National Academies Press: OpenBook

Freight Data Cost Elements (2013)

Chapter: Chapter 6 - Addressing Freight Cost Data Gaps

« Previous: Chapter 5 - Identification and Assessment of Data Sources
Page 52
Suggested Citation:"Chapter 6 - Addressing Freight Cost Data Gaps." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Data Cost Elements. Washington, DC: The National Academies Press. doi: 10.17226/21939.
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Page 53
Suggested Citation:"Chapter 6 - Addressing Freight Cost Data Gaps." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Data Cost Elements. Washington, DC: The National Academies Press. doi: 10.17226/21939.
×
Page 53
Page 54
Suggested Citation:"Chapter 6 - Addressing Freight Cost Data Gaps." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Data Cost Elements. Washington, DC: The National Academies Press. doi: 10.17226/21939.
×
Page 54
Page 55
Suggested Citation:"Chapter 6 - Addressing Freight Cost Data Gaps." National Academies of Sciences, Engineering, and Medicine. 2013. Freight Data Cost Elements. Washington, DC: The National Academies Press. doi: 10.17226/21939.
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Page 55

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52 This chapter discusses the need for the analyst to clearly identify what is expected from freight cost techniques; ana- lyzes the role of alternative data collection techniques, rang- ing from web searches to random surveys; defines a basic set of alternative data collection frequencies; and suggests com- binations of data collection techniques and data collection frequencies for the various data cost elements identified in previous chapters. 6.1 Means for Addressing Limitations and Gaps Unfortunately, there is no all-inclusive way to address all of the limitations and gaps in freight cost data. In some cases, many different sources of cost data are available; however, quite frequently, these are one-time-publications for which the data is almost always tailored to the specific needs of the report. To address the limitations and gaps, data must be col- lected directly from the freight community. Depending on the need, the data might be collected from random surveys, convenience samples, technical reports, in-depth interviews with experts, trade publications, or from the Internet. Also, an evaluation of the available cost data sources should be conducted regularly to verify the continued availability of existing sources, identify new sources, and assess the applica- bility of any new sources. 6.2 Methods and Procedures Section 6.2 summarizes a number of key questions ana- lysts should address before undertaking freight cost analyses, including data collection techniques, data collection frequency, and other issues. The starting point of an analysis should be a clear definition of its objective. This is important because it directly determines the most appropriate ways to collect the data and perform the cost estimation. More specifically, the analyst should answer the following set of questions: • What is the desired spatial coverage? The answer to this question has a direct impact on data collection costs and data needs. For example, collecting data from a large area is more expensive than collecting data from a small one. Also, if the analysis includes international freight flows, currency exchange rates will need to be considered. • What are the industrial sectors? The more limited and specific the sectors to be analyzed, the simpler and less expensive the data collection will be. • What freight modes are involved? If more than one freight mode is involved, then different cost estimation techniques and data sources may be required for each segment. Further- more, the intermodal exchange and overall administrative costs have to be considered in the cost analysis. • What is the level of accuracy desired? The higher the level of accuracy desired, the higher the cost. This factor could have a direct impact on the sample size required to meet the anticipated needs. • What type of cost is desired? The analyst must decide if the main focus is on marginal costs at the vehicle level, route-related costs, or average costs. The requirements of the analysis will affect the data collection procedures to be employed. • What is the desired time frame? If an analyst is looking at historical trends and/or long-term future estimates, such factors as inflation adjustments will need to be taken into account. Once these questions have been answered, the next step is to consider the various means to collect the data needed. In so doing, two important decisions need to be made: (1) which data collection technique to use; and (2) at which collection frequency will the data be updated. These aspects are discussed next. C H A P T E R 6 Addressing Freight Cost Data Gaps

53 6.2.1 Data Collection Techniques Section 6.2.1 identifies each of the data collection tech- niques that could be used to obtain data for a particular cost data element. The technique used will ultimately depend on the particular needs of the analysis being performed. The techniques are: • Conducting Web Searches (W): In some cases, perform- ing a web search can provide the necessary cost data. The World Wide Web often is consulted as a source for general cost data and costs related to public agencies, such as vehi- cle registration costs, sales tax rates, and other data about permits and licenses. The posting agency or organization should be contacted to verify the information. • Consulting Trade Publications (TP): For some of the freight modes, regularly published trade publications may provide general cost data, and can also provide contact information for gathering further data. • Consulting Reports (R): This technique involves referring to published documents that are readily available for use. Reports that were found by the research team to have excel- lent or medium levels of detail for a particular cost data element are recommended. • Conducting Interviews (I): Depending on the purpose of the analysis, interviews could be an efficient means of data collection. Interviews should target individuals famil- iar with the particular cost data elements, which might be specific to a certain freight sector. For example, if the purchase price of a new truck is sought, an interview with a knowledgeable truck salesperson might be all that is required to determine the cost. In other cases, it might be necessary to interview individuals at a specific company or people who work within a certain sector. The interview questions need to be well prepared and pretested to avoid confusion. • Obtaining Convenience Samples (CS): In convenience sampling, the observations are not selected at random; rather, they are selected on the basis of what works best for the analyst. Convenience samples are an excellent method for obtaining data for cost data elements that are not expected to vary greatly from company to com- pany or from region to region. If the cost data elements are expected to be similar regardless of sector or location, convenience samples are sufficient. It is important to men- tion that the analyst should try to ensure that the observa- tions selected as part of the convenience sample represent a broad spectrum of cases. • Conducting Random Surveys (RS): In random surveys, all observations have the same chance of being chosen. The populations in survey work contain a finite number of units, and random surveys are of particular interest when an unbiased sample is required. They can be used to collect most of the cost data, although—depending on the level of detail required—random surveys can be cost prohibitive and time prohibitive. Random surveys are most beneficial for cost data elements that might have fluctuations from company to company or region to region. Random sur- veys ensure that the range of costs in a particular freight mode will be illustrated. Random surveys must be care- fully designed. Table 6.1 summarizes the advantages and disadvantages of the different data collection techniques. The various data collection techniques each have dif- ferent implications in terms of associated cost and time required, overall level of effort, and accuracy of the data col- lected. Qualitatively, the techniques can be ranked in terms of these aspects. Figure 6.1 shows the ranking produced by the research team. For example, conducting web searches on the Internet requires minimal cost, time, and effort, though this technique also may have the lowest level of accuracy. By contrast, conducting random surveys to collect data is likely to entail the highest cost and require the most time and effort, though it may be the most accurate way to estimate freight data cost elements. 6.2.2 Data Collection Frequency The frequency at which the data cost elements ought to be collected depends on both the analyses’ objectives and the inherent volatility of the data. For example, some cost data elements, such as fuel prices, can change drastically within a short period of time while other cost data elements, such as the average service life of a piece of equipment, may change more slowly. To provide guidance to practitioners, the research team included recommendations for data col- lection frequency for the various data cost elements (see Table 6.2). 6.3 Suggested Approaches to Close Data Gaps Table 6.2 summarizes the top data gaps identified for the “essential” cost data elements for different modes. The table shows the suggested data collection techniques and the frequency of data collection needed to close those data gaps. Appendix C shows the research team’s suggestions for mechanisms to close the data gaps identified for truck- ing, rail, waterways, and freight terminals. It is important to keep in mind that these recommendations are general and could change depending on the specific needs of the analyses.

54 Technique Advantages Disavantages Web Search Low cost, less time to collect data, low level of effort required to gather data. Low level of accuracy of data collection. It depends on the frequency of the publication. Trade Publications Low cost, less time to collect data, low level of effort required to gather data. Low level of accuracy of data collection. It depends on the frequency of the publication. Reports Low cost, less time to collect data, low level of effort required to gather data. Low level of accuracy of data collection. Interviews High accuracy of data collection, provides deep knowledge in a certain sector. High data collection cost. Convenience Samples High accuracy of data collection, provides deep knowledge in a certain sector. They are great way to obtain cost data for cost data elements that are not expected to vary greatly from company to company or region to region. High data collection cost. Random Surveys High accuracy of data collection, provides deep knowledge in a certain sector. They are the most beneficial for cost data elements that might have fluctuations from company to company or region to region. They are the most unbiased means for collecting data. High data collection cost (cost prohibitive). Although random surveys are the most unbiased mean for collecting data, they could seriously bias the results if the sampled universe includes entities that do not meet stated criteria. Table 6.1. Advantages and disadvantages of data collection techniques. Technique: Data collection cost Time to collect data Level of effort required Accuracy of data collected Web Searches Trade Publications Reports Interviews Convenience Samples Random Surveys In cr ea sin g co st In cr ea sin g tim e In cr ea sin g ef fo rt In cr ea sin g ac cu ra cy Figure 6.1. Relative performance of data collection techniques. In the case of air transportation—because air freight operations are within the domain of the private sector—no publicly available data or information can be used to iden- tify the corresponding freight data cost elements. Should transportation agencies want to step into this domain to produce air freight cost estimates, a significant amount of work would be required to identify the corresponding cost elements. The research has shown that there is clearly a lack of freight cost data. For those data elements that are available, it is not always clear where to find the data or how to use the data in an efficient manner. Clearly there is a need to compile all of the cost data and cost models in a single repository, such as a research center that could serve as a clearinghouse for this type of data. Combinations of one-time and regular frequency studies exist that often contain useful data, but if the data are not consolidated, they are not very useful to the community at large. A clearinghouse could be responsible for continuously updating the available data sources, and could provide a “one-stop shop” for this type of data. The clearing- house could also provide descriptions of the various data, models, and guidance to the end users of the data.

Cost Data Element / Data Source Legend W e b S e a r c h T r a d e P u b l i c a t i o n R e p o r t I n t e r v i e w C o n v e n i e n c e S a m p l i n g R a n d o m S u r v e y B i e n n i a l A n n u a l S e m i - a n n u a l M o n t h l y W e e k l y Permits and tolls (trip specific) Parking costs (including fines) Driver benefits and bonuses Salvage value of specialized equipment Maintenance costs for power units and trailers Insurance for both power units and trailers Estimated salvage value of locomotives and rolling stock Average maintenance costs of locomotives and rolling stock Insurance costs for locomotives and rolling stock Wages for operators and crew Benefits for operators and crew Average maintenance costs for towing vessels and barges Insurance for equipment Lock charges and licenses for operation of equipment Average purchase cost Operating costs Fuel or electricity consumption Fuel cost Electricity cost Estimated salvage value Terminals Data Collection Technique ( Preferred, Method) Data Collection Frequency ( Preferred) Trucking Rail Waterborne Table 6.2. Top data gaps for “essential” cost data elements.

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TRB’s National Cooperative Freight Research Program (NCFRP) Report 22: Freight Data Cost Elements identifies the specific types of direct freight transportation cost data elements required for public investment, policy, and regulatory decisionmaking. The report also describes and assesses different strategies for identifying and obtaining the needed cost data elements.

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