Traffic Flow models for Road Networks

designpadAI and Robotics

Dec 1, 2013 (3 years and 6 months ago)

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By

Team
-
2


Traffic

congestion

is

a

serious

problem,

that

we

have

to

deal

with

in

order

to

achieve

smooth

traffic

flow

conditions

in

road

networks
.




E
xpanding

infrastructure

of

road

networks

such

as

widening

the

roads

was

just

not

sufficient

to

handle

the

smooth

traffic

flow

conditions

in

road

networks

due

to

increased

traffic

demands



Some

traffic

flow

modeling

method

are

required

to

model

the

traffic

flow

conditions


INTRODUCTION


Traffic

congestion

is

one

of

the

major

problems

affecting

the

whole

world



Intelligent

transportation

systems

like

ATMS

and

ATIS

face

a

big

challenge

in

controlling

traffic

congestion

and

estimating

the

traffic

flow

in

road

networks
.



To

model

efficient

Traffic

flow

in

road

networks,

clear

understanding

on

traffic

flow

operations

like

what

causes

congestion,

how

congestion

propagation

takes

place

in

road

networks

etc

are

required



In

our

presentation

we

are

going

to

explain

some

traffic

flow

models

,

their

classification

and

their

applications

in

the

road

network
.





MOTIVATION


Due

to

the

improved

economic

conditions

of

many

countries,

there

is

a

tremendous

increase

in

motor

vehicles

use

from

many

years
.



The

current

road

infrastructure

in

almost

all

the

countries

is

just

not

sufficient

to

handle

the

current

traffic

conditions




Expanding

the

road

infrastructures

just

solves

the

problem

to

certain

extent

but

cannot

fully

solve

the

traffic

congestion

problem



There

arise

a

need

for

some

traffic

flow

modelling

methods

to

control

the

congestion

which

gave

rise

to

many

traffic

flow

theories


PROBLEM STATEMENT

Traffic

flow

models

classified

in

many

ways

based

on

Levels


Microscopic

Traffic

flow

models

Macroscopic

Traffic

flow

models

M
esoscopic

Traffic

flow

models



.


CLASSIFICATION



Traffic flow models


Macroscopic


Mesoscopic

BLOCK DIAGRAM

Queue
based

LWR

Microscopic

Cell
Automation
Model

Car
following

Model

AMS

DTA

A Car
-
Following Model for

Intelligent Transportation Systems
Management


Driver

individual

maximum

speed

is

considered

to

enable

the

model

to

reflect

the

external

environment

and

driver

characteristics
.



Explains

why

speeds

and

spacing

differ

among

drivers

even

when

the

driving

conditions

are

identical
.



The
model
applies individual
maximum speed
as a model
variable.



Examined

traffic

flow

phenomena
,

such

as
:

equilibrium

speed
-
flow

relationship
,

capacity

drop

and

traffic

hysteresis
.


A

stochastic

discreet

automation

model

is

introduced

to

simulate

free

way

trafic
.



Monte

carlo

simulations

of

the

model

show

transition

from

laminar

traffic

flow

to

start


stop

waves

with

increase

in

vehicular

density
.



Different

control

mechanisms

used

at

intersections

such

as

cycle

duration,

green

split,

and

coordination

of

traffic

lights

have

a

significant

effect

on

intervehicle

spacing

distribution

and

traffic

dynamics
.



It

is

computationally

advantageous


Cellular Automation Model


Hybrid

traffic

flow

models

couples

a

microscopic

(vehicle

based)

and

a

macroscopic

(flow

based)

representations

of

traffic

flow
.


The

hybrid

model

presented

here

combines

a

flow

and

a

vehicular

representations

of

the

same

model,

which

is

the

classical

Lighthill
-
Witham
-
Richards

model
.


H
omogeneous

hybrid

model

correctly

translates

boundary

conditions

from

a

model

to

the

other,

both

under

fluid

and

congested

conditions

LWR Model


A
n

extended

version

of

the

METANET

traffic

flow

model

to

describe

the

evolution

of

the

traffic

flows

in

the

freeway

part

of

the

network


A

new

model

For

the

urban

network

is

proposed

based

on

the

Kashani

model


The

model

has

been

developed

for

use

in

a

model

predictive

control

approach,

and

offer

an

appropriate

trade
-
off

between

accuracy

and

computational

Complexity


T
he

coupling

between

the

freeway

and

the

urban

part

of

the

model

is

also

described
.

A Macroscopic
traffic
Flow Model for Integrated
Control of Freeway and Urban Traffic

Networks


This

model

considers

explicitly

queues

in

the

links,

in

order

to

take

into

account

congestion

phenomena

which

usually

characterize

urban

traffic

neworks



The

traffic

network

is

modelled

by

means

of

a

directed

graph,

and

the

equations

which

drive


the

dynamics

of

the

system

derive

from

the

well
-
known

LWR

model
.



Links

of

the

model

are

divided

into

a

running

section

and

queue

section
.


A queue
-
based macroscopic model for
performance evaluation of

congested urban traffic networks


S
hort
-
term

traffic

flow

forecasting

method

is

described

based

on

the

macroscopic

urban

traffic

network

model
.


A

macroscopic

UTN

model

is

established

and

used

to

forecast

traffic

flow

in

short

term
.



The

model

is

founded

based

on

the

mechanism

of

the

traffic

flow

movement
,

and

takes

all

the

spatial

relationship

of

the

links

into

consideration

through

the

network

topology



It

also

has

a

good

real
-
time

quality

when

guaranteeing

the

forecasting

effectiveness
.

Short
-
Term Traffic Flow Forecasting Using Macroscopic Urban

Traffic Network Model




This

paper

discusses

a

new

Anisotropic

Mesoscopic

Simulation

(AMS)

approach

that

carefully

omits

micro
-
scale

details

but

nicely

preserves

critical

traffic

dynamics

characteristics




AMS

model

allows

computational

speed
-
ups

in

the

order

of

magnitudes

compared

to

the

microscopic

models,

making

it

well
-
suited

for

large
-
scale

applications



It

accounts

for

special

scenarios

involving

stalled

or

particularly

slow
-
moving

vehicles



ANISOTROPIC MESOSCOPIC TRAFFIC SIMULATION
APPROACH TO SUPPORT LARGE
-
SCALE TRAFFIC
AND LOGISTIC MODELING AND ANALYSIS


P
resents

a

new

dynamic

traffic

assignment

model

that

is

based

on

the

mesoscopic

space
-
time

queue

network

loading

Method


It

incorporates

a

route

choice

method

inspired

from

optimization

theory


This

hybrid

optimization

simulation

method

was

applied

to

a

portion

of

the

Stockholm

road

network,

which

consists

of

220

zones,

2080

links

and

5000

turns
.


The

execution

times

for

the

code

developed

for

this

algorithm

are

reasonable

A Hybrid
Optimization
-
Mesoscopic

Simulation

Dynamic Traffic Assignment Model


P
resents

a

mesoscopic

traffic

simulation

model
,

particularly

suited

for

the

development

of

integrated

meso
-
micro

traffic

simulation

models
.


It

combines

a

number

of

recent

advances

in

simulation

modeling

with

new

features,

such

as

start
-
up

shockwaves,

to

create

the

flexibility

necessary

for

integration

with

microscopic

models


D
iscusses

the

structure

of

the

model,

presents

a

framework

for

integration

with

micro

models,

and

illustrates

its

validity

through

a

case

study

with

a

congested

network

north

of

Stockholm


C
ompares

its

performance

with

a

hybrid

model

applied

to

the

same

network
.

A Discrete
-
Event
Mesoscopic

Traffic Simulation Model for Hybrid

Traffic simulation


[1]
Ozan

K.
Tonguz

and
Wantanee

Viriyasitavat
, Fan
Bai


Modeling Urban
Traffic: A cellular Automata Approach
” IEEE Communications Magazine • May
2009


[2] HSUN
-
JUNG CHO, YUH
-
TING WU “
A Car
-
Following Model for Intelligent
Transportation Systems Management
” ISSN: 1109
-
9526 Issue 5, Volume 4,
May 2007


[3]
K.Nagel

and M.
Schreckenberg
,"
A cellular automaton model for freeway
traffic
",Physics

Abstracts December 1992


[4]
Shunping

lis
,
Zhipeng

Li,
Jianping

Wu “
Microscopic
Behaviour

of Traffic at a
Three
-
staged Signalized intersection


Jianping

Wu:
Transporetion

Raeareh

Group. University of Southampton, S o u ~ p t o n , SO17 IBJ, UK. Chong Kong
Scholar
Pmfwr
.
Nonhem

liaotong

UNvenity
.
Bcijiog

Iwo44. China, 2003
IEEE



[5] Emmanuel
Bourrel
, Jean
-
Baptiste
Lesort


Mixing Micro and Macro
Representations of Traffic Flow: a Hybrid Model Based on the LWR Theory

82th Annual Meeting of the Transportation Research Board, 12
-
16 January
2003, Washington, D.C.

References


[6]
Shu

Lin,
Yugeng

Xi, and
Yanfei

Yang, “
Short
-
Term Traffic Flow forecasting Using
Macroscopic Urban Traffic Network Model

“11th International IEEE Conference on
Intelligent Transportation Systems Beijing, China, October 12
-
15, 2008




[7] Marco
Ciccia
,
Davide

Giglio
, Riccardo
Minciardi

and
Matteo

Viarengo
, “
A queue
-
based macroscopic model for performance evaluation of congested urban traffic
networks
” 2007 IEEE Intelligent Transportation Systems Conference Seattle, WA, USA,
Sept. 30
-

Oct. 3, 2007



[8] R.
Asha

Anand
,
Lelitha

Vanajakshi
, and Shankar C. Subramanian, “
Traffic Density
Estimation under Heterogeneous Traffic Conditions using Data Fusion
” 2011 IEEE
Intelligent Vehicles Symposium (IV) Baden
-
Baden, Germany, June 5
-
9,
2011




[9] M. van den Berg, A.
Hegyi
, B. De
Schutter
, and J.
Hellendoom
, “
A Macroscopic
aaffic

Flow Model for Integrated Control of Freeway and Urban Traffic Networks

42nd IEEE Conference on Decision and Control Mad, Hawaii USA, December 2003



[10]Ye
Tian,Yi
-
Chang Chiu , “
Anisotropic
mesoscopic

traffic simulation approach to
support large
-
scale traffic and logistic modeling and analysis
” 2011 Winter
Simulation IEEE Conference


References

[11]
Wilco

Burghout
,
Haris

N.
Koutsopoulos

and Ingmar
Andreasson
, “
A
Discrete
-
Event
Mesoscopic

Traffic Simulation Model for Hybrid Traffic
simulation
” IEEE ITSC 2006 IEEE Intelligent Transportation Systems Conference

Toronto, Canada, September 17
-
20, 2006


[
12] Michael Florian’, Michael
Mahut
’ and Nicolas Tremblay,
”A Hybrid
Optimization
-
Mesoscopic

Simulation Dynamic Traffic assignment Model”
,
2001 IEEE Intelligent Transportation Systems Conference Proceedings
-

Oakland
(CA), USA
-

August 25
-
29, 2001


References

Thank you!