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Development of a microscopic tour based demand model without statistical noise
- 1. Klik om de stijl te bewerken A microscopic demand model without statistical noise2020-09-10 Luuk Brederode - DAT.Mobility (speaker) Tanja Hardt - Goudappel Coffeng Bernike Rijksen - DAT.Mobility
- 2. β’ Demand modelling: why shift to tour based and microscopic? β’ Statistical noise when using a microscopic approach β’ Statistical noise elimination technique as implemented in Octavius: The Tour Based micro simulator in OmniTRANS Transport Planning Software β’ Conclusions and recommendations 2 Contents
- 3. Klik om de stijl te bewerken 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: β’ Frameworks used in traditional models limit their usage to on / around the curve of ownership; β’ 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 β’ In the trip based model: β’ There is no tour consistency (dependency between end and start location of trips within a tour) β’ There is no mode consistency (availability of a mode is based on assumptions on trip level) β’ This makes these models unsuitable to evaluate scenarioβs 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 driversβ 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
- 10. Klik om de stijl te bewerken Statistical noise when using microscopic approach
- 11. Microsimulation causes statistical noiseβ¦. 11 Effect of 180 additional inhabitants in circled area β microsimulator applied naively Why microsimulation cannot be used naively Effect of 180 additional inhabitants in circled area β 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 π(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 π(0,1) guaranteed to yield 6 agents choosing for Car Drawing 5 random values from π(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
- 18. Klik om de stijl te bewerken The statistical noise elimination technique in Octavius: The Tour Based micro simulator in OmniTRANS transport planning software
- 19. β’ A microsimulator for demand modelling implemented in OmniTRANS transport planning software β’ (Following Vovsha 2019*, one should not call this an agent-based model). β’ It currently contains a population synthesizer and discrete choice models for Tour generation, Destination- and Mode choice β’ Choice models are applied on agent level (instead zone/segment level) β’ It is a modular framework that allows to add (future) choice models β’ 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. 36β48. 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: Β» Still remains, but: β’ The size of its effects is known and (very) small β’ Causes no differences in ceteris paribus situations, due to the solution to 3. Β» We foresee a method to minimize it, this is future work 2. Randomness: Β» 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. Β» The selected draw value per choice situation becomes a property of the agent reflecting its βlifestyleβ preference for that type of choice 3. Non-Uniqueness: Β» Eliminated by maintaining βlifestyleβ preferences of agents from reference to scenarioβs 22 How does the statistical noise elimination technique work?
- 23. 23 Octavius β 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
- 24. Klik om de stijl te bewerken Conclusions & recommendations
- 25. Conclusions β’ The trend βfrom owning to usingβ asks for a shift from trip- to tour-based and from macro to micro demand models β’ But microsimulation causes statistical noise severely limiting applicability in the strategic application context β’ Octaviusβ statistical noise elimination technique fixes this 25 Conclusions & recommendations
- 26. Recommendations The statistical noise elimination technique: β’ Uses uniformly distributed random draws per choice situation that reflect an agents βlifestyleβ preference. By changing the distribution, sensitivity analysis on the effects of trends in lifestyle preferences could be done β’ Can be applied on any case where micro simulation is applied to a cumulative distribution function (CDF). CDFβs may come from a model but they could just as well come from a dataset, making the applicability of the method potentially very large. β’ May be extended to minimize the quantization error at a certain aggregation level. 26 Conclusions & recommendations
- 28. Klik om de stijl te bewerken Backup slides
- 29. 29 Microsimulation creates statistical noise, visible only on lower aggregation levelsβ¦. Effect of 180 additional inhabitants in circled area β microsimulator applied naively Effect of 180 additional inhabitants in circled area β 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) β’ Maatschappelijke participatie (werkend, student, anders) β’ Leeftijdsklasse (0-17, 18-29, 30-44, 45-64, 65+) β’ Geslacht (man/vrouw) 2Totalen per zone (huishoudniveau) β’ Huishoudgrotte (1-6+ personen) β’ Aantal autos in huishouden (0-3+) Population Synthesizer Synthetic population Tour Generator Tours per person Trip Simulator Trip table per mode
- 33. TourGenerator β’ Elk genummerd blokje is een multinomial logit model β’ 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: ππ|β,π = exp(ππ|β,π) πβ² exp(ππβ²|β,π) Met utiliteit: ππ|β,π = π½ π‘βπ + π‘ππ + ln(ππ) waarin π‘βπ: reistijd van β tot π π‘ππ: reistijd van π tot π ππ: socio/economische activiteiten op π π½: parameter per combinatie: (βπ‘π¦ππ, ππ‘π¦ππ, ππ‘π¦ππ, πππππ, πππππ) β = π π stap 1 π β stap 2 π 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 π bepaald, gegeven de gekozen ritketen π per modaliteit uit het bestemmingskeuzemodel π π|π = exp(π π|π) πβ² exp(π πβ²|π) Met utiliteit: π π|π = π½ π1 π‘ π,π +π½ π2 π π2+. . +π½ ππ π ππ + logsumc,m Waarin: π‘ π,π: totale reistijd voor realisatie ritketen π met mode π π π2. . π ππ: verklarende variabelen (reistijd ratioβs, autobeschikbaarheid, β¦) ππππ π’π π,π: gemiddelde aantrekkelijkheid van de bestemmingen in π ππππ π’π π,π = 1/π π=1..π exp(ππ|β,π) π½ π1. . π½ ππ: parameters Illustratief voorbeeld: er wordt tussen gehele ketens gekozen!