Post on 03-Jan-2016
description
Lecturers:Dr. Francesco CiariDr. Rashid Waraich
Assistant: Patrick Bösch
Lecture:
Agent Based Modeling in Transportation
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)
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Agent
Attributes
Behavioral rules
Decision making
Memory
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Environment
Agent-based modeling
…
…
…
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Agent-based modeling
…
…
…
…
…
…
…
…
…
…
…
…
25
Agent-based modeling
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Agent-based modeling
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Agent-based modeling
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 simulationSimple rules implemented at the micro-level (individual) allows modeling complex behavior at the macro-level (system)
30
Pros and cons
Cons:
– Data hungry– Skilled users
Pros:
– Models Individuals– Agents heterogeneity– Emergent behavior– Can deal with complexity
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
35
152St. Gallen
2332Herisau
1038Waldkirch
1282Gossau (SG)
1428Teufen (AR)
1861Gaiserwald
2674Flawil
1996Niederbüren
1630Bühler220
Stein (AR)
39Wittenbach
2359Schlatt-Haslen
498Speicher
1068Mörschwil
335Andwil (SG)
1160Gais
2620Oberbüren
861Degersheim
1452Untereggen
1138Hundwil
1777Waldstatt
1980Schwellbrunn
543Eggersriet
757Hauptwil-Gottshaus
2541Goldach
2238Niederhelfenschwil
Attra
ction
Gen
erati
on
Four Step Process – Trip Distribution
36
152St. Gallen
2332Herisau
1038Waldkirch
1282Gossau (SG)
1428Teufen (AR)
1861Gaiserwald
2674Flawil
1996Niederbüren
1630Bühler220
Stein (AR)
39Wittenbach
2359Schlatt-Haslen
498Speicher
1068Mörschwil
335Andwil (SG)
1160Gais
2620Oberbüren
861Degersheim
1452Untereggen
1138Hundwil
1777Waldstatt
1980Schwellbrunn
543Eggersriet
757Hauptwil-Gottshaus
2541Goldach
2238Niederhelfenschwil
Attra
ction
Gen
erati
on
Four Step Process – Mode Choice
37
152St. Gallen
2332Herisau
1038Waldkirch
1282Gossau (SG)
1428Teufen (AR)
1861Gaiserwald
2674Flawil
1996Niederbüren
1630Bühler220
Stein (AR)
39Wittenbach
2359Schlatt-Haslen
498Speicher
1068Mörschwil
335Andwil (SG)
1160Gais
2620Oberbüren
861Degersheim
1452Untereggen
1138Hundwil
1777Waldstatt
1980Schwellbrunn
543Eggersriet
757Hauptwil-Gottshaus
2541Goldach
2238Niederhelfenschwil
?
Four Step Process – Route Assignment
38
152St. Gallen
2332Herisau
1038Waldkirch
1282Gossau (SG)
1428Teufen (AR)
1861Gaiserwald
2674Flawil
1996Niederbüren
1630Bühler220
Stein (AR)
39Wittenbach
2359Schlatt-Haslen
498Speicher
1068Mörschwil
335Andwil (SG)
1160Gais
2620Oberbüren
861Degersheim
1452Untereggen
1138Hundwil
1777Waldstatt
1980Schwellbrunn
543Eggersriet
757Hauptwil-Gottshaus
2541Goldach
2238Niederhelfenschwil
??
?
Four Step Process – Facts
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Tra
dit
ion
al F
ou
r S
tep
Pro
cess Trip Generation
Trip Distribution
Mode Choice
Route Assignment
• Traditional approach in transport planning• Simple, well known and understood
• Sequential execution• Feedback not required, but desirable
• Aggregated Model• No individual preferences of single
travelers• Only single trips, no trip chains
• Static, average flows for the selected hour, e.g. peak hour
Iterative Four Step Process
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Iter
ativ
e F
ou
r S
tep
Pro
cess
Trip Generation
Trip Distribution
Route Assignment
Iter
atio
ns
Mode Choice
• Improvement of the traditional approach• Iterations allow feedback to
previous process steps
• Still an aggregated model
Modern Modeling Approaches
• Activity-based demand generation
• Dynamic traffic assignment
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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)
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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.
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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
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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.
• Replaces all steps of the four step process.
• During the whole process, people from the synthetic population are maintained as individuals.
Individual behavior can be modeled!47
Fu
lly
Ag
en
t-b
as
ed
Ap
pro
ac
h
Ac
tiv
ity
-ba
se
d
De
ma
nd
Ge
ne
rati
on
Dy
na
mic
Tra
ffic
A
ss
ign
me
nt
Synthetic Population Generation
Agent-based Traffic Flow Simulation
Agent-based Activity Generation (Trip
Generation & Distribution)
Agent-based Mode Choice
Agent-based Route Assignment
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)
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Introduction to MATSim
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
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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
scoringinitial
demandexecution
(simulation)analyses
replanning
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
Road network55
High resolution navigation network, including turning rules
Day-plan56
7:30
7:40
7:50
7:56
17:03
17:09
17:13
17:25
17:45
17:55
19:24 19:31
Speed vs Resolution
Speed
Reso
lutio
n
physical(VISSIM)
CA(TRANSIMS)
meso(METROPOLIS)
macro(VISUM)
Q(Cetin)
Q event(MATSIM)
parallelQ event(MATSIM)
Facilities58
„Facilities“:• Building location• Activity options• Capacity, Opening time
Source: Enterprise register, Building register
Performance - Scenario59
• 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
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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
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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
• Munich, Germany• Germany/Europe – Main road network• Padang, Indonesia• Tel-Aviv, Israel• Kyoto, Japan• Toronto, Canada• Caracas, Venezuela
Switzerland
Berlin and Munich, Germany
Toronto, Canada
Tel Aviv, Israel
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.