TRAFFIC FLOW MODELS

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1 Δεκ 2013 (πριν από 3 χρόνια και 6 μήνες)

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A SURVEY ON

TRAFFIC FLOW MODELS

Srinithya Kakani

Amrutha Chalasani


Introduction


Traffic congestion occurs when a volume of traffic or model split generates demand for
space greater than the available road capacity. Traffic flow state estimation and traffic
congestion detection are the major factors causing social, environmental and ec
onomical
problems to the society. The big challenge for Intelligent transportation systems,
Advanced Traveler Information systems (ATMS) and Advanced Traffic Management
Systems (ATMS) is to take appropriate measures to control traffic congestion.
To find t
he
counter measures for traffic congestion, there should be a clear understanding on traffic
flow operations. How
do

the congestions occur
s in spatio
-
temporal process (t
ime and
space), causes of congestion, the frequent times for traffic breakdown etc.

Sev
eral
mathematical theories are used to predict traffic congestion. Many have compared traffic
congestion to fluid dynamics, as spontaneous jams can occur due to a minor event in
instances of traffic that is heavy, though free flowing. Unfortunately, traffi
c can be
affected by many events, such as signals, entries from on ramps, and departures onto exit
ramps, accidents and road construction. Here, a survey has been provided on the different
traffic flow models based on the classification in to Microscopic,
macroscopic and
Mesoscopic traffic flow levels.

Microscopic traffic flow models describe the traffic flow
by considering the individual entities like vehicles and drivers. They simulate singe
vehicle
-
driver units, so the dynamic variables of the models
represent microscopic
properties like the position and velocity of single vehicles
.


Macroscopic Traffic models describe the traffic flow by
aggregate behavior of vehicles
(group of vehicles) without considering its constituent parts. It formulates the
r
el
ationships among
traffic flow

characteristics like density, flow, mean speed of a traffic
stream, etc. Such models are conventionally arrived at by integrating

microscopic traffic
flow models

and converting the single
-
entity level characteristics to comparable system
level characteristics.

Mesoscopic tr
affic models
describe the traffic in terms of small
group of traffic entities and the interactions are represented at low levels of detail.
Mesoscopic traffic flow models use the probabilistic terms to specify the behavior of
individual entities.



Problem

definition


12 Papers are chosen that presents

different efficient and
well
-
used

traffic flow models
that help us to model traffic flow, identify the traffic state and identify congestion in the
traffic networks. The main goal of this survey is to classify the traffic flow models for
congestion modeling by its level of detail

(Microsc
opic, Macroscopic, Mesoscopic
.
).

The
Causes, identification methods and counter measures/models
specified in the selected
research papers
are explained
in our survey.


Literature survey


Existing traffic mobility models can be classified into two
categories based on the
modeling approach, car following and CA. Examples of mobility models based on car
following include the Manhattan model [13] and street random waypoint (STRAW) [14].
Models using car following (e.g., the Manhattan model) either do n
ot

support any
intersection control mechanisms such as traffic lights or stop signs, or (e.g., STRAW)
require real street maps and support only two intersection control operations: traffic lights
and stop signs. In [1] a new CA based mobility model as a fr
amework to study
characteristics of urban traffic is proposed. [2]
Proposes

a new microscopic traffic flow
model to describe car
-
following process and to represent certain traffic flow phenomena.
Driver individual maximum speed is considered to enable the
model to reflect the
external environment and driver characteristics. A stochastic discrete automaton model to
simulate freeway traffic is introduced in paper [3]. Monte
-

carlo simualtions of the mode
show a transition from laminar traffic flow to start st
op waves with increasing vehicle
density,

same as observed
in







free way traffic. [4]
Reports

the behaviour of traftic flow at signalize intersectio
ns with
mixed traflic in Beijing. The research
is
based on the survey data collected by
using a
digital video camera and co
mputer video analyzing method.
Coupling

a microscopic
(vehicle based) and a macroscopic (flow based) representations of traffic flow may be a
useful tool to better understand the relationships between the various types of
representation. They can also be a basis for implementing various model e
xtensions,
which may be easier using one type
of representation

or the other. The Hybrid model
presented in [5] combines a flow model and vehicular representations of the same model,
which is the classical Lighthill
-
Witham
-
Richards model
. [6]
Proposes

a ma
croscopic
urban traffic network model

that describes the
substantial mechanism of traffic flow
movement and the topology of the entire urban traffic network

It can simulate the traffic
movement in the urban traffic network, and forecast the traffic flow st
ates in the near
future accurately
.


A macroscopic model in [7] considers
explicitly queues in the links, in order to take into
account congestion
phenomena, which

usually characterize urban traffic neworks m
-
th
link (spillbacks, bottlenecks, etc.) The pr
oposed model has been applied to the urban
traffic network of the city of Bologna, North Italy; in this connection, so
me results are
reported in [7].
Data fusion is one of the recent approaches in traffic analysis for the
accurate estimation and prediction

of traffic parameters. [8]
Discusses

a model based
approach to estimate the parameters of heterogeneous traffic using both location data and
spatial data using data fusion.
[9]

develops a macroscopic model for mixed urban and
freeway

traffic networks that is particularly suited for control purposes.
An extended
version of the METANET traffic flow model
to
describe the evolution of the traffic flows
in the freeway part of the

network. [10]
Discusses

a new A
nisotropic Mesoscopic
Simulat
ion (AMS) approach that carefully omits micro
-
scale details but nicely preserves
critical traffic dynamics characteristics. The AMS model allows computational speed
-
ups
in the order of magnitudes compared to the microscopic models, making it
well suited

fo
r
large
-
scale applications.

In [11] the proposed model combines a number of the recent
advances in simulation modeling such as discrete event time
resolution and combined
queue
-
server and speed
-
density modeling, with a number of new features such as the
ab
ility to integrate with microscopic models to create hybrid traffic simulation.

A new
dynamic
traffic assignment model that is

based on the mesoscopic space
-
time queue
network loading method developed by Mahut is introduced in [12]. This hybrid
optimizatio
n simulation method was applied to a portion of the Stockholm road network




































References:


[
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
",Physic
s 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.


[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 Bei
jing, 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 Confer
ence 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
-
Bad
en, Germany, June 5
-
9, 2011


[9] M. van den Berg, A. Hegyi, B. De Schutter, and J. Hellendoom, “
A Macroscopic
tr
affic Flow Model for Integrated Control of Freeway and Urban Traffic Networks

42nd IEEE

Conference
on
Decision and Control Mad, Hawaii
USA,
December
2003


[10]Y
e Tian,Yi
-
Chang Chiu
, “

A
nisotropic mesoscopic traffic simulation approach to
support large
-
scale traffic and logistic modeling and analysis

2011 Winter
Simulation IEEE Conference


[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


[13] F. Bai, N. Sadagopan, and A. Helmy
, “The IMPORTANT Framework for Analyzing
the Impact of Mobility on Performance of Routing for Ad Hoc Networks,”
Ad Hoc

Net. J.
, vol. 1, no. 4, Nov. 2003, pp. 383

403.


[14] D. Choffnes and F. E. Bustamante, “An Integrated

Mobility and Traffic Model for
Veh
icular Wireless Networks,”

Proc. ACM Int’l. Wksp. Vehic. Ad Hoc Net
.,Sept. 2005,
pp. 69

78.