Micro-simulation and visualization of individual space-time paths within a GIS A bouquet of alternatives (G) Arnaud Banos, Pau University/CNRS, France.

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

Contents

Urban daily mobilitySimulation“What if.. ?” scenarios

Hägerstrand conceptual frameworkMonte-Carlo approach to diffusion : Macro

levelTime-Geography : Micro level

From concepts to methods and techniques

“A Monte-Carlo approach to urban rythms”

D3

D3

O3O3

D2 D1

O2O2

O1O1

D2D1[T1]

D3

D3

O3O3

D2 D1

O2O2

O1O1

D2D1[T1]

Monte-Carlo

Banos & Thévenin, 2001

O/D matrix (time period, mode, activity)

GIS

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

Time Geography

Space-time cube

Space-time path

Trip chaining

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

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)

A systematic Time Geographic approach

Potential Path Area

[Miller, 2003]

Potential Path Area

10000 cells

Network :100 000 nodes

Area :30 km2

From Land use to probability Field

25000 objects

Network :100 000 nodes

Area :30 km2

Various probability fields

Residences : RPF Work places : WPF Shops : SPF

Zone 1 Zone 2 Zone 1 Zone 1

RPF WPF RPFSPF

CellsZ11Z12Z13Z14…

Z1n

PP11P12P13P14…

P1n

CellsZ21Z22Z23Z24…

Z2n

PP21P22P23P24…

P2n

CellsZ11Z12Z13Z14…

Z1n

PP11P12P13P14…

P1n

Z13

Shortest path

RP[(t1, t2, t3, tn) = T1+- ]

t1t2

tn

t1t2

tn

t1t2

tn

R{[(t1, t2, t3, tn) = T2+- ] INTERSECT [(t, t2, t3, tn) = T3+- ]}

RP(Z11, Z12, Z13, Z1n)

08:00 08:10 17:45 19h

H W S HT1 T2 T3

17h30 18:30

Perspectives

Straightforward translation of concepts into methods

HUGE COMPUTATION BURDEN !!! (10 000 cells, 100 000 nodes)

A swarming approach

Stigmergy

Food

Ants Nest

Ants

Pheromones Trail

Netlogohttp://ccl.northwestern.edu/netlogo/

Prototype

Zone 1

Zone 2 Zone 3

Zone 4

Forward Ants

Backward AntsTour to realize :Z2 --> Z3 --> Z4 --> Z2Distances to respect : 30 --> 30 --> 44

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 :

ki

Nlijij

ijijkij Nj

t

ttp

ki

if ][)]([

][)]([)(

ijd

1

Feasible neighbourhood

of ant k ant node i

Random proportional rule

Reinforcement learning scheme to favour better solutions

Pheromones decay parameter (0<<1)

Amount of pheromones at edge ij

Updating pheromones trails

m

k

kijijij

1

)1(

otherwise 0

0 and ant by done tour if 1

0 and ant by done tour if 1

wherem

kij

mkij

mkij

kij cumdcumdk(ij)

cumdcumdk(ij)cumdcumd

Pheromones = pheromones deposit – pheromones evaporation

Actual situation (debugging !)

What comes next ?

GeoVisualisation ?

Mei-Po Kwan, 2000

A bouquet of alternatives based on mobile objects GIS : Grass, Postgis (PostgreSQL) Visualization : VTK

Banos, Jobard, Lesbegueries (ICC 2005)

Applications ?

T1-T3

X

Y

Tim

e (

T)

T3 – T5

T1

T2OriginDestination

T3

T3

T4Origin Destination

T5

Exposure of citizens to urban transport hazards

Tomorrow afternoon : Session 5505, Applied Transportation Research ProjectsSylvain LASSARRE (5:05)

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

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!

Links

HEARTShttp://www.euro.who.int/hearts

MIROhttp://lifc.univ-fcomte.fr/~lang/MIRO

AnimationsHttp://www.univ-pau.fr/~banos/banos.html