Parameterising Activity Scheduling Model for Transportation Planning

1. Preferred citation style • Axhausen, K.W. and K. Meister (2007) Parameterising the scheduling model, MATSim Workshop 2007, Castasegna, October 2007.

2. Parametrising the scheduling model KW Axhausen and K Meister IVT ETH Zürich October 2007

3. Detour: Why social networks ?

4. Distance distribution

5. Example of a social network geography

6. Size of network geometries

7. Contacts and population shares

8. Contact frequencies by distance band

9. End of detour – So why parametrisation ? • We use uniform current wisdom values • We need: • Locally specific values • Heterogenuous values

10. Degrees of freedom of activity scheduling • Number (n ≥ 0) and type of activities • Sequence of activities • Start and duration of activity • Group undertaking the activity (expenditure share) • Location of the activity • Connection between sequential locations • Location of access and egress from the mean of transport • Vehicle/means of transport • Route/service • Group travelling together (expenditure share)

11. 2007: Planomat versus initial demand versus ignored • Number (n ≥ 0) and type of activities • Sequence of activities • Start and duration of activity • Group undertaking the activity (expenditure share) • Location of the activity • Connection between sequential locations • Location of access and egress from the mean of transport • Vehicle/means of transport • Route/service • Group travelling together (expenditure share)

12. Generalised costs of the schedule • Risk and comfort-weighted sum of time and money expenditure: • Travel time • Late arrival • Duration by activity type • Expenditure

13. Generalised costs of the schedule • Risk and comfort-weighted sum of time and money expenditure: • Travel time • By mode (vehicle type) • Idle waiting time • Transfer • Late arrival by group waiting and activity type • Duration by activity type • By time of day/group • Minimum durations • By unmet need (priority) • Expenditure

14. Generalised costs of the schedule • Risk and comfort-weighted sum of time and money expenditure: • Travel time • By mode (vehicle type) • Idle waiting time • Transfer • Late arrivalby group waitingand activity type • (Desired arrival time imputation via Kitamura et al.) • Duration by activity type • By time of day/group • Minimum durations • By unmet need (priority) (Panel data only) • Expenditure – Thurgau imputation; Mobidrive: observed

15. Approaches • Name Need for Estimation • unchosen • alternatives • Discrete choice model Yes ML • Work/leisure trade-off No ML • W/L & DC (Jara-Diaz) (Yes) ML • Time share replication (Joh) No Ad-hoc • Rule-based systems No CHAID etc. • Ad-hoc rule bases No Ad-hoc

16. Criteria • How reasonable is the approach ? • How easily can the objective function by computed ? • Are standard errors of the parameters easily available ? • Can all our parameters be identified ? Can we estimate means only ? • What is the data preparation effort required ? • Do we need to write the optimiser ourselves ?

17. Frontier model of prism vertices (Kitamura et al.) • Idea: Estimate Hägerstrand’s prisms to impute earliest departure and latest arrival times • Approach: Frontier regression (via directional errors) • Software: LIMDEP

18. PCATS (Kitamura, Pendyala) • Not a scheduling model in our sense • Idea: Sequence of type, destination/mode, duration models inside the pre-determined prisms • Target functions: • ML (type, destination/mode, number of activities) • LS (duration) • Software: Not listed (Possibilities: Biogeme; LIMDEP)

19. TASHA (Roorda, Miller) • Not quite a scheduling model in our sense • Idea: Sequence of conditional distributions (draws) by person type: • Type and number of activities • Start time • Durations • Rule-based insertion of additional activities • No estimation as such; validation of the rules

20. AURORA - durations (Joh, Arentze, Timmermans) • Idea: • Duration of activities as a function of time since last performance ( time window and amount of discretionary time) • Marginal utility shifts from growing to decreasing • Target function: Adjusted OLS of activity duration under marginal utility equality constraint • Software: Specialised ad-hoc GA • See also: Recent SP, MNL & non-linear regression (including just decreasing marginal utilities functions)

21. W/L tradeoff with DCM (Jara-Diaz et al.) • Idea: Combine W/L with DCM to estimate all elements of the value of time • Value of time savings in activity i • μ: Marginal value of time • λ: Marginal value of income • μ/λ: Value of time as a resource

22. W/L tradeoff with DCM (Jara-Diaz et al.) • Idea: Combine W/L with DCM to estimate all elements of the value of time • Target function: • Cobb-Douglas for the work/leisure trade-off • DCM for mode choice • Estimation: LS for W/L trade-off; ML for DCM

23. Discrete continuous multivariate: Bhat (Habib & Miller) • Idea: Expand Logit to MVL and add continuous elements • Target function: closed form logit • Estimation: ML • Example: Activity engagement and time-allocated to each actvity

24. Issue: • Various frameworks for activity participation and time allocation • No joint model including timing