Value of Time for Commercial Vehicle Operators in Minnesota

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Value of Time for Commercial Vehicle Operators in
Minnesota

• by
• David Levinson and Brian Smalkoski
• University of Minnesota

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Spring Load Restrictions

• Minnesota Statute 169.87 enacted policy in 1937.
• Restricts weights of commercial vehicles during
  spring months.
• Impetus for policy pavement strength varies with
  seasonal change.

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

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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.

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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.

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Value of Time

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

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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.

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Previous Value of Time Studies
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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.

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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.

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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.

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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
  projects budget constraint.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Switching Point Results
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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
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Tobit Results

• 49.42, using all 50 cases and an upper limit of
  78.75.
• Statistically significant with a t-statistic of
  11.07.
• 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 52.36. (0.65
  80)

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Variation in Value of Time

• The value of time varies based on the operation
  of the trucking firm.

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By Freight Facility Type
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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.