1 1 MUSCAMAGS Project Progress Report June2006 Prof. Bernard Moulin in collaboration with the MUSCAMAGS team 2 The MUSCAMAGS Project Multi-Scale MAGS Funded by GEOIDE,the Network of Centers of excellence in Geomatics (April 05- March09) 6 researchers Laval Univ.: Bédard, Moulin, Thériault, Wilfried Laurier Univ.: Doherty, McMaster Univ.: Scott, Queens Univ: Harrap. Current partners: RDDC Valcartier, NSim Technology, Alberta Sustainable Resource and Development, Center for Spatial Analysis (Mc Master Univ.), CRAD (Univ. Laval), Ministère des transports du Québec, Ministère des ressources naturelles du Québec, Ministère de la sécurité publique du Québec, Sûreté du Québec, Processus Network, Quebec SOPFEU, Ville de Québec 3 Multi-Actor Dynamic Spatial Situations MADSSs involve a large number of actors of different types (human, animal, etc.) acting in geographic spaces of various extents MADSSs need to be monitored to insure : human security and equipment preservation (flood, earthquake, wildfire, oil slicks), the respect of public order (population evacuation, crowd monitoring and control, peace-keeping, etc.) the adequate use of infrastructures (monitoring of people and households transportation and shopping habits in a urban area to better plan transportation infrastructures, location of services’ and retailers, etc.) Impact of emergency response plans 4 MADSS (continued) Certain MADSSs occur on a regular basis (ex. daily traffic patterns in a urban area) whereas other MADSSs can evolve rapidly as a consequence of the occurrence of particular events and/or changes in individual behaviors (often in crisis situations) Certain MADSSs may occur within the context of another MADSS of larger extent Decision makers need an overall understanding of the situation to monitor its evolution, to develop strategies to adequately intervene, to develop and compare alternative intervention scenarios and to anticipate the consequences of these interventions
Transcript
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MUSCAMAGS ProjectProgress Report
June2006
Prof. Bernard Moulinin collaboration with
the MUSCAMAGS team
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The MUSCAMAGS ProjectMulti-Scale MAGS Funded by GEOIDE,the Network of Centers of excellence in Geomatics (April 05- March09)6 researchers
Queens Univ: Harrap.Current partners: RDDC Valcartier, NSim Technology, Alberta Sustainable Resource and Development, Center for Spatial Analysis (Mc Master Univ.), CRAD (Univ. Laval), Ministère des transports du Québec, Ministère des ressources naturelles du Québec, Ministère de la sécurité publique du Québec, Sûreté du Québec, Processus Network, Quebec SOPFEU, Ville de Québec
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Multi-Actor Dynamic Spatial Situations MADSSs involve a large number of actors of different types (human, animal, etc.) acting in geographic spaces of various extentsMADSSs need to be monitored to insure :
human security and equipment preservation (flood, earthquake, wildfire, oil slicks), the respect of public order (population evacuation, crowd monitoring and control, peace-keeping, etc.)the adequate use of infrastructures (monitoring of people and households transportation and shopping habits in a urban area to better plan transportation infrastructures, location of services’ and retailers, etc.)Impact of emergency response plans
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MADSS (continued) Certain MADSSs occur on a regular basis (ex. daily traffic patterns in a urban area) whereas other MADSSs can evolve rapidly as a consequence of the occurrence of particular events and/or changes in individual behaviors (often in crisis situations)Certain MADSSs may occur within the context of another MADSS of larger extentDecision makers need an overall understanding of the situation to monitor its evolution, to develop strategies to adequately intervene, to develop and compare alternative intervention scenarios and to anticipate the consequences of these interventions
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MUSCAMAGS Global Objective and IssuesObjective: To develop a methodology and a software platform to
create multi-scale multi-agent geo-simulations to support operational decison support systems for MADSSs
Several important issues need to be tackled : Construction of multi-level, multi-resolution world models with reliable physical terrain models and contextual semantic attribution (informed environment)Development of the MUSCAMAGS Platform, of the Scenario Specification Module (behavior specification, scenario specif., observation specification)Use of real population data and creation of plausible and significant agent populations (+ Persistency) Monitoring of the situation evolution (Update of the simulation data and anticipation of the consequences) Simulation and assessment of different courses of actionsExtraction / Exploitation of simulation results (based on users’ needs) in order to compare different scenarios Introduction of physical data (ex. fire spreading) and user’s capability to modify the virtual environment
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Application domains of MUSCAMAGSRDDC Valcartier and Service de Police de Québec and Sûreté du Québec: Simulation of crowd movements and impact on different activities (ex. impact on emergency evacuation of people, use of non-lethal arms, etc.)
RDDC Valcartier: MAGS as a support to the creation and assessment of Courses of Action (towards the development of Critquing systems)
RDDC Valcartier and Nsim Technology: Exploration of the use of Multi-agent geo-simulation and reinforcement learning to solve patroling problems (UAVs and moving targets) in plausible virtual environments (in 2006)
Institut de Santé Publique du Québec: Simulation of the spreading of the West Nile Virus (ends August 06)
CRAD and associated researchers in Toronto area: Simulation of the travel, shopping and leisure behaviors of people in large urban areas and decision support for the organization of urban space
Ville de Québec: Application of population simulation in a urban area to support urban planning decisions and the creation and discussion of regulations with stakeholders
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Example: a multi-Scale shopping behavior simulation
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Overview of Progress ReportThe PLASMA Environment and Language (Garneau, Moulin)Preliminary Model to represent Multi-scale Environments (Chaker, Moulin)Synthetic Population Creation (Chaker, Thériault, Moulin)Accessibility and Perception of Space (Biba, Thériault) Modeling Consumers’ Destination Choices (Biba, Thériault) Modeling Accessibility using Public Transit Services (Bernard-Lachance, Thériault) Toward a Systematic Way to Characterize Persons’ Travel Activities and Decisions (Scott, Doherty)Issues in Urban Semantics (Harrap)MUSCAMAGS and SOLAP Research (Bédard et al.)Cellular Automata and the Simulation of Land Use Changes (Ménard and Thériault)
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The PLASMA Environment and Languageby Tony Garneau (PhD with B. Moulin)
Goal of the PLASMAS project
To offer an agent behavior specification language and environment for georeferenced simulations.
The language must be expressive and simple to use. It also should help adeveloper to validate the coherence of created behaviors.
Four main components
The programming languageThe development environment (IDE)The runtime engineThe visualization tools
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The programming languageImplementation characteristics
Fully interpreted languageWith some real-time compilation for optimization purpose.
Completely portable language (Windows, Unix, Linux “and untested mac”)Usable in applets
Directly transferable in appletsNo modification needed
Independent from the development environmentHighly declarative language with a taste of procedural and object-oriented aspectComponent-oriented language3D visualization with jpct (www.jpct.net)
“Real” and complete programming languageBasic data types
int, double, string, array, references (hashtable), object, etc.User defined types (objects and agents) :
Constructors Methods (allowing overload and recursively methods)Value and reference parametersControl structures : if, while, for, assert, return, call, set, map, etc.Arithmetic, boolean and relational operatorsImplicit and explicit cast operatorsAccess modifiers : public, restricted, privatePackage scope resolutionAnd many more…!
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PLASMAS modelProgramming language and simulation modelMain Elements
Static objectsDo not evolve during the simulation
Active objectsObjects with behaviors (sophisticated rule lists)
AgentsAgents with objectives and behaviors similar to those of the MAGS Platform
Also, Scenarios ….to be designed and implemented
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Development Environment: PLASMAS IDEThe development environment allows
To manage projectsTo program in the languageTo “compile” the current project (with real-time check)To log and debug current projectTo run 2D and 3D simulations
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The Prospectors Example
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PLASMA: Conclusion and future work
The development is not yet completed…We need to implement our agent modelWe need to improve the scenario definitionOptimize the 3D modelImplement effective validation techniques
Graph algorithmsOntologies“Home made errors validation technique”
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A Preliminary Model to Represent Environments for Multiscale Simulations
By W. Chaker, B. Moulin et M. Thériault
MotivationIn simulation, varying the level of detail is useful for many purposes :
To focus on the analysis of specific phenomenaTo reduce computation and memory resources To handle data availabilityTo calibrate theoretical models ….
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Multi-Scale Models in Traffic Simulation
Macro, meso, micro levels
Existing simulators are hybrid systems that integrate different monoscale models
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Drawbacks of current approaches
When we consider such a hybrid system, a lot of effort is spent to check coherencies !Example : in the Burghout and Andéasson meso-micro traffic simulation approach (2004), the designer has to check :
Consistency in route choice and network representationConsistency of traffic dynamics at meso-micro boundariesConsistency in traffic performance for meso and micro submodelsTransparent communication and data exchanges
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Importance of structuring the simulation environment
A multiscale representation of the simulation environment (the network structure) is missing in these approachesIn contrast, we propose to work on a simulation system which interacts with an environment structured according to different scales
IntuitionsMulti-agent approaches offer a good way to design such a system, providing that we can distinguish agents from objectsHierarchical decomposition is a basic and powerful notion to organise spatial environments (Timpf, Farenc, …) If we maintain a connection between the different representations of the environment we expect to :
1. make agents’ behaviour specification easier2. reduce the cost of checking coherencies
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Creating a MultiScale Environment
Montreal
Québec City
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Québec City
Creating a MultiScale Environment
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Synthetic Population Creation
By W. Chaker, M. Thériault, B. Moulin
Based on data representing the main characteristics of a representative sample of Quebec city population, we built the corresponding synthetic agent populationTwo steps :
1. To generate a synthetic population2. To assign persons to activity locations
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Origin Destination Survey Quebec 20013 months 2001, the Ministry of Transport ation of Quebec (MTQ) and Quebec City Transit Authority (RTC)68 121 persons living in 27 839 households interviewed 174 243 trips to reach activities during a week dayHomes and activity places located on maps using street addressesEach person characterized by age, gender, occupation (worker, student, retired, unemployed, etc.) and ownership of a car driver licenceRole in household: lone adult, child (less than 16 years or less than 21 years and still at school), adults living in couple (husband, wife, father, mother), and adult living in multi-generational (2adults with more than 15 years of difference of age) or living in multiple adults householdsVarious household types: lone person, childless couple, two-parent family, lone-parent family, and other households, either multi-generational or more than two adults, with and without childrenDeparture time, origin and destination of each trip are known
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Compilations of the OD Survey
We start from an Origin Destination Survey (2001) From the OD survey database, characteristics of the sample population are extracted These characteristics are represented by a set of attribute dependencies The correlation is statistically confirmed by constructing cross tablesEach cross table will be translated as a variable distribution of probabilities when we build the synthetic population
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Household table structure (1945 entries in the sample)
UniqueMena Integer Index 1 Numéro unique du ménage
Longitude Float Longitude du lieu de résidence (degrés décimaux)
Latitude Float Latitude du lieu de résidence (degrés décimaux)
ResidGrHex Smallint Numéro de grille hexagonale du lieu de résidence
NbPers Smallint Nombre total de personnes dans le ménage
NbAuto Smallint Nombre total d’automobiles dans le ménage
DEucReCen Decimal (10, 3) Distance euclidienne entre le lieu de résidence et le centre-ville (Km)
Noeud94Axe Integer Numéro du noeud le plus rapproché de l’axe urbain central dans le réseau routier de 1994
DEucReAxe Decimal (10, 3) Distance euclidienne entre le lieu de résidence et l’axe urbain central (Km)
TypeMena Smallint Code de type détaillé de ménage
NomTypeMenage Char (110) Nom de type détaillé de ménage (Fonction de la variable TypeMena)
GroupeMena Smallint Code de groupe général de ménage
Enfant6 Logical Présence d’enfant de moins de 6 ans dans le ménage (préscolaire)
Enfant6_15 Logical Présence d’enfant de 6 à 15 ans dans le ménage
Etudiant16 Logical Présence d’étudiant de 16 à 21 ans dans le ménage
DeuxTrav Logical Ménage à deux revenus
UnTrav Logical Ménage à un seul travailleur
OD Survey 2001
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Person table structure (4763 entries in the sample)
OD Survey 2001
UniquePers Integer Numéro unique de la personneUniqueMena Integer Numéro unique du ménageSexe Smallint Code de sexe de la personneGenre Char (8) Genre de la personne (Fonction de la variable Sexe)Age Smallint Age de la personne (années)GrpAge Smallint Groupe d’âge de la personneGroupe_age Char (50) Nom du groupe d’âge (Fonction de la variable GrpAge)PermisCond Smallint Code de possession d’un permis de conduirePossession_permis Char (30) Description de la possession de permis de conduire (Fonction de la variable PermisCond)
LPasserBus Logical Possession d’un laisser-passer d’autobusOccupation Smallint Code d’occupation principale de la personneTypeOccupation Char (30) Occupation de la personne (Fonction de la variable Occupation)
RoleMena Smallint Code de rôle de la personne dans le ménageNomRoleMenage Char (50) Nom du rôle de la personne dans le ménage (Fonction de la variable RoleMena)
Enfant Logical La personne est un enfant du ménageProfession Char (2) Code de profession de la personneNomProfession Char (35) Nom de la profession de la personne (Fonction de la variable Profession)GroupeProf Integer Code de groupe professionnel de la personneNomGroupeProf Char (30) Nom du groupe professionnel de la personne (Fonction de la variable GroupeProf)
TravGrHex Integer Numéro de grille hexagonale du lieu de travail habituel (0 si pas de lieu habituel)
EtudGrHex Integer Numéro de grille hexagonale du lieu d’études habituel (0 si pas de lieu habituel)
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Compilations of the OD SurveyResult : a set of cross tables
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Residence and Activity Places
Data from the census of the populationRepresented in a GIS by regrouping information for each local area. Two hexagonal grids are obtained :
Residence gridActivity grid
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Residence and Activity Grids
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Accessibility and Perception of Spaceby M. Thériault and D. Biba
Accessibility to goods and services is a complex notion which completely pervades territorial issuesIt relates to the transportation network and to the distribution of amenities within the city in the light of the needs and preferences of individuals and householdsConsequently, it can be defined as the ability (or willingness) of individuals to travel and to participate in activities they consider important at different locations in their environmentResearch hypothesis: Various types of persons experience different constraints and are not equally willing to travel in order to reach various kinds of activities, meaning that they have an heterogeneous perception of space 30
Objective of the study (Thériault et al. 2005)
Design a methodology to integrate people’s commuting patterns in assessing their perceived accessibilityAnalyses based on the 2001 OD Survey
Analyze mobility behaviour used by people to reach their activity places during a typical week-dayAnalyse people’s sensitivity to travel time considering various types of activities and householdsDefine thresholds to qualify suitability of trip duration and activity choices using a fuzzy logic approachDefine and map purpose and status-specific accessibility indices
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Locating Home Place (Québec 2001)
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Locating Activity places
Trips to Work10 000
5 0001 000
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Fastest route34.25 minutes
Shortest path43.1 kilometres
Impedance
Speed LimitsTurn PenaltiesRoad directions
Modeling Routes and Travel Times Using TransCAD
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Trip purpose
Health care
Restaurant
Leisure
Grocery
Shopping - other sto
Shopping - large sto
School
Work
Car
trav
el ti
me
(min
utes
)
50
40
30
20
10
0
Gender
Male
Female
Analysing Mobility Behaviours
Trip purpose
Health care
Restaurant
Leisure
Grocery
Shopping - other sto
Shopping - large sto
School
Work
Car
trav
el ti
me
(min
utes
)
50
40
30
20
10
0
Family Status
Childless adult
Parent
Child
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Suitability Indices (using Fuzzy logic)According to Kim and Kwan (2003) accessibility measurement should consider “thresholds on activity participation time and travel time in order to identify a meaningful opportunity set when evaluating space-time accessibility”We define accessibility as the ease with which persons, living at a given location, can move to reach activities and services which they consider as most importantThresholds were computed in SPSS from actual O-D trip durations using the HAVERAGE method considering weighted average of four maximum-likelihood estimators, that is, Huber’s M-estimator, Andrews’ wave estimator, Hampel’s M-estimator and Tukey’s biweight estimatorAssumptions (fuzzy logic): [1] any travel time < median (C50) is totally acceptable (1), [2] a travel time > C90 is likely to be unsatisfactory (0), [3] intermediate cases obtained using linear interpolation between 1 and 0
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Trip purpose Type of person/household C50 C90
Work Women 9.5 18.6 Men 9.8 19.3 School Adults (16 years and more) 8.3 18.9 Children (15 years and less) 4.8 16.1 Shopping – large stores Childless households 5.9 12.5 Families 6.5 14.1 Shopping – small shops 4.8 12.6 Grocery Childless households 3.6 9.4 Families 3.5 11.2 Leisure Childless women 6.4 14.9 Mothers 7.1 15.0 Childless men 7.3 16.2 Fathers 8.3 17.6 Children (15 years and less) 6.0 14.3 Restaurant 5.3 12.6 Health care Adults (16 years and more) 6.8 16.6 Children (15 years and less) 4.4 11.3
Suitability Thresholds
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0 5 10
kilometres
Work Places
13 000
6 5001 300
Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 20
0 to 10
Accessibility to Workplace
Women C50 : 9,5 min.C90 : 18,6 min.
Men C50 : 9,8 min.C90 : 19,3 min.
0 5 10
School Places
13 000
6 5001 300
Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 200 to 10Accessibility to
School
C50 : 9,5 min.C90 : 18,6 min
C50 : 4,8 min.C90 : 16,1 min.
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Accessibility to Shopping (Families)
0 5 10
kilometres
Large Stores
13 000
6 5001 300
Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 20
0 to 10
0 5 10
Groceries
3 000
1 500300
Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 200 to 10
Accessibility to Groceries (Families)
C50 : 6,5 min.C90 : 14,1 min.
C50 : 3,5 min.C90 : 11,2 min.
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Findings and further Work on Accessibility
Findings indicate that overall accessibility to jobs and services is quite homogeneous throughout the City thanks to a highly efficient highway networkThere are nevertheless statistically significant differences in the way accessibility is structured depending on trip purposes home locations and household profiles, thereby supporting the hypothesis that various types of persons experience different constraints, make different home location choices and are not equally willing to travel in order to reach various kinds of activity placesMoreover, perception of space is heterogeneous among personsThis work on accessibility is very relevant to the MUSCAMAGS Project, since these models will enable us to specify agents’preferences to various kinds of activity places, depending on accessibility and perception of space
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Modeling Consumers’ Destination Choices«We investigate consumer destination choice (type of outlet) rather
than consumer behaviour within commercial space»
Multinomial Logistic Regression Model Adjusted using O-D Survey Data
Satisfaction
Set of shopping choice alternatives:i. Commercial streets (with/without small
shopping center) ii. Community shopping centresiii. Regional and super regional shopping centresiv. Big boxes and power centers
Where, When, and How to go shopping ?
Consumer store choice = f (utility) = f (retail structure, consumer profile, spatial determinants)
Consumer profile:- Socio-economic and professional status- Household characteristics
Spatial determinants:
- Consumer origin and destination place- Transportation mode and trip attributes
Accessibility
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Shopping Trips Attributes by Retail Form (%)
3.33.22.73.92.8Single parent family
17.715.918.921.110.6Lone person
25.826.121.325.732.2Two adults or more with children
53.254.857.149.354.5Two adults or more without children
Type of household
58.262.060.356.451.8Woman
41.838.039.743.648.2Man
Gender
2.42.02.12.92.4More than one car per driver
51.952.452.549.556.4One car per driver
36.137.736.033.938.9Less than one car per driver
9.67.99.313.72.2Without car or without driverCar per
driver in household
1.00.71.01.40.4Other
10.33.29.119.72.3Walk
4.77.53.84.60.7Bus
83.988.686.274.396.7Car
Transportation mode
3.34.82.32.24.6> 20.00 Km
4.57.62.82.75.115.00 - 19.99 Km
10.716.57.37.611.310.00 - 14.99 Km
24.230.221.816.635.55.00 - 9.99 Km
57.341.065.870.943.50.00-4.99 Km
Length of trip
13.95.314.424.04.9Leisure
9.96.17.317.41.6Restaurants
24.94.834.531.437.0Grocery
51.283.843.827.356.5General goods and products
Shopping purpose
Total (n= 14913)
Regional and supra regional centre
(n=5510)
Community centre
(n= 2977)
Commercial street (with or without neighbourhood centers)
(n=4395)
Big box (n=2031)
Factors
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The utility that individual i is associated with alternative α is given by:
i i iU Vα α α= + ε V i α is the deterministic part of the utility, and εαi is the stochastic part, capturing the uncertainty
The probability that alternative α is chosen by decision-maker i within choice set C is:
( ) maxi i ic bb C
P P U Uα ∈⎡ ⎤α = =⎣ ⎦
The MNL assumption : If error terms (εiα) are independent and identically
distributed, the probability that a given individual chooses alternative i within C is given by:
( )i
k
V
c Vk C
eP ie
µ
µ∈
=∑
Independence from Irrelevant Alternatives (IIA): the ratio of the probabilities of any two alternatives is independent of the choice set (Ben-Akiva and Lerman, 1985). So, for any choice sets S and τ such that , for any alternative α1 and α2 in S, we have:
1 1
2 2
( ) ( )( ) ( )
s
s
P PP P
τ
τ
α α=
α α
µ is a positive scale parameter
MultiNomial Logistics (formulation and assumptions)
Reference is “Big Boxes - Power Centres” [Figures present odds ratios]
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Conclusions on this Study of Destination Choices
This is another model that may be used to specify agent’s decision and behavior rules based on the OD Survey: hence a certified repartition of destination choices relative to individuals’ characteristicsA difficult decision problem: determining for each kind of agent the chain of decisions: choose an activity; choose the activity’s location given individual constraints (allocated activity time, expected travel time, travel mode), group constraints (shared car, schedule of other members in the household), and individual preferences)No known statistical spatial analysis approach can solve all these factors at once. The Multi-Agent Geosimulation approach is our solution to this problem: Various statistical models may be used to create different sets of plausible rules that are input in agents which act as integrators of the models, hence creating a plausible population
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Modeling Accessibility using Public Transit Services
Nicolas Lachance-Bernard, Master candidate (Aménagement du territoire et développement régional) (started in 2006 with M. Thériault)
Primary goal : To develop a procedure that enable simulation of accessibility for public transit network users in a specialized transportation GIS, based on low-structured management data (ASCII files queried from Corporate Bus/Driver Resources/Services Assignment Database)
This methodology will enable the translation of corporate management data (bus routing information) into a topological route system using a road network in order to build a transit simulation network operated within the TransCAD GIS softwareThe model should address four modelling constraints to assess impedance : bus frequencies, average speed, suitable walking distances and costs/durations (Access, Egress, Transfer & in Vehicle Times/Values)
Secondary goal : To assess and compare the accessibility to several shopping places within the CMQ (Quebec Metropolitan Area), using public transit systems. Combining Origin-Destination survey 2001, Road/Bus/Walk networks and route systems & TransCAD GIS
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Methodology summary…
Step 1 : Operational data translationStep 2 : Networks creation (transit systems)Step 3 : Trip’s duration simulationStep 4 : Thresholds calculation using fuzzy logic, transportation GIS and Origin-Destination surveysStep 5 : Accessibility evaluation using Tiefelsdorf method (2003). used by Thériault and Des Rosiers (2004) and Vandersmissen (2003)
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Project WorkFlow (5-steps), Databases, Files
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Public Transit Service Routes (RTC & STL)
(Lachance-Bernard, 2006)
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Shopping centers within the CMQ (2000)
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Done by MTQ, RTC and CRAD174 243 trips (all modes)16 839 trips using transit system;Household and individual characteristics;Consumers trips in 2001
Car : 85,5%; Transit bus : 4,2%; Walk : 9,1%;
Average car trip durations for consuming motivation: under 7 min.
Origin-Destination Survey (2001)
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Accessibility to Galeries de la Capitale during morning peak hour using public transit (Simulated within TransCAD GIS by Bourel 2005)
Accessibility to a shopping center
using public transit
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0 5 10
kilometres
Groceries
3 000
1 500300
Accessibility Index (%)90 to 10080 to 9070 to 8060 to 7050 to 6040 to 5030 to 4020 to 3010 to 20
0 to 10
Accessibility Index Map to Groceries for families (2001)
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Toward a Systematic Way to Characterize Persons’ Travel Activities and Decisions
by D. Scott and S. Doherty
Goals:Find an adequate model for constrainted activity schedulingFind an adequate model for constrainted destination choiceDevelop these models from appropriate data surveys (panel surveys done by the PROCESSUS Network)Derive from these models the corresponding agent’s behavior models (With B. Moulin’s team)These models would be more detailed than the models obtained from the exploitation of the OD Surveys (see work by Thériault and Moulin’s teams). However they will require more extensive data analysis (see richness of panel surveys) 54
Panel Survey data from the PROCESSUS Network (Wave 1)
(Done by M. Lee Gosselin and his team in 2002-2006)Wave 1, Toronto (March 2002 to May 2003): 7-days of activity + travel scheduling decisions of 270 households studied using CHASE, a self-administered computerised survey, including an end-of-the-week review of the typical spatial, temporal and interpersonal flexibility of main classes of activity
Wave 1, Québec City (May 2002 to December 2003): 7-days activity + travel scheduling decisions of 269 households studiedusing paper instruments faxed daily to the lab, followed by an in-depth interview focussing on the perceived spatio-temporal flexibility of every activity executed in the 7 day observation period, a holistic interpretation of activity planning, and expectations about change.
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Panel Survey data from the PROCESSUS Network (Waves 2 & 3)
Wave 2 in Toronto (July 2003 to May 2004) and Quebec City (March 2004 to May 2005): using almost identical 2-day activity diaries retrieved by phone; hypothetical perturbations of a fewrandomly selected activities are posed during the phone interview. Both regions have achieved over 80% retention from Wave 1 to Wave 2.
Wave 3 in Toronto (August 2004 to January 2006) and Quebec City (July 2005 to January 2006): similar to Wave 2, minus hypothetical perturbations, plus a focus on flexibility of attributes of routine activities; also link to geomatics research (GPS-aided diaries)
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A Scheduling Process Approach to Understanding Observed Time-Space Paths
Travel betweenactivitylocations
Time spent conducting activity
Activity Location
x
ytime of day
home
workshop
Work in progress done by Scott and Doherty’s team
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Space-time Prism Constraints within the Scheduling Process
time of day
x
y
home
workshop
Travel betweenactivitylocations
Time spent conducting activity
Activity Location
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Prism Constrained Scheduling ProcessFramework by Scott-Doherty (in progress)
SCHEDULING PROCESS
Meta Level Tour structure (?)↓
Within Tour Pre-planned Pegs↓
Prism Constrained (day-of and impulsive) choices↑↓
Fuzzy-constrained modifications/rescheduling
Household Activity AGENDA
Observed Daily Activity-travel Path
Habits Learning
Needs/desires/ projects
Land-use Opportunities
Lifecycle (age, family, etc.)
Lifestyle (mode, residence, employment)
Inpu
tsO
utpu
tPr
oces
s
Activity-specific Micro Behaviours
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Current Activities / Developments(Doherty and Scott)
Explore validity of “tour” structureAssess available inputsMore thoroughly explore rule set needsCalibrate activity pre-planning choice rule (using CHASE data)
Not static by activity typeBased on frequency, duration, etc.
Couple the above with space-time prism algorithmConstrained destination choice sets for more in-day and impulsive activitiesMinimum participation time assumed/modeled
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Issues in Urban Semantics (R. Harrap)Geometric urban models are semantically impoverishedAgents interact with buildings because of what those buildings are. MUSCAMAGS needs semantically rich worlds
Issues in urban semantics:
Language to express semanticsEnvironment to build out rich modelsTesting semantics
Need to support:Semantics of agentsSemantics of world artifacts including affordances, perceptive issues, …Different agents should have situated, culturally specific ‘views’
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Looking for AnaloguesThe semantics will be used to encode affordances, causal relations, …Related work has been done in:
ArchitectureComputer game design
Architectural Shape GrammarsSIM CITY, etc.In the Sims, objects announce their affordances to nearby characters:
The alternative is for agents to “pull” semantics by interrogation, but this is computationally more expensive
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Current Status of Work (Harrap)
Need a language for semantically rich descriptions of spatio-temporal, causal affordancesNeed an environment that can ‘push’ or ‘pull’ the codingsNeed a geometrically real world with ability to incorporate realgeomatics dataNeed shape-grammar generators capable of building forms at multiple urban scales, so we can test reality against representationsNeed to:
Test temporal toolsLive link to ArcGISBuild interoperability layer for geomatics dataIncorporate building-scale generative shape grammars
Working towards methodologies for building semantically rich 4d worlds for use in multi-scale, multi-agent simulationNeed to work on theoretical aspects (spatio-temporal, urban scale shape grammars), and pragmatic aspects (ex. generators in Python for Blender).
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MUSCAMAGS and SOLAP ResearchDr Yvan Bédard, Laval University (SOLAP)Fariza Boultache, PhD candidate in geomatics (started 01/2006)Rose Marie McHugh, MSc candidate in geomatics (started 01/2006) Maname Nouri Sabo, PhD candidate in geomatics (finishing in 2006)
Combining map generalization algorithms, multiple representations, SGODr Omar Boussaid, Univ. Lyon II, ERIC Lab (specialist in data mining for complex data)
spent a full month at Laval (03/2006) and will continue collaboration)Marie-Josée Proulx, MSc, Laval University, Research Assistant (SOLAP)Walid Ali (Post-doc Cmputer Science, Laval Univ.) and Bernard Moulin
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From MAGS Analysis Tools to …
To SOFTMAP SOLAP Tools
Initial Investigations
(2005)
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Current Research Primary Objective
We are investigating the communication between geosimulation and SOLAP technology in order to achieve two benefits:
1) use SOLAP to freely, interactively analyse the output of geosimulations for user-driven knowledge discovery and validation of simulated data
using base statistics for fine-grained and aggregated data + cross-tabbed data + geovisualization + temporal visualizationchallenges:
mapping the layers of abstraction between MUSCAMAGS and SOLAPdesigning meaningful spatio-temporal datacubesautomatically feeding the spatio-temporal datacubebuilding middleware to facilitate two-way communication
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Primary Objective (Continued)2) To introduce spatial data mining techniques to automaticallyperform certain analysis of the output of geosimulations for knowledge discovery and validation of simulated data
using sophisticated techniques such as decision-trees, neural networks, probabilistic algorithms for spatio-temporal datachallenges:
identifying the most promising spatial data mining approachesusing them with spatio-temporal datacubes (most methods use flat files)using them under spatio-temporal constraints (most methods are not geography-oriented and not constrained by topography obstacles, communication networks, land use, etc.)integrating the outputs of spatial data mining into SOLAP datacubes for further analysis
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Secondary ObjectivesTo investigate the following fundamental challenges:
1. To analyse the potential of using the multi-scale multi-representations geometry existing is SGO (Self-Generalizing Objects)
2. To analyse the potential of using raster spatial data and analysis into spatio-temporal datacubes for data mining
3. To analyse the potential of using STTOD (Spatio-Temporal Topological Operations Dimensions) for data mining
In collaboration with the NSERC Industrial Research Chair in Geospatial Databases for Decision-Support (Y. Bedard)
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Cellular Automata and the Simulation of Land Use Changes
Project: Development of a geographic cellular automata for the simulation of land-use changes and the testing of planning scenarios for the city of Quebec.
by Dr André Ménard (NSERC postdoctoral scholarship, CRAD)and Dr Marius Thériault (Professor, CRAD)
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Context
The city of Quebec recently published a new planning and development plan that seeks to reduce residential expansion and consolidate land use of the main historical axes.The goal of the dynamic model is to identify the drivers of land-use changes and test the influence of zoning, demographic and economic growth scenarios and transportation initiatives on land-use changes.
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Steps1. Detection of land-use changes in the city of Quebec in the last few decades2. Elaboration of a logistic regression model to explain these changes
Potential explanatory variables: neighborhood pressure, commercial and work accessibility, road network, slope, socio-demographic status, …
3. Development of the CA model on the basis of the probabilities obtained through the regression analysis and constrained by growth projections and zoning.
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Link to MUSCAMAGS
The CA model could be used in two ways:As a dynamic environmental background for multi-agent simulations of the Quebec city population.As a static projected environmental background for multi-agent simulations of the Quebec city population at another time in the future (and for different planning scenarios)
