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Preferred citation style

Axhausen, K.W. and K. Meister (2007) Parameterising the scheduling model, MATSim Workshop 2007, Castasegna, October 2007.

Parametrising the scheduling model

KW Axhausen and K Meister

IVTETHZürich

October 2007

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Detour: Why social networks ?

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Distance distributionF

req

ue

nc

y

250

200

150

100

50

0

Great circle distance [km]100'00010'0001'0001001010

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Example of a social network geography

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Size of network geometries

95%-confidence ellipse of the social network geography

1.E10

1.E91.E81.E71.E61.E51.E41.E31.E21.E11.E0

Pe

rce

nt

40

30

20

10

0

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Contacts and population shares

0.00

0.05

0.10

0.15

0.20

0 10 20 30 40 50 60 70 80 90 100Distance band [km]

Sha

re [%

]

0.0

1.0

2.0

3.0

4.0

Rat

io []

Share of contacts [%]

Share of population [%]

Ratio of contact and population shares

Ratios at 1km: 39; 2km: 9; 3km: 5

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Contact frequencies by distance band

10000.001000.00100.0010.001.000.001

100.00

80.00

60.00

40.00

20.00

0.00

Great circle distance (km)

10000.001000.00100.0010.001.000.001

100.00

80.00

60.00

40.00

20.00

0.00

SMS messages/year Great circle distance [km]

Email messages/year Great circle distance [km]

Phone calls/year Great circle distance [km]

Face-to-face visits/year Great circle distance [km]

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End of detour – So why parametrisation ?

We use uniform current wisdom values

We need:

• Locally specific values• Heterogenuous values

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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)

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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)

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Generalised costs of the schedule

Risk and comfort-weighted sum of time and money expenditure:

• Travel time• Late arrival• Duration by activity type• Expenditure

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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

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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

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Approaches

Name Need for Estimationunchosenalternatives

Discrete choice model Yes MLWork/leisure trade-off No MLW/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

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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 ?

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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

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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)

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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

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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)

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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

i i w iK U T U T U Tw

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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

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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

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Issue:

• Various frameworks for activity participation and time allocation

• No joint model including timing