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The traveler costs of unplanned transport network disruptions: An activity-based approach. Erik Jenelius Royal Institute of Technology, Sweden Lars-Göran Mattsson Royal Institute of Technology, Sweden David Levinson University of Minnesota. Background.

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the traveler costs of unplanned transport network disruptions an activity based approach

The traveler costs of unplanned transport network disruptions: An activity-based approach

Erik Jenelius Royal Institute of Technology, SwedenLars-Göran Mattsson Royal Institute of Technology, SwedenDavid Levinson University of Minnesota

background
Background
  • How to value increases in travel time due to unplanned transport network disruptions (floods, snowfall, severe car crashes etc.)?
  • In cost-benefit analysis
  • For bonus provision for restoration work
  • State of practice: Standard value of time
  • Related but different: Value of reliability/variability

variability

extreme events

travel time

average travel time

slide3
Aim
  • Build theoretical foundation for the traveller delay costs of unplanned transport network disruptions
  • Capture the following aspects:
    • Large delays – marginal values may be misleading
    • Long disruptions – more than one trip may be affected
    • Unexpected events, imperfect information – less ability to adjust travel and daily schedule optimally
    • Flexibility – smaller intrusion of delay
    • Time of day – less room for schedule adjustments later
framework
Framework
  • Trips are made between two activities, e.g., home and work
  • Costs arise as we rather spend time at home or at work than in car
  • Schedule preferences expressed as utility maximization
  • We consider three activities (”morning”, ”work”, ”evening”), two trips (”morning commute”, ”evening commute”)
  • Calibration against empirical results from Tseng & Verhoef (2008)
variables
Variables
  • Marginal activity and travel utilities:u1(t), u2(t–ξts2), u3(t), ν
  • Marginal utility of activity 2 (work) may depend on arrival time:Parameter ξ controls schedule flexibility:ξ = 0clock-time only ξ = 1duration only
  • Travel times T1, T2 (assumed exogenous here, departure time dependent in paper)
  • Departure times td1, td2, arrival times ts2 = td1 + T1, ts3 = td2 + T2
the model
The model
  • Daily utility U determined by departure times
travel costs
Travel costs
  • To avoid new notation, assume utility is money metric. Marginal WTP functions for activity/travel transitions:
  • Assume optimally timed trips normally
  • FOC and marginal VOT can be found
  • Travel cost
delay costs
Delay costs
  • Journey delays T1, T2
  • Delay costs
  • Depend on:
    • journey delays (magnitude and distribution)
    • schedule adjustments (information)
    • work schedule flexibility
adjustments
Adjustments
  • Evidence from I-35W bridge collapse
  • We consider:
    • no adjustment
    • no + optimal
    • over-adjustment
    • over + optimal
    • optimal adjustment
calibration
Calibration
  • Calibrated against time-varying WTP for home/work from Tseng & Verhoef (2008) and some findings from Hess et al. (2007)
  • Parameterized logistic functions for marginal WTP functionsa1(t), a2(t – xts2), a3(t): min, max, steepness, location
numerical results
Numerical results
  • Delay on both morning and evening trip (baseline tr. time 240 min)
  • Fixed (left) vs. flexible (right) work hours
numerical results1
Numerical results
  • Delay on morning trip only or evening trip only
  • Fixed (left) vs. flexible (right) work hours
some conclusions
Some conclusions
  • Delay costs increase rapidly with length of delay
  • Better adjustments (information) can reduce costs significantly
  • Flexible work hours great if journey delay occurs early
  • Previous model-based valuations of disruption impacts (I-35W bridge collapse etc.) have probably underestimated delay costs
  • We here only considered work trips and individuals’ own stated costs