Value of Time for Commercial Vehicle Operators in Minnesota by David Levinson and Brian Smalkoski University of Minnesota

Spring Load Restrictions Minnesota Statute enacted policy in Restricts weights of commercial vehicles during spring months. Impetus for policy: pavement strength varies with seasonal change.

Spring thaw introduces a saturated condition in the soil under the pavement; load bearing capacity is reduced and heavy trucks cause additional damage.

Spring Load Restriction Study Mn/DOT study to conduct a cost- benefit analysis of SLR policy. Freight demand model must be estimated. Pavement benefits must be estimated. User costs must be estimated.

User Costs Vehicles complying with SLR policy must change behavior: Shift seasonal timing of shipments. Reduce load size per vehicle. Change vehicle type. Change routes. These behaviors add costs to commercial vehicle operators.

Quantification of Cost Freight demand model will estimate total travel time and vehicle miles traveled with and without SLR policy. The difference must be multiplied by an estimate of the value of time or cost per mile to derive the total user cost.

Value of Time Studied for over 40 years. Four methods: –Net Operating Profit –Cost Savings Model –Cost-of-Time –Willingness to Pay

Methods Net operating profit approach fixes vehicle and labor costs so that with improved speeds, a vehicle will be able to travel further in the same time and contribute more profit. Cost savings model is based on a reduction of those costs that are not variable with miles of operation. Cost-of-time method determines the cost of providing time savings. Willingness to pay method in which individuals are faced with a decision between time savings and other benefits.

Previous Value of Time Studies

Preferences Many past studies have used revealed preference (RP) and stated preference (SP) to derive choice data. RP refers to preferences observed in actual market situations. SP refers to preferences recorded in hypothetical situations.

Stated Preference Several advantages: Can be used when little market choice data exists. Can control for outside influences. Less expensive. Can be used for testing markets for new items not yet introduced. Can introduce variability in situations where little variation exists.

Population Sample Sources: Mn/DOT Freight Facilities database, Mn/DOT filed insurance list, Mn/DOT overweight permit list, MTA board of directors, and city/county engineer survey results. A survey was initially constructed and mailed to this sample to gather information on the companies, the effects of SLR on their operations, and their willingness to participate in an interview. Desired interview sample size: 50.

Interviews Interviews were chosen rather than telephone and mailed methods because the interviewer can be available for clarifying and follow-up questions. Freight demand model is to be constructed for four Minnesota counties. Goal is to interview 12 candidates from each of those counties, a reasonable amount with project’s budget constraint.

Locations Counties were chosen based on available data and geographic location. Only 40 candidates were willing to interview from the four counties. Sample area was increased to include neighboring counties. A pilot study was conducted in Hennepin county, this was chosen to include metropolitan data and due to its close proximity to the University of Minnesota.

Final Design Used adaptive stated preference (ASP) methods. Differ from traditional SP in four major ways: –Options presented in subsequent games depend upon answers in previous games. –Fewer alternatives and attributes are presented in individual games. –Subject is often presented with more games. –Possible to obtain estimates of parameters at the disaggregate level.

Final Design II Added permit schemes as an attribute. Used expected value of fine (probability is taken out of the equation and only the product of the fine and probability of getting caught is included). Presented one no cost option and one cost option in exchange for time or truck load savings. Five scenarios, each with six games.

Final Design III Survey administered on a laptop. A computer program running through an Access database altered values. The computer program used bisection techniques to narrow in on each subjects’ maximum willingness to pay.

Value of Time Estimates Value of time estimates can be obtained by two different methods: –Switching point analysis. –Statistical analysis. Switching point analysis estimates the value of time from the level of trade-off where the choices switch from the cost option to the free option.

Statistical Analysis Based on consumer welfare theory in which consumers choose the alternative that maximizes their utility. Logit models are typically used with discrete choice data The value of time is estimated from the quotient of the parameters for time and cost.

Tobit In cases of truncated data, there may be a number of responses that take on a limiting value. Probit models would be appropriate to estimate the probability of responses taking on the limiting value. Regression analysis would be appropriate for non-limited values. The tobit model is a hybrid of these two techniques.

Switching Point Results

Maximum of Presentations The column Max P refers to the maximum value that an individual chose for the non-free option over all presentations. The value presented is the mean of all subjects. i = individual n = sample size

Tobit Results $49.42, using all 50 cases and an upper limit of $ Statistically significant with a t-statistic of A check for this estimate would be to take the stated cost per kilometer reported by the interviewees and multiply that by a reasonable estimate of kilometers per hour: $ ($0.65 · 80)

Variation in Value of Time The value of time varies based on the operation of the trucking firm.

By Freight Facility Type

Conclusions The value of time was estimated by an ASP survey. The individual games were bounded by ‘reasonable’ estimates of value of time. Several subjects reached the upper limit of the survey. The best model for truncated data of this type is the tobit model, this yields an estimate of $49.42 for the value of time for commercial vehicle operators in Minnesota.