Lecture: Agent Based Modeling in Transportation

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: ???


Transportation: is the movement of people, animals and goods from one location to another (Wikipedia)

Model: ???


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

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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…

…

…

…

…

…

…

…

…

…

…

…

25


26


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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


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)

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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

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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.

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Four Step Process – Trip Generation

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152St. Gallen

2332Herisau

1038Waldkirch

1282Gossau (SG)

1428Teufen (AR)

1861Gaiserwald

2674Flawil

1996Niederbüren

1630Bühler220

Stein (AR)

39Wittenbach

2359Schlatt-Haslen

498Speicher

1068Mörschwil

335Andwil (SG)

1160Gais

2620Oberbüren

861Degersheim

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1777Waldstatt

1980Schwellbrunn

543Eggersriet

757Hauptwil-Gottshaus

2541Goldach

2238Niederhelfenschwil

Attra

ction

Gen

erati

on

Four Step Process – Trip Distribution

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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


2541Goldach


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


2541Goldach


?

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


2541Goldach


??

?

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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http://www.matsim.org/

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.

Lecture: Agent Based Modeling in Transportation

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Transcript of Lecture: Agent Based Modeling in Transportation