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MOSAIIC

presentation for for ECCS 2013 - UrbanNet
by

Pierrick Tranouez

on 18 January 2017

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Transcript of MOSAIIC

How does traffic react to industrial disaster?
design by Dóri Sirály for Prezi
Summing up
An agent based urban traffic simulation
Disabled bridge
Consequences?
Complex consequences
Risk management
Increasingly sophisticated
Stays planar
Who lives in the discs?
Who works in the discs?
Road network usage
Road network topology
Short term influence
Long term influence
Constraints
Who goes through the discs?
Traffic at the time of the accident
Evacuation and assistance?
What we did
Multiagent simulation
Ordinary urban traffic
Extraordinary urban traffic
Real geographical data (not toy towns)
Creating the right graph
From geographical data to a directed graph
Nodes vs intersections
Cleaning the data (redundancy)
cutting streets into edges
Handling elevation
Combining different layers
Agents
Mobile Agents
Behaviors:
ordinary situation
Strategical
Operational
Tactical
Establishes a list of destinations
Tries to reach them
Based on the density of the traffic it can adjust its plans
Look for less populated edges around him when stuck in a jam
Compute new trajectory based on new information that edges can communicate to him

It drives in a way suited to its environment
Speed limits
Traffic around him
Density of the lanes if multiple
Crossing crossroads
Intelligent Driver Model
Individual variability (careful/careless)
Length of the vehicle
Braking distance (taste and capacities)

Behaviors:
extraordinary situation
Strategical
Operational
Tactical
Establishes a list of destinations
Tries to reach them
Chicken behavior
Flee with the danger in the back;
Bystander behavior
Go to the danger
Wandering behavior
Randomly chooses an edge at each crossroad;
Roadrunner behavior
Pick the less congested edge at each node;
Sheep behavior
Agent follows the crowd whatever the direction

It drives in a way suited to its environment
Speed limits
Traffic around him
Density of the lanes if multiple
Crossing crossroads
Intelligent Driver Model
Individual variability (careful/careless)
Length of the vehicle
Braking distance (taste and capacities)

Edges: modelling emerging jams
UMR IDEES - MTG
Guillaume Czura, Éric Daudé, Patrice Langlois, Patrick Taillandier
EA LITIS
Pierrick Tranouez
Origin and destination of daily traffic?
Sophisticated surveys
Possibilities
Traffic statistics
Parsimony
Decomposing the traffic as the sum of different simple fluxes
Linear decomposition of fluxes
Enquêtes ménages-déplacements
INSEE
Census
Modeling
Ex: Four step model
Transit traffic?
Road goods transport (light and heavy truck)?
Using some for calibration other for validation
Gravity model for O/D
Showtime
MOSAIIC V1
MOSAIIC V2
2008
Delphi
NavteQ data
2013
Java/GAMA
OpenStreetMap
MOSAIIC
Explorations
Comparing the ODs? How parcimonious can we be? What is necessary?
Analysing the alternative behaviours
Impossible to know how people react
Analysis of proportions
Measurements of characteristics of the topology
Interest of abstract road networks?
Full transcript