Lecture:
Agent Based Modeling in Transportation
Lecturers:
Dr. Francesco Ciari
Dr. Rashid Waraich
Assistant:
Patrick Bösch
Autumn Semester 2014
Lecture I
September 16th 2014
Lecture Structure
- Theory
- Modeling Transport
- Agent Based Modeling
- Multi Agent Transport Simulation (MATSim)
- Practice
- Case studies (individual or in small groups)
- Paper
- The expected output is a case study report in the form
of a proper scientific paper
Modeling transport(ation)
Modeling transportation
Transportation: ???
Model: ???
Modeling transportation
Transportation: is the movement of people,
animals and goods from one location to another
(Wikipedia)
Model: ???
Modeling transportation
Transportation: is the movement of people,
animals and goods from one location to another
(Wikipedia)
Model: A simplified representation of a part of
the real world which concentrates on certain
elements considered important for its analysis
from a particular point of view (Ortuzar and
Wilumsen, 2006)
What for?
• Planning (i.e. infrastructure, systems)
• Policy making
Type of model depends on:
–
–
–
–
Decision making context
Accuracy required
Data
Resources
Activity based paradigm
Transportation
Transportation: is the movement of people,
animals and goods from one location to another
Transportation
Transportation: is the movement of people,
animals and goods from one location to another
Transportation
Transportation: is the movement of people,
animals and goods from one location to another
What are the reasons of this movement?
Activity approaches
Activity approaches means «The consideration
of revealed travel patterns in the context of a
structure of activities, of the individual or
household, with a framework emphasizing the
importance of time and space constraints.
(Goodwin, 1983)
Activity approaches
Allow looking at important aspects of travel like:
•
•
•
•
•
Activity Generation
In home/out of home activities (patterns, substitution)
Constraints
Scheduling
Social Networks
(Kitamura, 1988)
Modeling with agents
What is an agent?
• An agent:
•
•
•
•
•
•
•
Has a set of attributes/characteristics
Follows given behavioral rules
Has decision making capability
Is goal oriented
Acts in an environment and interacts with other agents
Is autonomous
Can learn
• Agents are:
• Heterogeneous
• Attributes can change dynamically
(Source: Macal and North, 2005)
Agent
Attributes
Behavioral rules
Decision making
Memory
22
Agent-based modeling
…
…
…
Environment
23
Agent-based modeling
…
…
…
…
…
…
…
…
…
…
…
…
24
Agent-based modeling
25
Agent-based modeling
26
Agent-based modeling
27
Agent-based modeling
The actors of the (real) system modeled are
represented at indivudual level and implement
simple rules.
The behavior of the system is not explictly
modeled but emerges from the simulation
Agent-based modeling
The actors of the (real) system that is modeled
are represented at indivudual level and
implement simple rules.
The behavior of the system is not explictly
modeled but emerges from the simulation
Simple rules implemented at the micro-level
(individual) allows modeling complex behavior
at the macro-level (system)
Pros and cons
Pros:
–
–
–
–
Models Individuals
Agents heterogeneity
Emergent behavior
Can deal with complexity
Cons:
– Data hungry
– Skilled users
30
Why Agent-Based Modeling is
becoming popular?
• Increasingly complex world
• Availability of high resolution level data
• Computer power
What about transportation?
Traditional Modeling Approach
• Four steps model
33
Four Step Process
• Trip generation
– Define number of trips from and to each zone.
• Trip distribution
– Define for each zone where its trips are coming from and going to.
• Mode choice
– Define transport mode for each trip.
• Route assignment
– Assign a path to each route.
34
Four Step Process – Trip Generation
2238
Niederhelfenschwil
757
Hauptwil-Gottshaus
39
Wittenbach
1038
Waldkirch
1996
Niederbüren
1068
Mörschwil
2541
Goldach
1452
Untereggen
335
Andwil (SG)
543
Eggersriet
1861
Gaiserwald
2620
Oberbüren
152
St. Gallen
1282
Gossau (SG)
498
Speicher
861
Degersheim
1428
Teufen (AR)
Generation
Attraction
2674
Flawil
1980
Schwellbrunn
2332
Herisau
1630
Bühler
220
Stein (AR)
2359
Schlatt-Haslen
1777
Waldstatt
1160
Gais
1138
Hundwil
35
Four Step Process – Trip Distribution
2238
Niederhelfenschwil
757
Hauptwil-Gottshaus
39
Wittenbach
1038
Waldkirch
1996
Niederbüren
1068
Mörschwil
2541
Goldach
1452
Untereggen
335
Andwil (SG)
543
Eggersriet
1861
Gaiserwald
2620
Oberbüren
152
St. Gallen
1282
Gossau (SG)
498
Speicher
861
Degersheim
1428
Teufen (AR)
Generation
Attraction
2674
Flawil
1980
Schwellbrunn
2332
Herisau
1630
Bühler
220
Stein (AR)
2359
Schlatt-Haslen
1777
Waldstatt
1160
Gais
1138
Hundwil
36
Four Step Process – Mode Choice
2238
Niederhelfenschwil
757
Hauptwil-Gottshaus
39
Wittenbach
1038
Waldkirch
1996
Niederbüren
1068
Mörschwil
2541
Goldach
1452
Untereggen
335
Andwil (SG)
543
Eggersriet
1861
Gaiserwald
2620
Oberbüren
?
1282
Gossau (SG)
152
St. Gallen
498
Speicher
2674
Flawil
1428
Teufen (AR)
2332
Herisau
1630
Bühler
220
Stein (AR)
861
Degersheim
2359
Schlatt-Haslen
1980
Schwellbrunn
1777
Waldstatt
1160
Gais
1138
Hundwil
37
Four Step Process – Route Assignment
2238
Niederhelfenschwil
757
Hauptwil-Gottshaus
1996
Niederbüren
335
Andwil (SG)
2620
Oberbüren
1282
Gossau (SG)
2674
Flawil
543
Eggersriet
1861
Gaiserwald
152
St. Gallen
498
Speicher
?
1428
Teufen (AR)
1630
Bühler
220
Stein (AR)
861
Degersheim
1777
Waldstatt
2541
Goldach
1452
Untereggen
?
?
2332
Herisau
1980
Schwellbrunn
39
Wittenbach
1038
Waldkirch
1068
Mörschwil
2359
Schlatt-Haslen
1160
Gais
1138
Hundwil
38
Four Step Process – Facts
• Sequential execution
• Feedback not required, but desirable
• Aggregated Model
• No individual preferences of single
travelers
• Only single trips, no trip chains
Traditional Four Step Process
• Traditional approach in transport planning
• Simple, well known and understood
Trip Generation
Trip Distribution
Mode Choice
Route Assignment
• Static, average flows for the selected hour,
e.g. peak hour
39
Iterative Four Step Process
Trip Generation
Trip Distribution
Mode Choice
Iterations
• Still an aggregated model
Iterative Four Step Process
• Improvement of the traditional approach
• Iterations allow feedback to previous process
steps
Route Assignment
40
Modern Modeling Approaches
• Activity-based demand generation
• Dynamic traffic assignment
41
Activity-based demand generation
• Models the traffic demand on an individual level.
• Based on a synthetic population representing the original
population.
• For each individual a detailed daily schedule is created, including
descriptions of performed…
– …activities (location, start and end time, type)
– …trips (mode, departure and arrival time)
• Activity chains instead of unconnected activities and trips.
• Represents the first three steps of the 4 step process.
42
Activity-based demand generation
• Spatial resolution can be increased from zone to
building/coordinate.
• High resolution input data is required such as…
– …the coordinates of all locations where an activity from
type X can be performed.
– …the capacity of each of this locations.
• Examples of activity-based models
– ALBATROSS (A Learning-Based Transportation Oriented
Simulation System)
– TASHA (Travel Activity Scheduler for Household agents)
43
Dynamic Traffic Assignment
• Supports detailed description of the demand
(persons/households).
• Based on trip chains instead of single trips.
• Time dependent link volumes replace static traffic
flows.
– Spatial and temporal dynamics are supported.
• Represents the fourth step of the 4 step process.
44
Dynamic Traffic Assignment
• Typical implementations are simulation based.
– Iterative simulation and optimization of traffic flows in
a network on an individual level.
• Examples of DTA implementations
–
–
–
–
DYNAMIT (Ben-Akiva et.al.)
DYNASMART (Mahmassani et.al.)
VISSIM (PTV; only small scenarios)
TRANSIMS
45
State of the art
Fully agent-based approach
– Combination of activity-based demand generation
and dynamic traffic assignment
Fully Agent-based Approach
• Combines the benefits of activity-based
demand generation and dynamic traffic
assignment.
Activity-based
Demand Generation
Dynamic Traffic
Assignment
• During the whole process, people from
the synthetic population are maintained
as individuals.
Fully Agent-based Approach
• Replaces all steps of the four step
process.
Synthetic Population
Generation
Agent-based Activity
Generation (Trip
Generation &
Distribution)
Agent-based Mode
Choice
Agent-based Route
Assignment
Agent-based Traffic
Flow Simulation
Individual behavior can be modeled!
47
Macro-Simulation vs. Micro-Simulation
• Macro-Simulation
– Based on aggregated data
– Flows instead of individual movement
– Often planning networks
• Micro-Simulation
– Population is modeled as a set of individuals
– Traffic flows are based on the movement of single vehicles (or
agents) and their interactions
– Various traffic flow models, e.g. cellular automata model, queue
model or car following model
– Often high resolution networks (e.g. in navigation quality)
48
Introduction to MATSim
50
MATSim at a glance
• Implementation of a fully agent-based approach as part of a
transport modeling tool
– Disaggregated
– Activity-based
– Dynamic
– Agent-based
•
•
•
•
Open source framework written in java (GNU License)
Started ~10 years ago, community is still growing
Developed by Teams at ETH Zurich, TU Berlin and senozon AG
www.matsim.org
Working with MATSim…
• Users
• Black-box use
• Super-users
• Add new features
• Developers
• Add new fundamental features
Working with MATSim…
• Users
• Black-box use
• Super-users
• Add new features
• Developers
• Add new fundamental features
MATSim Optimization Loop
•
Optimization is based on a co-evoluationary algorithm
•
Period-to-period replanning (typically day-to-day)
•
Each agent has total information and acts like homo economicus
•
Approach is valid for typical day situations
initial
demand
execution
(simulation)
scoring
replanning
analyses
MATSim – Scenario Creation
• A MATSim scenario contains some mandatory as well
as some supplementary data structures
• Mandatory
– Network
– Population
• Supplementary
– Facilities
– Transit (Schedule, Vehicles)
– Counts
55
Road network
High resolution navigation network, including turning rules
56
Day-plan
7:56
17:03
7:50
7:40
17:09
17:13
19:24
19:31
17:25
17:55
17:45
7:30
Resolution
Speed vs Resolution
physical
(VISSIM)
CA
(TRANSIMS)
Q
(Cetin)
Q event
(MATSIM)
parallel
Q event
(MATSIM)
meso
(METROPOLIS)
macro
(VISUM)
Speed
58
Facilities
„Facilities“:
• Building location
• Activity options
• Capacity, Opening time
Source:
Enterprise register, Building register
59
Performance - Scenario
• Transportation system in Switzerland
• 24 h of an average Work-day
•
•
•
•
•
•
5.99 Mio Agents
1.6 Mio Facilities for 1.7 Mio Activities (5 Types)
Navigation network with 1.0 Mio Links
4 Modes (others optional  i.e. shared modes)
22.2 Mio Trips
Routes-, Time-, (Subtour-)Mode- und „Location“-Choice
 One Iteration in ca. 4.5 hours
Current research themes (I)
• Simulation of public transport
– Improved routing, multimodal simulation
• Replanning improvement
– Reduce the number of iterations, add other choice dimensions
• Simulation of traffic lights and lanes
– Focus on adaptive signal-control
• Queue simulation
– Parallelization
• Modeling of vehicle fleet
– Calculation of emissions
• Electric vehicles
– Simulation of the use of electric vehicles
60
Current research themes (II)
• Agents coordination
– Simulation of joint plans
• Parking
– Improvement of parking choice and search
• Introduction of land-use
– Integration with UrbanSim
• Location choice of retailers
– Addition of supply-side agents
• Car-sharing
– Car-sharing as an additional modal option
• Weather impacts
– Modeling of weather and climate change effects
61
Current scenarios
• Zurich and Switzerland
– Switzerland 7,6 Mio Agents
– Navigation road network with 1 Mio Links
• Berlin, Germany
• Singapore
• Gauteng, South-Africa
• Sioux Falls, USA
Tel Aviv, Israel
Switzerland
•
•
•
•
•
•
•
Munich, Germany
Germany/Europe – Main road network
Padang, Indonesia
Tel-Aviv, Israel
Kyoto, Japan
Toronto, Canada
Toronto, Canada
Caracas, Venezuela
Berlin and Munich, Germany
Gauteng, South Africa
• MATSim Singapore 60FPS NEW TITLES.mkv
(author: Pieter Fourie)
Possible Case Study Themes
• Carsharing
• Electric Vehicles
• Weather
Questions
• Laptop?
– Windows
– Mac
Additional Literature
• Bhat, C. R., J. Y. Guo, S. Srinivasan and A. Sivakumar (2004) A
comprehensive econometric microsimulator for daily activity-travel
patterns, Transportation Research Record, 1894, 57-66.
• Kitamura, R. (1988) An evaluation of activity-based travel analysis,
Transportation, 15 (1) 9–34.
• Macal, C. M. and M. J. North (2005) Tutorial on agent-based
modeling and simulation, Proceedings of the 37th Conference on
Winter simulation, Orlando, December 2005.
• Mahmassani, H. S., T. Hu and R. Jayakrishnan (1992) Dynamic traffic
assignment and simulation for advanced network informatics, in N.
H. Gartner and G. Improta (eds.) Compendium of the Second
International Seminar on Urban Traffic Networks.
• Ortuzar, J. D. D. and L. G. Willumsen (2006) Modelling Transport,
John Wiley & Sons, Chichester.