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Preferred citation style for this presentation
Fourie P.J., M. Shah (2012) Initial demand generation for MATSim Singapore, FCL Workshop, Singapore.
Initial demand generation for MATSim Singapore
FOURIE P., M. Shah FCL Workshop
Singapore
Out put /Inp ut M od els Inp ut da ta
Initial demand modelling: Activity assignment
SingStat Home facilities Work Facilities Educ. Facilities Other Facilities Pop. synthesis Pop distribution Driving license Mode choice EZ Link Synthetic Pop. Synthetic Pop. +1 Synthetic Pop. +2 Activity assign.
Primary location Initial Plans +1 Secondary location Initial Plans +2 Car ownership Synthetic Pop. +3
Initial Plans final.xml HITS M’plan/REALIS/St.Dir. Activity duration Initial Plan.xml Initial Plans +3 BDLG footprints
Travel decision assignment
• Not everybody uses
motorized travel
• In HITS, approximately
one third of eligible persons do not make motorized trips
• Generally, classifiable by income, age, household car availability and car license
Classifying travel decision and mode
• We classify HITS records based
on modes used during the course of the day
• We add exclusive users of other
modes than car and PT to the list of non-travelers
• We produce joint distributions
of age x income x car availability x car driver licensing.
Classifying travel decision and mode
Then, we perform weighted sampling of travel decision and initial transport mode for the synthetic population. 6 Assignment HITS notravel 1903937 20276 car 516458 4028 pt 1448270 11547 ptmix 309638 2201 Grand Total 4206908 38052
Activity chain assignment
We identify the 22 top activity frequencies for motorized trip-makers. h – home w – work s – school/education l – shop/social/rec/eat/sport b – biz/errand/medi 7 other 2242 non-motorized 9123
Activity chain assignment
We derive joint distributions of the various activity chains by sex,
occupation…
Activity chain assignment
…age and household car availability…
Activity chain assignment
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Then, we perform a weighted sampling assignment for each synthetic traveler in R.
Activity chain assignment
As we include expats, the
relevant proportion of worker chains is higher than in HITS. As scholars are likely to be
dropped off, or take school bus, their contribution to our car
driver/PT demand is diminished.
12 Chain # HITS records Assignment
h-w-h 10,361 1,268,977 h-s-h 6,664 422,432 h-l-h 2,069 253,668 h-b-h 671 86,886 h-w-l-h 536 65,405 h-w-l-w-h 185 25,570 h-w-h-l-h 187 24,845 h-l-l-h 196 24,087 h-w-b-h 155 18,550 h-s-l-h 217 17,764
Work activity assignment
Ordonez’s facility classification process identifies 15 clusters of work activity, based on start time and duration. Here, HITS worker records have been classified in the same way.
Work activity assignment
HITS records are stratified by occupation, and
whether the person has been assigned additional activities before, during or after work, or not.
Then a kernel density
estimate is performed for each stratum.
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occup: cleaner chain: work_only
Work activity assignment
Where a stratum is too sparse, densities from the marginal distributions (occup x chain) are multiplied.
These density estimates are used in a weighted
sampling of start time and duration for each stratum in the synthetic
population.
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occup: professional chain: act_after_work
Work activity assignment
A nearest neighbour
assignment to the cluster centroids completes the process.
Here, a sample of 50,000 worker agents shows a similar structure to the HITS joint distribution.
Educational activity assignment
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Educational facilities are classified as primary,
secondary, post-secondary, tertiary and others (similar to HITS classification).
Pre-schools are excluded.
HITS records are then used to generate a joint distribution of educational activity x age x household income.
Educational activity assignment
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An educational activity type is then assigned to each agent through weighted sampling, based on the agent’s age and household income.
Educational act Number of travellers
s_other 20,798
s_postsecondary 20,650
s_primary 88,782
s_secondary 154,361
Out put /Inp ut M od els Inp ut da ta
Initial demand modelling: Primary activity type
SingStat Home facilities Work Facilities Educ. Facilities Other Facilities Pop. synthesis Pop distribution Driving license Mode choice EZ Link Synthetic Pop. Synthetic Pop. +1 Synthetic Pop. +2 Activity assign.
Primary location Initial Plans +1 Secondary location Initial Plans +2 Car ownership Synthetic Pop. +3
Initial Plans final.xml HITS M’plan/REALIS/St.Dir. Activity duration Initial Plan.xml Initial Plans +3 BDLG footprints
Primary activity location assignment
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A educational activity type is then assigned to each agent through weighted sampling, based on the agent’s age and household income.
Educational act Number of travellers
s_other 20,798
s_postsecondary 20,650
s_primary 88,782
s_secondary 154,361
Primary act location assignment: overview
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Primary activity location assignment is done on a per-agent basis in Java. 1. For each person, a sample of a 1000 qualifying facilities are drawn; 2. An ‘attractiveness’ weight is derived for each facility, based on gravity
assignment type strategy (weight ∝ 𝑐𝑐𝑐/𝑑𝑑𝑑𝑡2 );
3. For work activities, weights are attenuated by multiplying each weight with the relative probability of finding the agent’s occupation type at the facility’s URA land-use type;
4. A facility is then picked from the agent’s choice set using weighted sampling.
Work location assignment
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For each person, a sample of a 1000 qualifying facilities are drawn.
An ‘attractiveness’ weight is derived for each facility, based on gravity assignment type strategy
(weight ∝ 𝑐𝑐𝑐/𝑑𝑑𝑑𝑡2 ).
Gravity parameter estimation was done using Euclidean distance, by vehicle availability.
Work location assignment
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If there is a car available in the agent’s household, the procedure uses the car parameters for weight estimation, otherwise PT.
Work location assignment
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This figure illustrates the relative frequency of HITS occupation type by URA land use type.
The gravity weights for each facility are attenuated by
multiplying each weight with the relative probability of
finding the agent’s occupation type at the facility’s URA land-use type.
Work location assignment: result
Educational facility location assignment: result
Out put /Inp ut M od els Inp ut da ta
Initial demand modelling: Primary activity location
SingStat Home facilities Work Facilities Educ. Facilities Other Facilities Pop. synthesis Pop distribution Driving license Mode choice EZ Link Synthetic Pop. Synthetic Pop. +1 Synthetic Pop. +2 Activity assign.
Primary location Initial Plans +1 Secondary location Initial Plans +2 Car ownership Synthetic Pop. +3
Initial Plans final.xml HITS M’plan/REALIS/St.Dir. Activity duration Initial Plan.xml Initial Plans +3 BDLG footprints
Leisure facility location assignment: result
Biz/errand/medi trip facility location assignment: result
Out put /Inp ut M od els Inp ut da ta
Initial demand modelling: Activity durations
SingStat Home facilities Work Facilities Educ. Facilities Other Facilities Pop. synthesis Pop distribution Driving license Mode choice EZ Link Synthetic Pop. Synthetic Pop. +1 Synthetic Pop. +2 Activity assign.
Primary location Initial Plans +1 Secondary location Initial Plans +2 Car ownership Synthetic Pop. +3
Initial Plans final.xml HITS M’plan/REALIS/St.Dir. Activity duration Initial Plan.xml Initial Plans +3 BDLG footprints
Activity duration assignment
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For workers, the activity type assignment also takes care of start time and duration.
For these agents, their
assigned work activity times provide the structure of the day.
Any other activities are fitted between the times dictated by the work activity and
reasonable values for starting and ending the day
Activity duration assignment
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Educational activity timings are guesses, based on the activity chain of the agent and open times of facilities.
Times are blurred within 30 minute intervals to prevent initial congestion.
As time allocation mutation is a replanning strategy, final timings will evolve based on facility open times and overall plan utility maximisation.
Educational act Open times
s_other 07:00 – 15:00
s_postsecondary 07:00 – 17:00
s_primary 07:00 – 13:00
s_secondary 07:00 – 15:00
Out put /Inp ut M od els Inp ut da ta
Initial demand modelling: Activity durations
SingStat Home facilities Work Facilities Educ. Facilities Other Facilities Pop. synthesis Pop distribution Driving license Mode choice EZ Link Synthetic Pop. Synthetic Pop. +1 Synthetic Pop. +2 Activity assign.
Primary location Initial Plans +1 Secondary location Initial Plans +2 Car ownership Synthetic Pop. +3
Initial Plans final.xml HITS M’plan/REALIS/St.Dir. Activity duration Initial Plan.xml Initial Plans +3 BDLG footprints
Mode assignment
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Modal assignment is currently a result of the initial travel
decision assignment process (income, age, household car availability and car license). Half of the ptmix trips are allocated to PT, the other half aren’t simulated. Mode Assignment notravel 1903937 car 516458 pt 1448270 ptmix 309638/2 Grand Total 4206908
Out put /Inp ut M od els Inp ut da ta
Special trips
SingStat Home facilities Work Facilities Educ. Facilities Other Facilities Pop. synthesis Pop distribution Driving license Mode choice EZ Link Synthetic Pop. Synthetic Pop. +1 Synthetic Pop. +2 Activity assign.Primary location Initial Plans +1 Secondary location Initial Plans +2 Car ownership Synthetic Pop. +3
Initial Plans final.xml HITS M’plan/REALIS/St.D ir. Activity duration Initial Plan.xml Initial Plans +3 BDLG footprints
Concept:
• Reproduce LTA special trip matrices between given zones with single trips • 0.023 trips? -> Random rounding to integers
Heavy goods vehicle / light goods vehicles:
• Distributed between commercial and industrials buildings
Malaysia Checkpoint:
• Distributed to all buildings
Tourist:
• Distributed to hotels, shopping and leisure buildings
Time:
• According to dynamics as reported in HITS, slightly blurred
Special trip matrices – Validation against counts
Outlook
• Extensive validation of each step in the initial demand generation
process.
• Consolidation and cross-referencing of data will make discrete choice
models possible for location choice models, e.g. UrbanSim data framework.
• Educational facility assignment needs refinement, especially tertiary
level.
• Secondary facilities need refinement of capacities and attractiveness.