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

1. 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

2. 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

3. 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

4. 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)

5. 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

6. The model • Daily utility U determined by departure times

7. 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

8. Delay costs • Journey delays T1, T2 • Delay costs • Depend on: • journey delays (magnitude and distribution) • schedule adjustments (information) • work schedule flexibility

9. Adjustments • Evidence from I-35W bridge collapse • We consider: • no adjustment • no + optimal • over-adjustment • over + optimal • optimal adjustment

10. 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

11. Numerical results • Delay on both morning and evening trip (baseline tr. time 240 min) • Fixed (left) vs. flexible (right) work hours

12. Numerical results • Delay on morning trip only or evening trip only • Fixed (left) vs. flexible (right) work hours

13. 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

14. Thank you!