Estimating the Value of Truck Travel Time Reliability (2019) / Chapter Skim
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22 This chapter describes how the value of reliability (VOR) was estimated with the data collected with the stated-preference survey.
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Modeling 23 As seen previously, most research, particularly in Europe, has modeled reliability by using the standard deviation of travel times. Using the notation adopted for the present study, this could be calculated as However, this measure has several disadvantages.
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24 Estimating the Value of Truck Travel Time Reliability Substituting Equation 6 into Equation 5 and collecting terms leads to The per-ton costs can be calculated as One way to consider reliability in this model is to assume that firms pick a larger γ to buffer against the risk of late deliveries having spillover effects. Hirschman et al.
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Modeling 25 In this simple model, the component of VOR due to trucking is a function of how a marginal increase in uncertainty, s, decreases the utilization of vehicles (as γ is increased to add slack to the system) , leading to more trucks being needed to move the same freight and increasing time-related costs.
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26 Estimating the Value of Truck Travel Time Reliability 4.1.6 Lessons for Modeling The simple analytical model presented above and the findings from the literature provided the following lessons for the statistical modeling: • Unreliability does not increase the direct costs of operating trucks, in terms of fuel consumption, maintenance, and so forth. Instead, it makes it more difficult for truck operators to plan their delivery schedules and thus leads them to build slack into their operations in case of unforeseen delays.
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Modeling 27 and shipment reliability. However, not all models presented in this section were robust enough for use in planning analyses.
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28 Estimating the Value of Truck Travel Time Reliability standard deviation. All of these models were estimated on the sample with the outliers removed, as described in Section 3.4.
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Modeling 29 4.2.1.2 Sample Differences The differences in the estimates could also be attributed to the composition of the sample. As shown later in this chapter, company size and shipment distance were found to have a very large effect on VOR estimates.
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30 Estimating the Value of Truck Travel Time Reliability 4.2.3 Sample Weighting When estimating MNL or ML models, weights can be introduced in the log likelihood function that give greater weight to certain survey responses. This approach was used to reweight the sample so that it is more representative of the population being modeled.
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Modeling 31 Parameter Motor Carrier Shipper w/ Transportation Shipper w/o Transportation Model S Model T Model U Model V Model W Model X (ML + com. & dist.
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32 Estimating the Value of Truck Travel Time Reliability The models recommended for planning analysis have the specification used in Models T, V, and X These models are flexible, in that parameters are defined as random variables that can be correlated.
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Modeling 33 4.3.3 Company Size Table 4-7 explores the effect of company size on model estimates. Larger companies were observed to have lower VOR values, likely because these companies have a greater ability to manage the impacts of traffic unreliability.
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34 Estimating the Value of Truck Travel Time Reliability 1–4 Employees 5–100 Employees More than 100 Employees Parameter MNL + Com.
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Modeling 35 Own Company Customer Intermodal Transfer Parameter MNL + Com.
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36 Estimating the Value of Truck Travel Time Reliability 4.3.6 Question Order The order of the questions presented to respondents could influence the results. On the one hand, respondents could take the first questions more seriously because they were less fatigued.
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Modeling 37 estimated VOT values ranging from $14.5/hour to $411.6/hour. Overall, there was a weaker effect for VOT than VOR, suggesting that respondents were most concerned about whether they could predict arrival times.
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38 Estimating the Value of Truck Travel Time Reliability the shipment was a customer -- 2.4 times higher than if the shipment was internal. Small shippers are also more sensitive toward lateness, in part because shipments are less frequent and any delay could have a larger negative impact on operations.
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