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From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
From traffic flow modeling todemand modeling for large scalemulti-agent simulations of urban
systems
Kai Nagel, TU Berlin
15. Dezember 2005
1 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
2 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
3 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
The questions
Assume a modification in the urban system, e.g.conversion of some car street into pedestrian mall.Consequences:
I Car traffic goes somewhere else (where?).I People switch from car to public transit.I People go somewhere else (e.g. avoid inner city).I People relocate (e.g. move to inner city).I Property prices change.I Emissions change.I Etc.
⇒ Useful: Tool that is able to approach thesequestions. (Cf. Bazzani presentation yesterday)
4 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
5 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Conventional method: 4-step process
1. Trip generation: Find sources and sinks for trips. ≈land use, makes sense.
2. Trip distribution: Connect sources and sinks ...pij ∝ 1/dij ...
3. Modal split: Determine fraction of trips that doesnot use cars ...
4. Route assignment: Find routes for car trips ...similar to current assignment in resistor networkexcept that particles have destination...
6 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
What’s good about 4-step process?
Solution has some uniqueness properties.
⇒ Any correct computation will yield same result.
Simplifies analysis enormously.
7 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
What’s bad about 4-step process?
Does not accomodate many modern questions: peakspreading, 2-destination plans, telematics, emissions,...
Incremental steps towards any of these (“physicalqueues”, spillback) destroy uniqueness property
8 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Multi-agent simulation
Alternative to 4-step process: Multi-agent simulation
Everything (travelers, vehicles, traffic lights, etc.) isindividually resolved ...
... in principle. :–)In practice, limits of
I coding,I knowledge,I data needs.
9 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Physical vs. strategical level in MASim
The mental world:
− limits on accel/brake− excluded volume− veh−veh interaction− veh−system interaction− ped−veh interaction− etc.
� �� �� �� �
� �� �� �� �
� � � �
� � � �� �
� �� �
Concepts which are insomeone’s head.
plans(acts,routes,...)
per−for−
manceinfo
The physical world:
(once more, cf. Bazzani yesterday)
10 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Next three parts of talk
I Physical level: engineering, physics.
I Strategical level: psychology, sociology, AI
I Interaction between these two (learning,adaptation)
11 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
12 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Techniques for the physical layer
I Cellular automata. On graphs/2d space.
I Molecular Dynamics / coupled maps. E.g. forpedestrians (Bazzani talk. Helbing talk?).
I Queue(ing) sim. “Hourglass:” Vehicles move onlink with free speed until they hit queue; queue isserved first come first serve according to capacity.
Essentially queueing theory, but link can be full(spillback). [[Vis big zrh]]
13 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Summary of “physical world”
I There is technology to work with.
I Many of it comes from (computational) physics.
14 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
15 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
From particles to agents
Make particles “intelligent” ⇒ agents.
E.g.: Destination, day-plan, weekly plan, socialstructure, beliefs/desires/commitments, etc.
16 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Physical level vs strategic level
The mental world:
− limits on accel/brake− excluded volume− veh−veh interaction− veh−system interaction− ped−veh interaction− etc.
� �� �� �� �
� �� �� �� �
� � � �
� � � �� �
� �� �
Concepts which are insomeone’s head.
plans(acts,routes,...)
per−for−
manceinfo
The physical world:
17 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
A census block (A):
Portland, Block Group 321012
18 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Synthetic population (A)(from census data)
••• •
•
•• • •
•
•
• •
•
•
• •
•
•
•
••••
•
••
•
••••
•
•
•
•
••
•
••• •••
•
••
•
•
•
••
••
••
•
•
••
•
•
193238126
165
2128 7
104 37
34
11 27
3
321
19 26
239
33
40
48
37
504
17
9
49
3
307
204
5437
40
17
32
7
26
39
5
151
5
1366
2131
3431
15
10
98
16
30
HOUSEHOLDS BG 312002
•
see TRANSIMS www.Method by R.J. Beckman;
19 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Individual plans (B), (C)
HOME
WORKLUNCH
WORK
DOCTOR
SHOP
HOME
HUSBAND’S ROUTES
Plans for routes ->
HOME
WORKLUNCH
WORK
DOCTOR
SHOP
HOME
HUSBAND’S ACTIVITIES <- Plans for activities
20 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Plans in XML
<person id="241" income="50000"><plan score="123">
<act type="h" end_time="07:00" x100="7150"y100="2790" link="5834" />
<leg mode="car" dept_time="07:00" trav_time="00:25"><route>1932 1933 1934 1947</route>
</leg><act type="w" dur="09:00" x100="0650"
y100="3980" link="5844" /><leg mode="car" dept_time="16:25" trav_time="00:14">
<route>1934 1933</route></leg><act type="h" x100="7150" y100="2790" link="5834" />
</plan></person>
22 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Methods for plans
Try hierarchical model. For each agent:
1. Activity pattern (e.g.home-work-shop-leisure-home)
2. Approximate times (e.g. start in morning)
3. Locations for primary acts (work)
4. Mode choice
5. Locations for secondary acts
6. Precise times
7. Routes
Combinations possible (“simultaneous choice of ...”)
23 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Methods for plans, ctd
The following techniques are useful on some levels ofdemand modelling (plans generation):
I Making draws from statistical distributionsI Discrete choice modelsI Rule-based systemsI OR-type optimization (e.g. shortest path)I Genetic algorithmI Q-learningI Mental map
These are not central to this talk; our preference wouldbe to receive them from outside as plug-in modules.
24 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
The mental world, summary
I Methods to construct complete daily plans ofagents (act patterns, act locations, act times,mode choice, routes).
I This is rather different from “mainstream” physics(computer science, combinatioral optimization,artificial intelligence, ...).
25 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
26 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Learning
Simultaneous execution of all plans causes emergenteffects (e.g. congestion) ...
... which means that “good” plans are no longer good.
⇒ agents revise plans
Co-evolution
27 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Learning, ctd
Standard method: Iterations:
1. All agents have an initial plan.
2. Plans are executed in the mob.sim.
3. Some or all agents revise their plans.
4. Goto 2.
28 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Replanning example
TOP: initial plans BOTTOM: after 15 iterations
“Wider” spread of traffic.
29 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Strategy dimensions
home
route 1
route 2
workplace A
workplace B
route 3
Routes, modes, times, locactions, patterns,residences, life style, commercial location choice, ...
In words: Adaptation at all levels of the demandhierarchy.
30 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Traditional approach to learning (intraff sims)
Agents forget old plan when they get new one (noagent memory).
Disadvantages:I Conceptually problematic.I Not robust against small mis-specifications in
information exchange between modules.I External modules need to be “always” correct.
31 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Better: multiple plans per agentI Each agent has several plans, with score:
Description of plan 1 Score of plan 1Description of plan 2 Score of plan 2
...
I “Period” (e.g. day, week) is run over and overagain; score is updated every time plan is used.
I Normally, agent selects a “good” plan.
I Sometimes, agent re-tries presumably bad plan.
I Sometimes, bad plans are replaced by new ones.
Essentially a classifier system/genetic algorithm onlevel of agent.
Much more robust ...32 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Scoring function
Arbitrary scoring function can be used, e.g.:I Utility functionI Risk-averse averagingI Prospect theoryI ...
33 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Plans, ctd
I Plans are descriptions of intentions. They aresubmitted to the “physical world”, which executesthem, and returns performance information.
I They refer to a whole “period” (day, week, ...).
(This has something to do with game theory.)
I A plan can be conditional (“if jam then ..., else ...”).
I A plan can be revised during execution.
I A plan can be incomplete, filled during execution.
34 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Short excursion to game theory anddynamical systems
Our approach similar to evolutionary game theory:One “game” = one day (period); evol. dynamics fromone game to next.
Evolutionary games (with “best reply” or similar) canconverge to Nash Equilibrium (NE), but can also haveperiodic or chaotic attractors.
For our models, we do not know much about:I Type of attractor, basin of attractionI What happens when agents replan during the day.
(Subgame perfect equilibrium ... but how do youiterate (evolve) that??)
35 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Learning & Strategies, summary I
Plans
Module:RoutesActivities
Module:
Physical Simulation
(Interaction)
ag1 ag2 ag3 ...
Agent Database
mental level
physical level Eve
nts
36 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Learning & Strategies, summary II
I Co-evolutionary dynamical system.
I Has again a lot of physics (I think).
I Not enough explored (Cascetta; Watling)
I Enormous consequences of real-worldinterpretability of results.
37 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
38 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Scenario
I Network of CH with 20 000 links (major streetsonly).
I Demand: “Home-work-home” acts for all carcommuters in Zurich metro area (approx300 000 agents).
39 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Modules
timer
syn.pop
act.chains
prim.act.loc
mode choice
2nd.act.loc
timer
router
mob.sim
learning
learning depth
no memory
router
from census
from census
fixed fraction
fr. OD matrices
(GA−)optimizer
mental map
CA
gravity model
schedule based
mode
mental map
...
from time use survey
anchored (prim.act.loc)
from OD matrix
"fake"
OD matrices
car only
none
simple hill−climbing
best path (last it’n)
queue
agent memory
40 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Learning/feedback/agdb
I Scoring
Utotal =n∑
i=1
Uact,i +n∑
i=1
Ulate,i +n∑
i=1
Utrav ,i ,
Uact,i(tact,i) ∝ ln(tact,i) .
(Vickrey-type dp time choice ... but whole24h-days.)
I Choice between plans eβ Ui . IMPORTANT!!!!
I Sometimes new plans from time mutator, router.
I Many many iterations.
41 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Scoring, exampletime
@home @workplace @lunchtravel
workplace opening time
Notes:I Blue dots = values that are added up.I Marginal utls (red) need to be same at optimal
solution.42 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Application
[[Vis: ch w times-rt]]
43 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Departure time distribution
[[bigfiles/movies/dp-time-histos.eps]]
44 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Validation (7am to 8am, volumes)
This study:
1000
1000
Eve
nts-
base
d T
hrou
ghpu
t
Count Data
counts_vs_350.acts-routes_all6am_hrs7-8.out
datay=x
y=2xy=x/2
VISUM (“best effort”):
1000
1000
Vis
um A
ssig
nem
ent
Count Data
count_vs_visum_hrs7-8.out
datay=x
y=2xy=x/2
This study:. VISUM:.Mean Rel. Bias: +9.4% +42.4%Mean Rel. Error: 30.4% 42.1%
45 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
A real world case, summary
I About as good as traditional method
I Other results (not shown) similar
I Internalized time choice and resulting microscopictemporal structure goes beyond traditionalmethods
46 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
47 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Modules, in study just shown
timer
syn.pop
act.chains
prim.act.loc
mode choice
2nd.act.loc
timer
router
mob.sim
learning
learning depth
no memory
router
from census
from census
fixed fraction
fr. OD matrices
(GA−)optimizer
mental map
CA
gravity model
schedule based
mode
mental map
...
from time use survey
anchored (prim.act.loc)
from OD matrix
"fake"
OD matrices
car only
none
simple hill−climbing
best path (last it’n)
queue
agent memory
48 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Modules, next study
timer ...
...
syn.pop
act.chains
prim.act.loc
mode choice
2nd.act.loc
timer
router
mob.sim
learning
learning depth
no memory
router
fixed fraction
fr. OD matrices
mental map
CA
gravity model
schedule based
mode
mental map
car only
best path (last it’n)
queue
agent memory
from OD matrix
"fake"
OD matrices
none
simple hill−climbing
from census
from time use survey
from census
anchored (prim.act.loc)
(GA−)optimizer
49 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Departure time distributions, initially
50 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Departure time distributions, after 400iterations
51 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Outline
Introduction
Conventional method → MASim
The physical world (= mobility simulation)
The mental world (strategic level)
Learning
A real world case study
Towards the next real world case study
Overall summary
52 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Summary
I Individual agents.
I Separation into “physical world” (CA et al) and“mental world”.
I We are slowly approaching a fully resolvedsimulation laboratory of human spatio-temporalbehavior in real-world urban systems.
I Theoretial issues coming up, some of them ratherclose to interdisciplinary physics.
53 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
Space-time plot, principle
..5.......2..1...2......1..2.....4............4..........
.......3....1.2....2.....1...2.......4............4......
..........1..1..3....2....1....2.........5............4..
.4.........1..2....2...3...2.....3............4..........
.....5......2...2....3....1..3......4.............5......
..........3...3...3.....1..1....4.......4..............5.4............3...2...3...2..1.......5.......4................4...........2..2....1..1.1...........4......5................4.........1..2...2..1.2..............5.......4.....4.........4......2...3...1.1..2.................4............5.........4....2....1.0.2...2...................4.............5........2..3...01...2...2....................5...............4.....2...00.1....3...3.......................4..............2...0.01..2......3...3................5.......4............1.0.0.1...3.......3...3..................5......5.........00.1..2.....3.......3...4....................5......3....00..1...3......3.......4....4.....................4....0.01...1.....4......4........4....5.....................01.0.1...2........4......5........4......................1.00..1....3..........4.......4...........................000...2......3...........5.......4......5................000.....3.......3.............5.......4......5...........001........3.......4...............5......5.......4......00.1..........3........5............5..........5......1..01..2............3..........5............5..........2..1.0.2...3.............3............5.....4......4.......1.00...2....3.............3...................4......3....001.....2.....3.............3....................5.....1.00.1......3......4.............4........4.............1.000..1........3.......5.............5..
......4.....1..2.....3...2....3..........5..........5....
54 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
For experts: Phase trans’n(space-time plots)
↑ weak slow-to-start property
↓ strong slow-to-start property
low density high density
55 / 55
From traffic flowmodeling to
demand modelingfor large scale
multi-agentsimulations ofurban systems
Kai Nagel, TUBerlin
Outline
Introduction
Conventionalmethod → MASim
The physical world(= mobilitysimulation)
The mental world(strategic level)
Learning
A real world casestudy
Towards the nextreal world casestudy
Overall summary
56 / 55