Methodology for Estimating the Value of Travel Time Reliability for Truck Freight System Users (2016)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

64 Follow-On Research Plan In this chapter, a framework is suggested for further empirical research and economic modeling, drawing upon the research and modeling in this study. As part of the broad contours of future research, information gaps from NCHRP Project 08-99 are identified and an outline for a follow-up research plan that will extend this study and result in improved valuation analysis and modeling is provided. Information Gaps and Model Limitations • The surveys indicated (see Figure 17) that tight delivery windows are targeted by a majority of respondents; 44% cited delivery targets that are either time certain, or within 2 hours. Another 17% indicated within 4 hours. Unfortu- nately, because of a low response rate, the survey did not allow us to identify significant differences in these on-time goals by types of shipper, supply chain characteristics, or commodities. That information is clearly required, and should be explored in depth in subsequent research. An econometric analysis using dummy variables to adjust reli- ability costs up or down by market segment, or segmented model specifications (i.e., estimating separate within group statistical models) would be useful. This would require sig- nificantly larger responses. • The survey research did not allow the research team to sufficiently explore and explain cases where more aggres- sive and—in most cases—more costly mitigating strate- gies are employed; the surveys and interviews uncovered a range of strategies in addition to adding buffer time, such as driver teaming, real-time trip tracking, geo-fencing, price surcharges for very congested service areas, and in the long run, relocation of distribution or production centers. What do these strategies tell us about economic costs of unreliability? Under what conditions are more costly strategies employed? Do the different and—in some cases—much more costly mitigation strategies cited in the survey correlate with factors such as commodity type and value, intermodal connections, expedited deliveries, perishable products, etc.? The Truck Freight Valuation Model includes higher cost factors for different levels of cargo related time sensitivity, but these are assumption driven, not empirically derived. • The surveys did not detect differences in strategies (and implicitly) in valuation of reliability among different truck service providers. In a useful study of the value of truck delay, Miao et al. (2011) uncovered significant differences in valuation among truck service providers across a range of variables including type of carrier (e.g., owner operator, for-hire, private), truck size, cargo value, delivery window, trip length, region, and method of compensating the truck provider. Further research in this area might be included in the next round of SP analysis, or the results from Miao could be adapted in the next modeling phase and used to adjust values in the current model. • Additional exploration of dock penalties, for failure to deliver or complete pick up within a specified time window, is needed. The reasons for imposing those penalties are not well understood, in terms of how competitive conditions in trucking and distribution markets may affect these charges. In addition, the Truck Freight Economic Valuation Model has a number of technical limitations, which should be addressed. These include the following: • The functional relationship between unreliability— measured by the Buffer Index—and economic cost of per trip of unreliability is essentially linear in the model. However, it is much more likely that at low Buffer Index values—say up to 1.2—the costs are lower or close to zero. Similarly, at much higher levels of uncertainty—Buffer Index Values above 1.5 and at extreme values above 2.0, the costs may increase more rapidly than a linear relation- ship. The possibility of developing non-linear relationships C H A P T E R 8

65 should be addressed, and econometric methods based on robust SP data should allow this to be calibrated if sup- ported by the data. Other assumptions that merit testing include these: • The model assumes that shippers and truck service providers have good information about the distribution of probable delays—i.e., they know what the Buffer Index is, and have a good idea of what outlier delays might be. Study is needed to test this. A survey comparing what shippers and truck service providers believe are the probabilities of exceeding the average trip time at various points along the distribution (e.g., 95% on time), versus actual trip time and delay param- eters for a given truck route would be useful. Suggested Research Approach Consistent with the original intent of the truck freight reliability valuation research statement, the next phase should center on a SP analysis, to develop more fine grained and statistically grounded estimates of the value of truck freight reliability, The objective will be to conduct follow-up SP survey research, applied to a significantly larger and more stratified sample than obtained in this study, to obtain statistically valid functional relationships between the perceived level of trip time variabil- ity on the one hand, and the costs of actions taken to mitigate trip time uncertainty, such as buffering or other strategies identified in this study. Key Research Questions The research and empirical analysis and modeling should focus on the following key questions: • What are the costs of employing various mitigation strat- egies? The Truck Freight Reliability Valuation Model pro- duced for NCHRP Project 08-99 provides a good estimation framework and tool for those costs and how those costs vary as a function of varying levels of truck trip time uncertainty, as measured by the trip time distributions. The log normal specification might be reviewed if time permits, to deter- mine if other functional forms could provide better fits to trip time data. • What is the “rate” of cost tradeoffs made by shippers in employing such strategies for a given truck trip time distri- bution? This question relates to the functional form of the relationship between the direct cost given trip time vari- ability and the costs of mitigation. • The surveys found considerable variability in on-time performance targets, from time certain, to one or two hours, to twelve or more hours. Unfortunately, because of a low response rate, the survey did not allow the research team to identify significant differences in these on-time goals by types of shipper, supply chain characteristics, or com- modities. That information is clearly required, and should be explored in depth in the next phase of research. What factors explain these differences? Shipper characteristics/ types of shipper, supply chain characteristics, or commod- ities are all possibilities. • What variables explain the specific mitigation strategies employed by shippers and truck service providers, such as commodity type, trip purpose (i.e., line haul, connection to port or intermodal facilities), and how do these strategies and their costs vary at different levels of trip time uncertainty? As noted herein, additional analysis is needed to explore and explain cases where more aggressive and—in most cases— more costly mitigating strategies are employed; the surveys and interviews uncovered a range of strategies in addition to adding buffer time, such as driver teaming, real-time trip tracking, geo-fencing, price surcharges for very congested service areas, and in the long run, relocation of distribution or production centers. Scope of Work With these questions in mind, an SP analysis, entailing surveys and calibration of econometric models, is proposed, comprised of the following steps/tasks: Phase I Task 1: Develop modeling hypotheses: A first step will be to develop hypotheses of shipper and truck service provider behavior in response to trip time uncertainty. The results of the current NCHRP Project 08-99 study, combined with the other research cited throughout this report, are expected to provide a basis for hypotheses, which can be empirically tested through SP analysis and econometric modeling. Task 2: Design the survey: The survey instrument needs to be carefully designed. Expertise in conducting SP surveys, to obtain valid insights into tradeoffs between the cost of uncertainty and the cost of employing mitigation strategies is needed. Frequently, such surveys have focused on-time vs. cost tradeoffs (e.g., how much of a toll are users willing to pay to avoid congestion and delay at various levels.) This study is slightly different, in that modifying truck routes (such as to a toll facility or a longer but less uncertain route) is only one strategy employed. The possibility of posing simple toll vs. uncertainty tradeoffs may be considered, but its limitations should be carefully reviewed. Moreover, the relevant tradeoff is not between cost and travel time, but between cost and variability in trip times. In this study,

66 considerable attention was given to framing the notion of travel time uncertainty in the survey instrument in ways understand- able to truckers and supply chain professionals. The research team found that terms such as “unpredictable delay” of “percent on-time delivery” were useful. Task 3: Develop the sampling frame and survey approach: A large enough sample and response rate are needed to con- duct stratified analysis, for factors such as commodity type and value, supply chain factors and type of trip and market served, etc. The survey yielded fewer than 200 respondents; this demonstrated how difficult it can be to obtain very high response rates for surveys of this type. The survey consultant, Tompkins, finds that getting enough responses is difficult— indeed increasingly difficult—as shippers and truckers and others in the freight industry are becoming “surveyed out.” Phase II Task 4: Conduct the survey: The survey will be administered based on the Task 3 plan. Task 5: Develop the statistical framework: A standard econometric approach, used by travel demand modelers, is to develop the models within a utility maximization frame- work. The study should specify the functional form of the relationships for testing, based on the hypotheses developed in Task 1. The techniques applied to model development of this nature are well established, and the skills required to calibrate models of this type are quite ubiquitous throughout the transporta- tion industry. Task 6: Conduct additional case study demonstrations of the updated methodology: Two case studies should be conducted. The possibility of using the same case studies as presented in NCHRP Project 08-99 could be considered. Task 7: Integrate the econometric results into the Truck Freight Economic Valuation Model: The ultimate goal is to develop the valuation tool for project planning and pri- oritization purposes. The approach to modifying the Truck Freight Reliability Valuation Model should be developed. The revisions should address the technical limitations in the current model, identified herein. Time and Resources The study should be completed within 1 year to 18 months. A budget of $300K is suggested.