Preferred citation style for this presentation

1. Preferred citation style for this presentation A. Horni and F. Ciari (2009) Modeling Shopping Customers & Retailers with the Activity-based Multi-agent Transport Simulation MATSim, CCSS Seminar, Zurich, April 2009.

2. Modeling Shopping Customers & Retailers with the Activity-based Multi-agent Transport Simulation MATSim:- Customer Shopping Location Choice Retailer Location Choice Status Update & Open Questions F. Ciari & A. Horni IVT, ETH Zurich

3. MATSim: Model Purpose Link loads Travel distances Travel times … Location choice: Catchment area Number of visitors … Purpose: Model patterns of people’s activity scheduling and participation behavior at high level of detail. Dynamic, Disaggregated Method: Coevolutionary, agent-based algorithm adopted Goal: Average working day of Swiss resident population (> 7.5 M) in „reasonable“ time → 2009 (KTI project)

4. MATSim: Structure → Iterative 4 Fixed attributes e.g. home location, census data Utility function f (t): activity participation, travel Agents‘ day plans Exit conditon: „Relaxed state“, i.e. equilibrium initial demand execution scoring analyses Share x of agents (usually 10%): Time, route, location choice replanning

5. MATSim: Structure initial demand execution scoring analyses replanning 5 Fixed attributes e.g. home location, census data Utility function f (t): activity participation, travel Agents‘ day plans Exit conditon: „Relaxed state“, i.e. equilibrium replanning Share x of agents (usually 10%): Time, route, location choice

6. MATSim: Structure 6 replanning Controler → Replanning for all persons who are chosen to make replanning Nash eq. = stable state in sim → Exit condition StrategyManager person.removeWorstPlan(); strategy.run(person); Local opt. ≠ Nash eq. → Probabilistic p(score) e.g. selectExpBeta: selection dependent on plan score PlanStrategy this.planSelector.selectPlan(); person.copySelectedPlan(); this.plans.add(plan); PlanStrategyModule.handlePlan(plan); i.e., route, time or location choice

7. MATSim - Coevolution Agent i Plan 0 Plan 2 species i Individ. … Individ. j,0 Individ. i,3 Individ. i,2 Individ. i,1 Individ. i,0 Plan 1 Plan 3 Plan .. Plan 0 Plan 0 Plan 2 Plan 3 Plan 1 Agent j Plan 0 Plan 2 species j Plan 1 Plan 3 Agent … Plan .. Interpretation Competition on the infrastructure → Score (fitness) Weakest die → Generations person.removeWorstPlan() Individual learning Coevolution: „The evolution of two or more interdependent species, each adapting to changes in the other. […]“ The American Heritage Dictionary of the English Language Evolution: „(Biology): Change in the gene pool of a population from generation to generation by such processes as mutation, natural selection, and genetic drift. […]“„www.dictionary.com“ 7

8. What are we searching & How are we searching? Planning equilibrium Iterate until „relaxed state“ is reached ≠ Network equilibrium Wardrop (tt) Equilibrium =? Nash Multilateral (but uncoordinated) changes in a replanning step! → coalition-proof eq.? 8

9. What are we searching & How are we searching? Iterate until „relaxed state“ is reached ≠ Network equilibrium Wardrop (tt) Planning equilibrium Equilibrium =? Nash Multilateral changes in a replanning step But: Not coordinated. → No coalition-proof eq. Non-cooperative, coordination game (non-zero-sum)

10. What are we searching & How are we searching? Mixed Pure Battle of the sexes Equilibrium Store 0 Existence Player 1 Uniqueness • 0,0 • a,b Store 1 • c,d • 0,0 Non-cooperative, coordination game (non-zero-sum) ? ? Yes? Yes Existence conditions No Selection of plan for execution: p(score) →mixed │agent‘s memory│ << #possible strategies of an agent Score and hence p not stable between iterations ? Refinements? Closest to SO? Largest basin of attraction? … Player 0 Results: Prob. of e.g. link volumes Results: e.g. link volumes 10

11. What are we searching & How are we searching? Search space prohibitively large to be searched exhaustively or – even worse – globally at random. #locations#activities × tper iteration. Idea: Adapt local search techniques from optimization (e.g. simulated annealing). 11

12. Location Choice in MATSim - Status First results: Local search Improvement of realism by competition on the activities infrastructure Future work: Impr. local search & capacity restraints Validation Utility function extension Analyzing existence and uniqueness of solution

13. Day plan Aktivity i - Work Location Start time, duration … Location Set: Locations consistent with time choice (ttravel ≤ tbudget) Aktivity i+1 - Shopping Duration Travel time budget Aktivity i+2 - Home Location Start time, duration … Local Search Adopted to Coevolutionary Systems Tie together location choice and time choice (Dt) p(accept bad solutions) > 0 Time Geography Hägerstrand

14. Based on PPA-Algorithm Scott, 2006 „Implicit choice set“ Chains of consecutive shopping activities r = tbudget/2 * v Check all locations ttravel ≤ tbudget → choice set Random choice Check ∑ttravel ≤ tbudget 14

15. Location Choice in MATSim - Status First results: Local search Improvement of realism by competition on the activities infrastructure Future work: Impr. local search & capacity restraints Validation Utility function extension Analyzing existence and uniqueness of solution

16. Time-dependent capacity Penalty funtion Power function (cost-flow function for roads, BPR) ... a(load/capacity)ba,b: parameters Competition on Activities Infrastructure Micro-census 2005 16

17. Simulation Scenario • Region Zurich: 30 km radius circle, center Bellevue; 10% sample Initial location assignment: Region Zurich

18. Simulation Scenario - Configurations

21. Results – Shopping Facility Load

22. Shopping Facility Load 22

23. Results – Computation Times s/it (500 iterations)

24. Location Choice in MATSim – Conclusions I First results: Local search: Local search productive (w/ same computational effort per iteration). Competition on the activities infrastructure: Balanced facility load → number of implausibly overloaded facilities reduced

25. Location Choice in MATSim – Conclusions II Future work: Local search Improvements + extended analysis Activity location competition Diversification of capacity restraint functions Utility function extension Influencing factors + activity classification Validation Counting data, GPS (FCD), Micro census Analyzing existence and uniqueness of computed solution Exit condition Application of estimated models Hypothesis testing

26. Introducing retailer agents in the MATSim toolkit and modeling their location choices 26

27. Who are Retailers in MATSim? fromwww.wikipedia.org: Retailer: “In commerce, a retailer buys goods or products in large quantities from manufacturers or importers, either directly or through a wholesaler, and then sells smaller quantities to the end-user.“ In MATSim: Retailer: “Person or entity having the control on one or more shopping facilities” 27

28. Motivations & Tasks Motivations: First step to a fully agent-based representation of the system Correctly predict the location choices of retailers under a given policy scenario Estimate a benchmark value for retailers (# customers, turnaround, etc…) under a given policy scenario Tasks: Define/implement retailer agents in the MATSim framework Enrich individual agents (customer aspect) 28

29. Individual Agent Framework Individual Agent Current MATSim Next Stage • Personal attributes • Age • Gender • Home location • Work location • Driving License • Car availability • Transit tickets ownership • Location choice methodology • Not optimized • Objective function • Time based • Knowledge • Memory of previous plans (score) • Income • Household • Optimized in time and space • Utility based with budget constraints • Shop Attributes (Price, Quality, Parking, etc.) 29

30. Individual Agent Framework Individual Agent Current MATSim Next Stage • Personal attributes • Age • Gender • Home location • Work location • Driving License • Car availability • Transit tickets ownership • Location choice methodology • Optimized in time and space • Objective function • Time based • Knowledge • Memory of previous plans (score) • Income • Household • Utility based with budget constraints • Shop Attributes (Price, Quality, Parking, etc.) 30

31. Importance of Location for Retailers 31

32. Common Methods and Tools in Retail Location Planning Adapted from Hernandez and Benninson, 2000 32

33. Practice in Location Choices Extensive literature research 11 explorative interviews accomplished in Germany and Switzerland in 2008 Results: Location strategies vary both between and within different retail sectors Location choices are still heavily based on experience and intuition, particularly those decisions at the micro scale Simpler methodologies are still predominant, more sophisticated are sometimes used as a posterior confirmation 33

34. Retailer Agent Framework Retailer Agent • Attributes • Type • Facility portfolio • Price level • … • Location choice methodology • Market ratio • Catchment area • Checklists • Objective • Max. Customers • Max. Revenue • Max. Market share • Knowledge • Customers • Competitors • Land Prices • Land use regulation 34

35. Retailer Agent Framework Retailer Agent • Attributes • Type • Facility portfolio • Price level • … • Location choice methodology • Market ratio • Catchment area • Checklists • … • Objective • Max. Customers • Max. Revenue • Max. Market share • Knowledge • Customers • Competitors • Land Prices • Land use regulation 35

36. Retailer agents – Relocation steps Yes Stay on the current link N E X T I T E R A T I O N The retailer already owns a shop in this area No The ratio # residents / #shops in the new area is higher No Yes No The daily traffic volume on the new link is higher Yes Move to the new link A new random link is proposed Global search Refinements 36

37. Simulation Inputs and Parameters Inputs: Retailers file: List of retailer agents and shop facilities controled by them Links file Links allowed for the relocation of shop facilities Parameters: Frequency of retailers relocation Catchment area dimension 37

38. MATSim: Actual framework initial demand execution scoring analyses replanning 38 Fixed attributes Utility function f (t) Exit conditon: „Relaxed state“, i.e. equilibrium replanning Share x of agents (usually 10%): Time, route, location choice

39. MATSim: Framework with Retailer Agents 39 Fixed attributes Utility function f (t) initial demand execution scoring analyses Exit conditon: „Relaxed state“, i.e. equilibrium replanning Retailers replanning Retailer agents‘ facilities: Location choice

40. Results: Simulation Scenario - Region Zurich Number of shops relocating: 80 40

41. Issues and possible solutions • Simulations with different combination of input parameters: No relaxation is observed • Real Optimization Technique (e.g. SA)? • Same story as before: Search space prohibitively large ... • Alternative: Adapt local search techniques … • In each iteration • Outer loop → Replanning of person agents → relaxed state → local search 41

42. Customers & retailers – Coevolution Agent i Plan 0 Plan 2 species i Individ. … Individ. i,3 Individ. … Individ. u,0 Individ. v,0 Individ. i,1 Individ. i,2 Individ. j,0 Individ. … Individ. i,0 Plan 1 Plan 3 Plan 2 Plan 3 Plan 1 Plan .. Plan 0 Plan .. Plan 0 Plan .. Plan 0 Plan 0 Agent j Agent u Agent v Plan 0 Plan 0 Plan 0 Plan 2 Plan 2 Plan 2 species v species u species j Plan 1 Plan 1 Plan 1 Plan 3 Plan 3 Plan 3 retailer.removeWorstPlan() Interpretation Weakest die → Generations person.removeWorstPlan() - - - - - - - - - 42

43. Main limitations The land market is not represented Introduction of monetary costs for activities and taking into account prices for them Retail shops are undifferentiated Persons behavior on Saturday is different than during the week -> Simulating only Mo-Fr retailers’ location decision are biased …

44. Conclusions and future work Conclusions: The new retailer agents have been introduced in MATSim, but their behavior still has many limitations and a strategy to produce meaningful and easily interpretable results hasn‘t been found yet Future work: Try to use a local search Define an exit condition Overcome some of the limitations (e.g. take into account different types of retail shops, account for monetary costs, etc…)

45. THANK YOU FOR YOUR ATTENTION ! MATSim project page: www.matsim.org Further publications: http://www.ivt.ethz.ch/vpl/publications/reports 45

46. Activity Classification & Utility Function Micro census (e.g. shopping) • Alternative (Store) • This summer (data set available) • Store size, local store density • Later: • Parking, Product range, Price level Person Age, Income,Education Situation … Status quo Next steps Activity classification shop, leisure Utility function Time & route choice Time Store load – store capacity (Shopping) location choice