1. Micro-simulation and visualization of individual space-time paths within a GIS A bouquet of alternatives (G) Arnaud Banos, Pau University/CNRS, France (CS) Bruno Jobard, Pau University, France (S) Sylvain Lassarre, INRETS, France (CS) Julien Lesbegueries, Pau University, France (G) Pierpaolo Mudu, WHO, Italy (CS) Karine Zeitouni, Versailles University, France G : Geographer ; CS : Computer Scientist ; S : Statistician 2005 Annual Meeting of the Association of American Geographers, Denver, Colorado, April 5-9

2. Contents Urban daily mobility  Simulation  “What if.. ?” scenarios Hägerstrand conceptual framework  Monte-Carlo approach to diffusion : Macro level  Time-Geography : Micro level From concepts to methods and techniques

3. “A Monte-Carlo approach to urban rythms” Monte-Carlo Banos & Thévenin, 2001 O/D matrix (time period, mode, activity) GIS

4. Limits Global view of urban “pulses” based on a very segmented approach of mobility :  focused on independent activities  loosing trip chaining  loosing the very basic dimension of urban systems : INDIVIDUALS

5. Time Geography Space-time cube Space-time path Trip chaining

6. Typical data available in France 2 3 4 1 Lille : 1 million inhabitants 13000 sample survey Can we simulate their space-time paths ? 08:00 Zone 1 08:10 Zone 2 08:35 Zone 3 08:38 Zone 3

7. Generic problem in Monte-Carlo simulation of individual daily space-time activities Simulating activity scheduling by picking at random in time distributions, under flexible spatial constraints, to ensure global trends to be respected (O/D matrix)

8. A systematic Time Geographic approach

9. Potential Path Area [Miller, 2003]

10. Potential Path Area

11. 10000 cells Network : 100 000 nodes Area : 30 km 2

12. From Land use to probability Field 25000 objects Network : 100 000 nodes Area : 30 km 2

13. Various probability fields Residences : RPF Work places : WPF Shops : SPF

14. Zone 1Zone 2 Zone 1 RPFWPF RPFSPF Cells Z11 Z12 Z13 Z14 … Z1n P P11 P12 P13 P14 … P1n Cells Z21 Z22 Z23 Z24 … Z2n P P21 P22 P23 P24 … P2n Cells Z11 Z12 Z13 Z14 … Z1n P P11 P12 P13 P14 … P1n Z13 Shortest path R P [(t1, t2, t3, tn) = T1+-  ] t1 t2 tn t1 t2 tn t1 t2 tn R{[(t1, t2, t3, tn) = T2+-  ] INTERSECT [(t, t2, t3, tn) = T3+-  ]} R P (Z11, Z12, Z13, Z1n) 08:0008:10 17:4519h H WSH T1T2T3 17h3018:30

15. Perspectives Straightforward translation of concepts into methods HUGE COMPUTATION BURDEN !!! (10 000 cells, 100 000 nodes)

16. A swarming approach

17. Stigmergy Food Ants Nest Ants Pheromones Trail Netlogo http://ccl.northwestern.edu/netlogo/

19. Prototype Zone 1 Zone 2 Zone 3 Zone 4 Forward Ants Backward Ants Tour to realize : Z2 --> Z3 --> Z4 --> Z2 Distances to respect : 30 --> 30 --> 44

20. Pheromone trail Swarming Algorithm (Dorigo, 1996) Locate N/2 forward and N/2 backward ants on node i in Zone m=0 Each ant k :  Move at time t to a connected node j using a probabilistic action choice rule : Feasible neighbourhood of ant k ant node i Random proportional rule

21. Reinforcement learning scheme to favour better solutions Pheromones decay parameter (0<  <1) Amount of pheromones at edge ij Updating pheromones trails Pheromones = pheromones deposit – pheromones evaporation

22. Actual situation (debugging !)

23. What comes next ?

24. GeoVisualisation ? Mei-Po Kwan, 2000

25. A bouquet of alternatives based on mobile objects GIS : Grass, Postgis (PostgreSQL) Visualization : VTK Banos, Jobard, Lesbegueries (ICC 2005)

26. Applications ?

27. T 1 -T 3 X Y Time (T) T 3 – T 5 T1T1 T2T2 Origin Destination T3T3 T3T3 T4T4 Origin Destination T5T5 Exposure of citizens to urban transport hazards Tomorrow afternoon : Session 5505, Applied Transportation Research Projects Sylvain LASSARRE (5:05)

28. Simulation of Artificial Urban Life MIRO project, French Ministry of Transportation Agent Based Modelling :  Heterogeneous cognitive agents (Von BDI)  Limited knowledge (CFOS) and computation capacities  Interacting locally with their urban environment and with other agents  Having to program their daily calendar of activities and to perform their activities in a moving urban environment (traffic conditions, other agents, time schedule of urban opportunities, public transport availability…) Goal : testing “what if…?” scenarios by modifying the opportunity constraints at a global level (public transport, opening/closing time of public services, schools, universities, shops…) : leave the system show us how agents react to these various time geographic constraints (capacity, conjunction, authority constraints) MORE at CUPUM’05, London

29. Perspectives Applying Time Geography is still a challenge… …what is more when dealing with large populations ! Various methodological and technological translations, and more to be invented ! No one best way ! (Herbert Simon) Time Geo is still alive and remains a major concern!

30. Links HEARTS  http://www.euro.who.int/hearts MIRO  http://lifc.univ-fcomte.fr/~lang/MIRO Animations  Http://www.univ-pau.fr/~banos/banos.html