CSS434 Presentation Guide
• # slides should be around 15 for a 20-minute talk.
• Show a table of contents, (i.e., what you will be talking
about).
• Get started with the background of the project you
surveyed.
• Digest the essense of the project rather than cut and paste
the contes from the papers you read.
• Include examples, illustrations, and performance results.
• Clarify pros and cons of the research/development project
you surveyed.
• Add your opinion to improve the project.
• Conclude your presentation.
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CSS434 Demo Talk: Agent-Based Traffic Simulation
CSS434 Demo Talk
Agent-Based Traffic Simulation
Munehiro Fukuda
University of Washington Bothell
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Table of Contents
1.
2.
3.
4.
Conventional Mathematical Models
Micro-Simulation: Agent-Based Models
MATSim
Challenges in Agent-Based Transport
Simulation
5. Summary
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Backgound
Macroscopic Simulation
Merits
Demerits
• Mathematical models
• General parameter
assumptions
• No micro events or
interactions considered
– Construction, fires, etc.
considered as bias to the model
• Ease of real data retrieval such
as highway traffic
– WSDOT annual traffic report
• Mathematical verification
–
–
–
–
Traffic signals and lanes
Parking
Freight traffic
Public transport
• No dynamic events considered
– Weather
– Dynamic trip plans
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Background
Agent-Based Modeling
• Micro-simulation
– Views interaction among a large number of simulation entities,
(a.k.a. agents).
– Simulates an emergent collective group behavior of agents
• Agent-based transport simulation
– Model each traveler as an agent.
– Consider as many traffic events as possible.
– Simulates traffic as an interaction among travelers and events.
• System examples (open source)
– TRANSIMS: based on cellular automata
– MATSim: based on a queuing network
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MATSim
Variable lenth
Event-based queuing simulation
XML input files
Network configuration
Optimization is performed in terms of agents’ plans.
10% agents: reroute their plans dynamically.
90% agents: choose their best score.
Log File
Score Statistics
Leg Travel Distance Statistics
Events
Trip Durations
Agent plans
From Andreas Horni, Kai Nagel, Kay W. Axhausen, “The Multi-Agent Transport Simulation MATSim”
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MATSim Example
https://vimeo.com/138598871
From http://www.matsim.org/scenarios
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Global City Teams Challenge Super Action Cluster Summit
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Challenges in Agent-Based Models
• Modeling
– A huge manpower would be required to model signals, lanes,
parking, etc. in details rather than to give global models and
parameters.
• Calibration
– Non-mathematical verifications are difficult to trust.
– How much detailed data can be sampled from the real world?
• Computation
– Millions of agents drive through several thousands of cells in
TRANSIMS and links in MATSim.
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Global City Teams Challenge Super Action Cluster Summit
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Modeling in Agent-Based Models
•
•
•
•
•
Links: speed, signals, and lanes
Parking
Public transport
Freight traffic
Dynamic events (e.g., accidents and weather changes)
Pro: Agents and micro-simulation can describe almost
whatever we want to model.
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Global City Teams Challenge Super Action Cluster Summit
Feb 2, 2017
Public Transport in MATSim
•
Teleportion
– An agent is removed from
one location and place at a
later point of time.
•
Teleportion
TransitVehicles.xml
– Vehicle type
– Passenger capasity
– Actual vehicles
•
TransitSchedule.xml
– Transit stops with names
– Transit lines
– Routes (links) used by the
transit
– Schedules
Con: Labor/time/data-intensive work
From Andreas Horni, Kai Nagel, Kay W. Axhausen, “The Multi-Agent Transport Simulation MATSim”
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Global City Teams Challenge Super Action Cluster Summit
Feb 2, 2017
•
Calibrations
in
Agent-Based
Models
Realism tests *
– Hourly traffic flows: can be compared with automatic
traffic recorders’ data
– Travel times and speeds: can be compared with public
transports’ data
– Traffic patterns (queuing patterns at intersections,
congested roads, freeway lane choice, merging, etc.):
traffic cameras??
• Available traffic data *
– Automatic traffic recorder(s)’ samples are sparse and
imperfect.
– Drivers’ mentality (e.g., aggressiveness) varies in
metropolitan and suburban areas.
– It is impossible to prepare millions of all agent
itineraries and perturbations, thus we need to sample
householders’ data.
*
• Comparing simulation results with real data
– Data-intensive and labor-intensive work
* From Wisconsin DOT Micro-Simulation Guideline: http://wisdot.info/microsimulation/
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Computation in Agent-Based Models
• Large # road links
– A TRANSIMS simulation of 200,000 links in Portland: 0.23 sec
per simulation step (1 sec)
From Kai Nagel, Marcus Ricket, “Parallel implementation of the TRANSIMS micro-simulation”, Parallel Computing Vol
27(N.12), 2001
– A day traffic simulation would take 5.5 hours.
• Large # agents
– A MATSim simulation of 10,000-car circular movement over
10,000 links: 51 sec
From John Piger, MASS library traffic simulation application development and performance evaluation. Css497 final
report, University of Washington, Bothell, WA, August 2011
– A movement of 200,000 cars driving through I-405 in Bellevue
would take 17 minutes, then a day traffic simulation?
• Solution: Parallel and distributed simulation
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Future Distributed Computing in MATSim
•
•
Master-slave mode
Qsim on master
– Runs selected plans in a full queue
simulation.
– Uses multithreading for
parallelization.
•
Psim on slave nodes
– Produce and evaluate plans for all
agents.
•
Pro
– Could distribute agents over a cluster
and reduce memory usage per node.
•
Con
– Would still suffer from CPU-intensive
micro-simulation.
From Andreas Horni, Kai Nagel, Kay W. Axhausen, “The Multi-Agent Transport Simulation MATSim”
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Our Approach to MATSim Parallelization
• Decentralized model
– Qsim on all computing node
– Links and nodes are mapped
to a distributed array
– Agents migrated over a
distributed space.
• Performance
– Pro: Better than the original
multithreaded MATSim
– Con: Load balancing needed
From Zach Ma and Munehiro Fukuda, “A Multi-Agent Spatial Simulation Library for
Parallelizing Transport Simulations”, WSC 2015
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Final Remarks
• Two major agent-based transport simulators:
– TRANSIMS and MATSim (The talk focused on MATSim.)
• Challenges
– Detailed modeling
• Agents and micro-simulation can describe almost whatever we
want to model.
– Calibrations
• Limitation of real data
• Labor/data-intensive work
– Computational needs
• Some parallel/distributed computing efforts have been made.
• On-the-fly simulation linked to IoT sensors is not yet addressed
because of long-time execution
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CSS434 Demo Talk: Agent-Based Traffic Simulation
Questions?
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