers at the national, state, regional, and local levels. The topic of freight data uses and needs has been widely covered in the literature through various reports, papers, peer exchanges, and conferences. A short sample of recent events and publications follows: • 2001 Conference on Data Needs in the Changing World of Logistics and Freight Transportation, Saratoga Springs, NY (5); • 2003 TRB Special Report 276: A Concept for a National Freight Data Program (4); • 2004 draft BTS report “A Preliminary Roadmap for the American Freight Data Program” (13); • 2005 Freight Data for State Transportation Agencies Peer Exchange, Boston, MA (125); • 2005 New York Metropolitan Transportation Council (NYMTC) report “Description of Transportation Data to be Collected for NYMTC’s Products, Reports, and Perfor- mance Measures” (126); • 2007 Meeting Freight Data Challenges Workshop, Chicago, IL (127); and • 2009 North American Freight Flows Conference: Under- standing Changes and Improving Data Sources, Irvine, CA (128). The focus of, and resulting recommendations from, these reports varied. For example, the 2001 Saratoga Springs confer- ence focused on freight movements and recommended identi- fying freight data gaps, using data synthesis tools to fill data gaps, and developing performance measures for freight (5). This conference did not produce a list of data needs, although it did highlight the need for finer resolution O-D data. The 2003 TRB Special Report 276 concluded that providing all the data needed to satisfy all applications would be beyond the scope of any national initiative and recommended the following data items to capture important characteristics of freight movements (4): • Origin and destination; • Commodity characteristics, weight, and value; • Modes of shipment; • Routing and time of day; and • Vehicle/vessel type and configuration. The 2004 BTS report discussed the availability and limitation of various data sources and proposed a data collection pro- gram within BTS’s American Freight Data Program (AFDP), as shown in Figure 7 (13). The 2005 report on state data needs included the results of a survey of state agencies, which included the following in terms of freight data used and/or needed by states (based on responses from 14 states) (125): • Business directories; • Commodity characteristics, weight, and value; • Congestion and travel time data; • Crash and fatality data; • Data on domestic versus international shipments within state; • Economic, land use, and employment data; • Hazardous material identifiers; • Modal inventories; • O-D data; • Performance measure data; • Real-time operational data; • Routing and time of day; • Truck and rail volume counts, classification, and weight; • Vehicle and vessel types and configurations; C H A P T E R 3 Surveys, Interviews, and Peer Exchange

• Waybill data; and • Weigh station data. The NYMTC report on transportation data requirements produced a listing of data elements needed to support the needs of a variety of agencies in the New York metropolitan area (126). A summary of freight-related data requirements at NYMTC follows: • Truck data: – Truck volumes (with respect to total traffic volumes); – Levels of service (LOS) for major truck routes; – Average speed; – Toll costs; – Curbside space management (loading/unloading zones, parking enforcement); – Accident and incident rates; – Height clearances; – Turning radii; – Access width; – Weight limitations; – Truck delays at railroad/highway grade crossings; – Usable shoulders; – Highway design standards, acceleration and decelera- tion lanes, truck climbing lanes; – Signage; and – Curbside capacity (for truck operations). • Rail data: – Rail carloads exchanged with East of Hudson origins/ destinations; – Container or trailer groundings in the East of Hudson region; – Rail freight levels of service (proprietary information, may be difficult to acquire); – Rail as a percentage of total regional freight traffic; – Number of competing carriers (preserving service options through future mergers); – Number of access modes (truck, barge/ferry); – Number of alternative access truck routes; – Connection time/distance to nearest limited-access highway or mainline rail head; and – Average cost of dray operations. • Port data: – Actual throughput (total and per acre); – Actual throughput as a percentage of theoretical “maxi- mum practical capacity” by functional component of each terminal (wharf and crane operation, storage, gate); – Average cargo dwell time; – Hours of terminal operation; – Utilization of storage (high versus low density); – Number of access modes (truck, rail, barge/ferry); – Rail barge mode share; – Number of alternative access truck routes; – LOS on major truck access routes; 50 Figure 7. Freight data collection under the American Freight Data Program (13).

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

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

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

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

It is worth noting that developing metrics of interest to the private sector is part of NCFRP Project 3, “Performance Measures for Freight Transportation” (129). This project is developing measures to gauge the performance of the freight transportation system in areas such as capacity, safety, secu- rity, infrastructure condition, congestion, and operations. The measures should support investment, operations, and policy decisions by a range of stakeholders, both public and private, and reflect local, regional, national, and global perspectives. Peer Exchange In conjunction with the 2009 North American Freight Flows Conference in Irvine, CA (128), the research team organized a peer exchange to discuss preliminary research findings; request feedback; and facilitate a dialogue on implementation strate- gies to develop, adopt, and maintain a national freight data architecture. As Figure 8 shows, the peer exchange included an opening session, breakout sessions, and a final group discussion session. 55 N C F R P P r o j e c t 1 2 – F r e i g h t D a t a A r c h i t e c t u r e P e e r E x c h a n g e T h u r s d a y , S e p t e m b e r 1 7 , 2 0 0 9 , 1 : 0 0 p m – 5 : 0 0 p m , B e c k m a n C e n t e r B a c k g r o u n d The Texa s Transpo rtation Inst itu te (TTI) is cond ucting research fo r NCFRP to (a ) develop specificatio ns for the content and structure of a freigh t data architecture that serves the needs of public an d private decision makers at the natio na l, state, and local levels; (b ) id entify th e value and ch allenges of th e potential architecture; and (c) re comm end institu tio na l strategies to d evelop and main ta in the ar chitectu re. Decisionmakers must u nderstand th e freight tr ansp ortation system (includi ng use, roles, and limita tio ns) to resp ond to th e grow ing logistical requirements of businesses an d ho useh ol ds. This un derstanding draws on disparate data so ur ce s—collected under various definitions, time scales, and geographic levels —such as commod ity mo vements, relationships amo ng sect or s of the economy, intern ational trad e, traffic operations, supply chains, and in frastr uc ture characteristics an d conditio ns. Several stud ies and co nf erences call fo r a nati onal fre ig ht data architecture to lin k existin g datasets an d gu ide new data coll ecti on pr ograms. Ho wever, none of thes e calls defi ne s wh at a da ta architecture is or how an architecture would be designed, imp le me nt ed, or evaluated. P e e r E x c h a n g e P u r p o s e The purpose of the peer exchange is to di sc us s pr el iminar y research find ings; request feed ba ck ; fa ci lita te a dialogue on architect ur e structure and comp onents; and identify im pl em entation strategies an d challenges. P r e l i m i n a r y A g e n d a 1:00 – 1:15 Welcome, introductions, and peer exchange objectiv es 1:15 – 2:00 Presentation of th e draft in te ri m report fi nd in gs 2:00 – 2:15 Breakout group organization and instructions 2:15 – 4:00 Breakout groups (depending on the number of attendees) 2:15 – 2:45 Review rese arch find ings 2:45 – 3:00 Coffee break 3:00 – 3:30 Identify and prioritize need s 3:30 – 4:00 Review a nd id entify specification requ ir ements 4:00 – 4:30 Reports fr om breako ut gr ou ps 4:30 – 4:45 Group discussion and synthesis 4:45 – 5:00 Closing remarks, next steps, and wrap-up C o n t a c t a n d A d d i t i o n a l I n f o r m a t i o n Cesar Quiroga Phone: (210 ) 731-993 8 Email: c-quiroga@tamu.edu Juan Carl os Villa Phone: (979 ) 862-338 2 E -ma il: j- vi lla@ta mu .edu Texas Tr ansportatio n In stitute, Texas A& M Un iv er si ty Syste m Freight data sources and data standards Group A: Developing an architecture for freight data Group B: Strategies and challenges for implementation Group C: Figure 8. Peer exchange agenda.

The purpose of the opening session was to provide an overview of the research project and draft research findings. The purpose of the breakout sessions was to discuss findings and issues in more detail. The purpose of the final group discussion session was to exchange breakout session findings and summarize recommendations. In total, 33 participants (representing federal, state, region, university, and private sectors) attended the peer exchange. Originally, the peer exchange included three breakout groups (Figure 8). However, breakout Group B was canceled because of low interest from peer exchange participants. As a result, relevant questions and issues for discussion were re-assigned to the other two breakout groups. To encourage participation and discussion, attendees received background materials such as relevant research topic summaries and breakout group agendas and discus- sion objectives. Feedback from peer exchange participants included recommendations for changes to initial research findings (which were implemented) as well as a list of issues, challenges, and strategies to consider during the implemen- tation of the national freight data architecture, which are summarized as follows: • Data at different geographic levels. Participants thought the list of data sources discussed at the peer exchange was useful, but highlighted the need to include state, regional, and local data sources in the national freight data architec- ture, noting that national-level data are frequently inade- quate for sub-state, local, corridor, and project analyses. For example, FAF and CFS regions are not consistent with MPO boundaries, making it difficult for MPOs to use national-level data. In addition, CFS is not designed to accept supplemental data from sources such as weigh- in-motion stations and routing permits. Participants con- sidered county-level data collection to be more appropriate than state-level data collection (although some counties are very large). Discussion also included data collection at the three-digit zip code level. • Data at different levels of compatibility. Participants noted that some datasets could be integrated more easily than other datasets. This observation led to a discussion about which datasets to include in a data architecture. Inte- grating spatial data (e.g., through conflation) is frequently possible if the geographic levels of resolution are compati- ble. However, other datasets are too dissimilar, making data integration considerably more difficult. A critical element in integrating datasets is a determination of how feasible it is to “integrate” each dataset into the system. • Freight data architecture vision. Participants agreed that a national freight data architecture should provide a generic framework while providing a methodology to cross- reference data located within different databases and, at the same time, addressing confidentiality concerns by all stakeholders involved. The data architecture should be dynamic rather than static, and be able to respond to new types of data, instead of just working with existing data. Participants also highlighted that a national freight archi- tecture would need to work with cross-border data, and have the ability to include Canada and Mexico (i.e., be multinational with a North American scope). In addition, the national freight data architecture should support crite- ria and methods to locate, map, and classify freight trip generators, trip receivers, and other factors (e.g., employ- ment, land use, ports, intermodal facilities, and airports). The freight data architecture would also need to pro- mote data harmonization (i.e., although datasets do not need to be located in the physical database, they need to have a common thread and comply with at least a mini- mum set of standards to facilitate data sharing and integra- tion). Data harmonization standards need to include basic elements as spatial referencing metadata, in addition to complex elements as performance tracking data. • Freight data architecture value. The value of a national freight data architecture would be demonstrated by the ability of stakeholders to make decisions on infrastructure projects and to the extent that business processes and data access become more efficient. The value also would be demonstrated by the level of support the data architecture provides to the implementation of freight performance measures. Participants discussed the monetary value of the data architecture. Although there was no consensus on how to manage that value, there was discussion about the need for consistent benefit/cost analysis standards and the need to identify the value of freight datasets, espe- cially near-real-time commodity data and routing data. Participants also recommended the development of met- rics to determine the effectiveness of implementing the data architecture. • Freight data architecture ownership. Participants consid- ered that the ownership and/or leadership role of a national freight data architecture would be best placed at the federal level, in principle, at RITA-BTS. • Private-sector data. Participants acknowledged the diffi- culty in obtaining data from the private sector, but recom- mended leaving the door open for data-sharing participa- tion and collaboration. Participants also recommended providing a strong message to the private sector that exist- ing freight data issues affect both the public and private sectors. Participants highlighted the importance of con- tacting the right person at a sufficiently high administra- tive level for discussions about data access and sharing (since low-ranking personnel might know the data, but frequently do not have the authority or permission to dis- cuss data-sharing options). It may be strategic to involve 56

57 trade associations rather than individual firms because trade associations can speak for the industry more easily and can provide leadership for starting a data-sharing rela- tionship. Inviting the private sector to participate in the development of the freight data architecture would also help all of the parties understand issues of mutual concern and identify potential opportunities, especially if the result is lower costs and operational improvements. • Strategies for developing the freight data architecture. Participants agreed with the concept of a scalable freight data architecture that can be implemented in phases (see Chapter 4), and highlighted that implementing a compre- hensive data architecture at once with no testing of options prior to making a decision about the correct approach would be too risky. Participants also favored the concept of developing and comparing several alternative approaches. Participants thought NCFRP would be a good avenue for funding the development of alternative data architec- ture concepts and a prototype. Participants indicated the request for proposals should outline clear objectives, while leaving the definition of approaches to the research team(s) selected. An idea discussed was to develop the data archi- tecture around scenarios or themes, such as business areas or processes, levels of government, and/or economic activ- ity. Participants identified four potential scenarios or themes, including MPOs, the private sector, cross-border trade (e.g., Washington State and British Columbia, or Texas and Mexico), and multistate freight (e.g., I-95 corridor, Great Lakes region). Activities and requirements in connection with each of these scenarios or themes would include struc- turing a competition for research teams (each of which would include a university partner, a private-sector partner, and a government-level partner) to develop and test com- peting data architecture concepts, making sure to include multimodal components in the scenarios and tests, and conduct a follow-up evaluation. The final step would be to merge the best concepts and practices into one composite program. Other strategies for developing the data architecture included adding a communication or marketing component as well as identifying buy-in and consensus issues, compli- ance options, and an administration-level champion. Addi- tional ideas discussed included developing a “showcase” to bring attention to the issue of freight data and developing a roadmap for collaboration between the public sector and the private sector for more effective data collection.

Next: Chapter 4 - Outline and Requirements for a National Freight Data Architecture »
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TRB’s National Freight Cooperative Research Program (NCFRP) Report 9: Guidance for Developing a Freight Transportation Data Architecture explores the requirements and specifications for a national freight data architecture to link myriad existing data sets, identifies the value and challenges of the potential architecture, and highlights institutional strategies to develop and maintain the architecture.

The report also includes an analysis of the strengths and weaknesses of a wide range of data sources; provides information on the development of a national freight data architecture definition that is scalable at the national, state, regional, and local levels; and offers readers a better understanding of the challenges that might block the implementation of a national freight data architecture as well as candidate strategies for developing, adopting, and maintaining it.

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