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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 IVT ETH Zürich October 2007. Detour: Why social networks ?. - PowerPoint PPT Presentation
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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