The document discusses the need to shift from trip-based to tour-based microscopic demand models due to trends like mobility as a service. Microsimulation approaches traditionally introduce statistical noise that limits their strategic planning use. The statistical noise elimination technique in the Octavius microsimulator implemented in OmniTRANS planning software removes randomness and non-uniqueness effects through an optimization process that selects a single discrete solution matching the expected choice model outcomes. This allows Octavius to provide noise-free demand modeling needed for strategic transportation planning while retaining the tour-based microscopic approach advantages.

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A microscopic demand
model without
statistical noise2020-09-10
Luuk Brederode - DAT.Mobility
(speaker)
Tanja Hardt - Goudappel Coffeng
Bernike Rijksen - DAT.Mobility

2. • Demand modelling: why shift to tour based and microscopic?
• Statistical noise when using a microscopic approach
• Statistical noise elimination technique as implemented in Octavius:
The Tour Based micro simulator in OmniTRANS Transport Planning Software
• Conclusions and recommendations
2
Contents

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Demand modelling:
why shift to tour based and
microscopic?

4. 4
From owning to using a mode
Reach
Flexibility
Potential of usership
What does this mean for demand models:
• Frameworks used in traditional models
limit their usage to on / around the
curve of ownership;
• With increased exploitation of the
potential of usership comes an
increased need for a different type of
demand model.
Reinforcement by MaaS

5. 5
Why are trip based models not
sufficient?
Example: how to model this tour from home > work > shopping > home?
Trip based model
• In the trip based model:
• There is no tour consistency (dependency between end and start location of trips within a tour)
• There is no mode consistency (availability of a mode is based on assumptions on trip level)
• This makes these models unsuitable to evaluate scenario’s on MaaS, CaVs and shared services.
Tour in reality

6. 6
Why are macro models not
sufficient?
Macromodel
(aggregated)
Departure time choice
Destination choice
Mode choice
Trip/tour generator
Population synthesizer
Macromodel
(disaggregated)
Model components
Micromodel
Availability of alternatives
may be dependent on:
Person/Household characteristics
Choices of other people
Choices made earlier

7. 7
Macromodel
(aggregated)
Departure time choice
Destination choice
Mode choice
Trip/tour generator
Population synthesizer
Macromodel
(disaggregated)
Model components
Micromodel
Availability of alternatives
may be dependent on:
Person/Household characteristics
Choices of other people
Choices made earlier
Why are macro models not
sufficient?
Agent has drivers'
license
-AND-
the household has a car
No other household
member is using the car
Car Driver
available only if:
Car Driver

8. 88
Macromodel
(aggregated)
Departure time choice
Destination choice
Mode choice
Trip/tour generator
Population synthesizer
Macromodel
(disaggregated)
Model components
Micromodel
Availability of alternatives
may be dependent on:
Person/Household characteristics
Choices of other people
Choices made earlier
Why are macro models not
sufficient?
There is a person with
drivers’ license in the
household
-AND-
the household has a car
No other household
member is using the car
-AND-
A car driver is available
Car Passenger
Car Passenger
available only if:

9. 999
Macromodel
(aggregated)
Departure time choice
Destination choice
Mode choice
Trip/tour generator
Population synthesizer
Macromodel
(disaggregated)
Model components
Micromodel
Availability of alternatives
may be dependent on:
Person/Household characteristics
Choices of other people
Choices made earlier
Why are macro models not
sufficient?
Agent has a subscription
for the service
Shared car is not in use
by other travellers
Shared car service
Shared car service
available only if:
No private mode was
used for access;
-OR-
Private mode is to be
picked up again

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Statistical noise when using
microscopic approach

11. Microsimulation causes statistical noise….
11
Effect of 180 additional inhabitants in circled area –
microsimulator applied naively
Why microsimulation cannot
be used naively
Effect of 180 additional inhabitants in circled area –
microsimulator within Octavius
Differences in # of car trips
within the City of Almere
400 veh increase
400 veh decrease
Differences in # of car trips
within the City of Almere
400 veh increase
400 veh decrease

12. 12
How microsimulation works (1/2)
Probabilities from
choice model
Cumulative probabilities Cumulatieve
distribution function
1. Apply Choice model
of considered segment
2. Convert results to cumulative distribution function

13. 13
How microsimulation works (2/2)
3. Draw a random value
from 𝑈(0,1)
For each synthetic person in the segment:
0.622
4. Determine the
corresponding alternative
0.622
Car PT

14. There are three reasons why results of this process do not
exactly replicate the choice models outcomes:
1. Quantization error
2. Statistical noise due to randomness
3. Statistical noise due to non-uniqueness
These concepts are explained in the next slides
14
Why microsimulation cannot
be used naively

15. 15
1. Quantization errors
Size of segment #agents % #agents % quantization error
1 (macro model) 0.6 60% 0.4 40% 0.0%
1 1 100% 0 0% 40.0%
2 1 50% 1 50% 10.0%
3 2 67% 1 33% 6.7%
4 2 50% 2 50% 10.0%
5 3 60% 2 40% 0.0%
6 4 67% 2 33% 6.7%
7 4 57% 3 43% 2.9%
8 5 63% 3 38% 2.5%
9 5 56% 4 44% 4.4%
10 6 60% 4 40% 0.0%
Car PT
These quantization errors represent the price you pay (amount of deviation
from the choice models behavior) to be able to use microsimulation

16. 16
2. Randomness
Size of segment #agents % #agents % quantization error
1 (macro model) 0.6 60% 0.4 40% 0.0%
1 1 100% 0 0% 40.0%
2 1 50% 1 50% 10.0%
3 2 67% 1 33% 6.7%
4 2 50% 2 50% 10.0%
5 3 60% 2 40% 0.0%
6 4 67% 2 33% 6.7%
7 4 57% 3 43% 2.9%
8 5 63% 3 38% 2.5%
9 5 56% 4 44% 4.4%
10 6 60% 4 40% 0.0%
Car PT
Neither is drawing 10
random values from
𝑈(0,1) guaranteed to
yield 6 agents choosing
for Car
Drawing 5 random
values from 𝑈(0,1) is not
guaranteed to yield 3
agents choosing Car
Randomness effects occur when the set of random draws to convert
probabilities into discrete choices does not yield the expected value

17. 17
3. Non uniqueness
Size of segment #agents % #agents % quantization error
1 (macro model) 0.6 60% 0.4 40% 0.0%
1 1 100% 0 0% 40.0%
2 1 50% 1 50% 10.0%
3 2 67% 1 33% 6.7%
4 2 50% 2 50% 10.0%
5 3 60% 2 40% 0.0%
6 4 67% 2 33% 6.7%
7 4 57% 3 43% 2.9%
8 5 63% 3 38% 2.5%
9 5 56% 4 44% 4.4%
10 6 60% 4 40% 0.0%
Car PT
In this case (5-1)!=24 different discrete solutions exist,
and they are all optima.
However, in subsequent choice models, these people
may be segmented differently, causing different
outcomes!
Car PT Car PT
Optimal solution 1 Optimal solution 2
Non uniqueness effects occur when different sets of random draws are used to
yield the same expected value

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The statistical noise
elimination technique in
Octavius:
The Tour Based micro simulator in
OmniTRANS transport planning
software

19. • A microsimulator for demand modelling implemented in OmniTRANS transport
planning software
• (Following Vovsha 2019*, one should not call this an agent-based model).
• It currently contains a population synthesizer and discrete choice models for
Tour generation, Destination- and Mode choice
• Choice models are applied on agent level (instead zone/segment level)
• It is a modular framework that allows to add (future) choice models
• It includes a statistic noise elimination technique to remove all randomness
and non uniqueness effects
19
*Vovsha, P., 2019. Decision-Making Process Underlying Travel Behavior and Its Incorporation in Applied Travel Models, in: Bucciarelli, E., Chen, S.-H., Corchado, J.M. (Eds.),
Decision Economics. Designs, Models, and Techniques for Boundedly Rational Decisions. Springer International Publishing, Cham, pp. 36–48. https://doi.org/10.1007/978-3-319-
99698-1_5
What is Octavius?

20. 20
Population
Synthesizer
Synthetic
population
Tour
Generator
Tours per
agent
Trip
Simulator
Trip table
per mode
Model that allocates agents to person and householdtypes using entropy maximization +
Statistical noise Elimination Technique to discretize results
Choice Model that generates activities and their order per agent using random utility maximization (RUM) +
Statistical noise Elimination Technique to discretize results
Choice Model that distributes departing trips within tours over destinations using RUM +
Statistical noise Elimination Technique to discretize results +
Choice model that distributes trip chains over modes/mode combinations using RUM
What is Octavius?

21. 21
Summarized in one sentence:
Pick a single discrete solution that -apart from the quantization error- perfectly
matches the expected value from the choice models outcomes and stick to it in
both reference case and scenarios.
How does the statistical noise
elimination technique work?

22. 1. Quantization error:
» Still remains, but:
• The size of its effects is known and (very) small
• Causes no differences in ceteris paribus situations, due to the solution to 3.
» We foresee a method to minimize it, this is future work
2. Randomness:
» Eliminated by optimizing the set of random draw values used in each choice situation, such that
the expected value from the choice model is exactly met.
» The selected draw value per choice situation becomes a property of the agent reflecting its
‘lifestyle’ preference for that type of choice
3. Non-Uniqueness:
» Eliminated by maintaining ‘lifestyle’ preferences of agents from reference to scenario’s
22
How does the statistical noise
elimination technique work?

23. 23
Octavius – calculation times
Octavius Almere
Component Modeltype Discretisation Calculation time [mm:ss]
Population Synthesizer Max entropy Yes 00:09:41
Tourgenerator Multinomial Logit Yes 00:03:12
Destination choice Multinomial Logit Yes 00:12:55
Mode choice Multinomial Logit No 00:05:48
Total 00:31:36
Computation times* of Octavius applied on the model of Almere (204.000 agents)
(hybrid modelling context, external and through demand modelled by gravity model)
*On a machine with Intel Core i7-8700 @3.70Ghz CPU and 64Gb of RAM

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Conclusions &
recommendations

25. Conclusions
• The trend “from owning to using” asks for a shift from trip- to
tour-based and from macro to micro demand models
• But microsimulation causes statistical noise severely limiting
applicability in the strategic application context
• Octavius’ statistical noise elimination technique fixes this
25
Conclusions &
recommendations

26. Recommendations
The statistical noise elimination technique:
• Uses uniformly distributed random draws per choice situation that reflect an agents
‘lifestyle’ preference. By changing the distribution, sensitivity analysis on the effects of
trends in lifestyle preferences could be done
• Can be applied on any case where micro simulation is applied to a cumulative
distribution function (CDF). CDF’s may come from a model but they could just as well
come from a dataset, making the applicability of the method potentially very large.
• May be extended to minimize the quantization error at a certain aggregation level.
26
Conclusions &
recommendations

27. Questions?
Luuk
Brederode
lbrederode@da
t.nl
+31
627369830

28. Klik om de stijl te bewerken
Backup slides

29. 29
Microsimulation creates statistical noise, visible only on lower
aggregation levels….
Effect of 180 additional inhabitants in circled area –
microsimulator applied naively
Effect of 180 additional inhabitants in circled area –
microsimulator within Octavius
Why microsimulation cannot
be used naively

30. 30
Currently1, 77% of tours in the Netherlands visit only one activity location, whereas
23% of tours visit multiple activity locations. Note that this means that a tour
based model is more accurate for only 23% of the total number of tours.
Tours visiting
one activity
Tours visiting
2+ activities
1Based on data in Dutch national travel survey (OViN) stacked from 2010-2017

31. 31
Destinatino choice models
auto, woninggebonden 2-tour
kenmerken
inw geslacht
motief reistijd ln(kst) parkeertot ind kantwink ov ondtot basis mid mboho inwh 2 3 4 l man <18 18-2930-4545-6465+ Alleen geen k wel k 1 2 3 4 5 6+
werk
zakelijk
winkel
school
socrec
overig
kosten
rit
leeftijdleerlingplaatsen
persoonbestemming
stedelijkheidarbeidsplaatsen
huishouden
samenstelling grootte
autopassagier, woninggebonden 2-tour
kenmerken
inw geslacht
motief reistijd ln(kst) parkeertot ind kantwink ov ondtot basis mid mboho inw h 2 3 4 l man <18 18-2930-4545-6465+ Alleen geen k wel k 1 2 3 4 5 6+
werk
zakelijk
winkel
school
socrec
overig
arbeidsplaatsenkosten leerlingplaatsen
rit bestemming persoon
stedelijkheid grootte
huishouden
leeftijd samenstelling
Negative relation
Positive relation
Insignificant relation
Untested / insufficient data

32. 32
Population synthesizer
Synthetische huis-
Houdens per zone
Totalen p zone1
Distributie
over 30
persoons-
segmenten
(uit OViN)
Totalenpzone1
Synthetische inwoners
per zone
Iterative Proportional fitting
Totalen p zone2
Distributie
over 24
huishoud
segmenten
(uit OViN)
Totalenpzone2
Iterative Proportional fitting
Samenstelling
huishoudens uit
mobiliteitspanel-data
Synthetische
Populatie
per zone
Iterative
Proportional
updating + noise
elimination techniq
1Totalen per zone (persoonsniveau)
• Maatschappelijke participatie (werkend, student,
anders)
• Leeftijdsklasse (0-17, 18-29, 30-44, 45-64, 65+)
• Geslacht (man/vrouw)
2Totalen per zone (huishoudniveau)
• Huishoudgrotte (1-6+ personen)
• Aantal autos in huishouden (0-3+)
Population
Synthesizer
Synthetic
population
Tour
Generator
Tours per
person
Trip
Simulator
Trip table
per mode

33. TourGenerator
• Elk genummerd blokje is een multinomial logit model
• Alle modellen zijn geschat op nationale OViN data 2010-2017
Population
Synthesizer
Synthetic
population
Tour
Generator
Tours per
person
Trip
Simulator
Trip table
per mode

34. 34
Destination choice model
Population
Synthesizer
Synthetic
population
Tour
Generator
Tours per
person
Trip
Simulator
Trip table
per mode
Multinomial logit model dat de kans op bestemming i bepaald,
gegeven het vorige reeds bepaalde punt h and het volgende
te bereiken punt j:
𝑃𝑖|ℎ,𝑗 =
exp(𝑉𝑖|ℎ,𝑗)
𝑖′ exp(𝑉𝑖′|ℎ,𝑗)
Met utiliteit:
𝑉𝑖|ℎ,𝑗 = 𝛽 𝑡ℎ𝑖 + 𝑡𝑖𝑗 + ln(𝑚𝑖)
waarin 𝑡ℎ𝑖: reistijd van ℎ tot 𝑖
𝑡𝑖𝑗: reistijd van 𝑖 tot 𝑗
𝑚𝑖: socio/economische activiteiten op 𝑖
𝛽: parameter per combinatie:
(ℎ𝑡𝑦𝑝𝑒, 𝑖𝑡𝑦𝑝𝑒, 𝑗𝑡𝑦𝑝𝑒, 𝑎𝑚𝑜𝑑𝑒, 𝑏𝑚𝑜𝑑𝑒)
ℎ = 𝑗 𝑖
stap 1
𝑗 ℎ
stap 2
𝑖
Resultaat
bestemmingskeuze

35. 35
Mode choice model
Population
Synthesizer
Synthetic
population
Tour
Generator
Tours per
person
Trip
Simulator
Trip table
per mode
Multinomial logit model dat de kans op mode 𝑚 bepaald,
gegeven de gekozen ritketen 𝑐 per modaliteit uit het
bestemmingskeuzemodel
𝑃 𝑚|𝑐 =
exp(𝑉 𝑚|𝑐)
𝑚′ exp(𝑉 𝑚′|𝑐)
Met utiliteit:
𝑉 𝑚|𝑐 = 𝛽 𝑚1 𝑡 𝑐,𝑚 +𝛽 𝑚2 𝑋 𝑚2+. . +𝛽 𝑚𝑛 𝑋 𝑚𝑛 + logsumc,m
Waarin: 𝑡 𝑐,𝑚: totale reistijd voor realisatie ritketen 𝑐 met
mode 𝑚
𝑋 𝑚2. . 𝑋 𝑚𝑛: verklarende variabelen (reistijd ratio’s,
autobeschikbaarheid, …)
𝑙𝑜𝑔𝑠𝑢𝑚 𝑐,𝑚: gemiddelde aantrekkelijkheid van de
bestemmingen in 𝑐
𝑙𝑜𝑔𝑠𝑢𝑚 𝑐,𝑚 = 1/𝑀 𝑖=1..𝑛 exp(𝑉𝑖|ℎ,𝑗)
𝛽 𝑚1. . 𝛽 𝑚𝑛: parameters
Illustratief voorbeeld: er
wordt tussen gehele ketens
gekozen!