MATSim Destination Choice for Shopping and Leisure Activities

MATSim User Meeting 2012, Berlin

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Topics: Destination Choice in MATSim …2

now: future: filling the gaps between small-scale choice models and large-scale microsimulations

→ how fancy can the models be?→ which data are required?

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V + implicit

+ explicit

MATSim Destination Choice

• fixed initial random seed• freezing the generating order of ij

• storing all ij

destinations

persons

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nn

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iji

j

personi alternativej

store seed ki store seed kj

regenerate ij on the fly with random seed f(ki,kj)

one additional random number can destroy «quench»

i,j ~ O(106) -> 4x1012Byte (4TByte)

Repeated Draws: Quenched vs. Annealed Randomness4

tdeparture tarrival

Dijkstra forwards 1-n Dijkstra backwards 1-n

approximation

probabilistic choice

search space

work homeshopping

Search Space Optimum5

Filling the Gaps6

• future study (IATBR, STRC)

• crux of matter in practice → study will be exemplified for specific microsimulation and data (MATSim and Swiss data)

• triumvirate of sources for gap:• I. lack of necessary data• II: computational issueso III: lack of recent progress in implementation/application

State-of-Art Theoretical Destination Choice Models7

• broad range of disciplines such as transport and urban planning, marketing and retailing science, economics, geography, psychology.

• attributes/choice determinants: • examples:

• standard attributes: • prices & incomes

• no comprehensive literature review yet

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State-of-Art Microsimulation Destination Choice Models8

person•age, gender, mobility tools, occupancy, home loc (ha), work loc (municipality)•act chain randomly assigned from microcensus

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destination•location (ha), open times, rough type (h, w, s, l, e)

validation•counts

→ relatively limited set of attributesother microsims → similar but no comprehensive literature review yet available

I. Lack of Data: Available Data Switzerland

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main data sets, complete Switzerland:•census of population (full survey) -> population / person attributes•microcensus (person sample) -> demand•business census (hectare) -> supply (infrastructure)•miv counts (lane) -> validation

only locally available (canton, municipality, city)•pt schedules and lines (*)•parking supply (*)•green times (*)•open times (*)

(*) ZH scenario only

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I. Lack of Data:“Missing”

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“missing” attributes in MATSim•incomes •vot•activity / shop subtypes•size categories (shop)•education•number of employees

•store price level•store hours (complete CH)•parking prices

I. Lack of Data:“Missing”

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• Should a manual data collection effort be undertaken?

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transferability /flexibility

collection costs

local sim quality

detail level of data

II. Computational Issues:Choice Sets … Variability

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• huge destination choice sets, in particular routing very expensive (for assessing an alternative)

• microsimulation stochasticity:• no data → sophisticated rolling of a dice + correlations

→ model is not restrained→ large variability → many runs

required

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Research Avenues13

• further heterogeneity of agents and alternatives, in particular, prices, income, vot (household budget survey)

• finer activity classification• available in microcensus and business census

• spatial correlations• agglomeration terms + correlated error terms

• choice dimensions interrelation• e.g., mode – destination etc.

Closing Gaps: Destination Choice Research Avenues13

Research Avenues14

• interaction effects at destinations (e.g., at parking lots)• similar to space-time competition on roads

Closing Gaps: Destination Choice Research Avenues14

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5000

10000

15000

20000

25000

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Load category

Vis

ito

rs it_0_config2/3

it500_config2

it500_config3

Load category1: 0 – 33 %2: 33 - 66 %3: 66 - 100 %4: > 100%

10 % ZH Scenario: 60K agents

reduces number of implausibly overloaded facilities

• destination choice equilibratione.g., approximate calculation of travel times

• errtot = w err + w err + wtt errtt • if (wtt = small) -> tt can be approximated• equilibrium concept is approximate itself