micro-simulation and visualization of individual space-time paths within a gis a bouquet of...
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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