1. 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. 3 Detour: Why social networks ?

4. 4 Distance distribution

5. 5 Example of a social network geography

6. 6 Size of network geometries

7. 7 Contacts and population shares

8. 8 Contact frequencies by distance band

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

10. 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. 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. 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. 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. 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 arrival by group waiting and 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. 15 Approaches NameNeed forEstimation unchosen alternatives Discrete choicemodelYesML Work/leisure trade-offNoML W/L & DC (Jara-Diaz)(Yes)ML Time share replication (Joh)NoAd-hoc Rule-based systemsNoCHAID etc. Ad-hoc rule basesNoAd-hoc

16. 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. 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. 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. 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. 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. 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. 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. 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. 24 Issue: Various frameworks for activity participation and time allocation No joint model including timing