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湖南科技大学
智能控制理论论文
姓名:
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Intelligent
Traffic Signal Control Using Wireless
SensorNe
tworks
Abstract
The growing vehicle
population in all developing and developed
countries calls for a
major change in
the existing traffic signaling systems. The most
widely used automated
system uses
simple timer based operation which is inefficient
for non-uniform traffic. Adv
anced
automated systems in testing use image processing
techniques or advanced com
munication
systems in vehicles to communicate with signals
and ask for routing. This mig
ht not be
implementable in developing countries as they
prove to be complex and expens
ive. The
concept proposed in this paper involves use of
wireless sensor networks to sens
e
presence of traffic near junctions and hence route
the traffic based on traffic density in
t
he desired direction. This system does
not require any system in vehicles so can be
impl
emented in any traffic system
easily. This system uses wireless sensor networks
technol
ogy to sense vehicles and a
microcontroller based routing algorithm for
traffic managem
ent.
Keywords:Intelligent
traffic signals, intelligent routing, smart
signals, wireless sensor
networks.
INTRODUCTION
The traffic density is escalating
at an alarming rate in developing countries which
c
alls for the need of intelligent
traffic signals to replace the conventional manual
and timer
based systems. Experimental
systems in existence involve image processing
based dens
ity identification for
routing of traffic which might be inefficient in
situations like fog, rain or
dust. The
other conceptual system which is based on
interaction of vehicles with traffic
si
gnals and each other require hardware
modification on each vehicle and cannot be
practi
cally implemented in countries
like India which have almost 100 million vehicles
on road
[1]. The system proposed here
involves localized traffic routing for each
intersection base
d on wireless sensor
networks. The proposed system has a central
controller at every jun
ction which
receives data from tiny wireless sensor nodes
placed on the road. The senso
r nodes
have sensors that can detect the
presence of vehicle and the transmitter wirelessly
trans
mits the traffic density to the
central controller. The controller makes use of
the proposed
algorithm to find ways to
regulate traffic efficiently.
THE NEED FOR AN ALTERNATE
SYSTEM
The most
prevalent traffic signaling system in developing
countries is the timer
based system.
This system involves a predefined time setting for
each road at an int
ersection. While
this might prove effective for light traffic,
heavy traffic requires an adaptiv
e
system that will work based on the density of
traffic on each road. The first system
prop
osed for adaptive signaling was
based on digital image processing techniques. This
syste
m works based on the captured
visual input from the roads and processing them to
find w
hich road has dense traffic. This
system fails during environmental interaction like
rain or
fog. Also this system in
testing does not prove efficient. The advanced
system in testing a
t Pittsburgh [2]
involves signals communicating with each other and
also with the vehicles
. The proposed
system does not require a network between signals
and vehicles and is a
standalone system
at each intersection.
THE PROPOSED SYSTEM
This paper
presents the concept of intelligent traffic
routing using wireless sensor
networks. The primary elements of this
system are the sensor nodes or motes
consi
sting of sensors and a
transmitter. The sensors interact with the
physical environment wh
ile the
transmitter pages the
sensor’s
data to the central
controller. This system involves t
he 4
x 2 array of sensor nodes in each road. This
signifies 4 levels of traffic and 2 lanes
i
n each road. The sensors are
ultrasonic or IR based optical sensors which
transmits stat
us based on presence of
vehicle near it. The sensor nodes transmit at
specified time inter
vals via ZigBee
protocol to the central controller placed at every
intersection. The controll
er receives
the signal and computes which road and which lane
has to be given green sig
nal based on
the density of traffic. The controller makes use
of the discussed algorithm to
perform
the intelligent traffic routing.
COMPONENTS INVOLVED IN THE SYSTEM
The proposed system
involves wireless sensor networks which are
comprised of t
hree basic components:
the sensor nodes or motes, power source and a
central controlle
r. The motes in turn
are comprised of Sensors and transceiver module.
The sensors sens
e the vehicles at
intersections and transceiver transmit the
sensor’s
data to the central
co
ntroller through a wireless medium.
The Power source provides the power needed for
the
sensor nodes and is mostly
regenerative. The central controller performs all
the computa
tions for the sensor
networks. The controller receives the input from
all sensors and proc
esses
simultaneously to make the required decisions.
s
Sensors are hardware
devices that produce a measurable response to a
change in
a physical condition like
temperature or pressure. Sensors measure physical
data of the
parameter to be monitored.
The continual analog signal produced by the
sensors is digiti
zed by an analog-to-
digital converter and sent to controllers for
further processing. A sen
sor node
should be small in size, consume extremely low
energy, operate in high volumet
ric
densities, be autonomous and operate unattended,
and be adaptive to the environme
nt. As
wireless sensor nodes are typically very small
electronic devices, they can only be
equipped with a limited power source of
less than 0.5-2 ampere-hour and 1.2-3.7 volts.
S
ensors are classified into three
categories: passive Omni-directional sensors;
passive nar
row-beam sensors; and active
sensors [3].
The sensors are
implemented in this system placed beneath the
roads in an intersec
tion or on the
lane dividers on each road. The sensors are active
obstacle detectors that
detect the
presence of vehicles in their vicinity. The
sensors are set in four levels on each
road signifying four levels of traffic from
starting from the STOP line. The fourth level
indi
cates high density traffic and
signifies higher priority for the road to the
controller. The se
nsors required for
obstacle detection can be either ultrasonic or
Infrared LASER based s
ensors for better
higher efficiency.
B. Motes
A mote, also known as a sensor node
is a node in a wireless sensor network that
i
s capable of performing some
processing, gathering sensory information and
communica
ting with other connected
nodes in the network. The main components of a
sensor node
are a microcontroller,
transceiver, external memory, power source and one
or more sens
ors [3].
C. Need for Motes
The
primary responsibility of a Mote is to collect
information from the various
distrib
uted sensors in any area and to
transmit the collected information to the central
controller
for processing. Any type of
sensors can be incorporated with these Motes based
on the r
equirements. It is a completely
new paradigm for distributed sensing and it opens
up a fa
scinating new way to look at
sensor networks.
D. Advantages of
Motes
?
The core of a mote
is a small, low-cost, low-power controller.
?
The controller monitors
one or more sensors. It is easy to interface all
sorts of
sensors, including sensors
for temperature, light, sound, position,
acceleration, vibrat
ion, stress,
weight, pressure, humidity, etc. with the mote.
?
The controller connects to
the central controller with a radio link. The most
comm
on radio links allow a mote to
transmit at a distance of about 3 to 61 meters.
Power cons
umption, size and cost are
the barriers to longer distances. Since a
fundamental concept
with motes is tiny
size and associated tiny cost, small and low-power
radios are normal.
?
As
motes shrink in size and power consumption, it is
possible to imagine solar
power or
even something exotic like vibration power to keep
them running. It is hard
to imagine
something as small and innocuous as a mote
sparking a revolution, but that's
exactly what they have done.
?
Motes are also easy to
program, either by using serial or Ethernet cable
to conne
ct
to the
programming board or by using Over the Air
Programming (OTAP).
E.
Transceivers
Sensor nodes often
make use of ISM band, which gives free radio,
spectrum
allocation and global
availability. The possible choices of wireless
transmission medi
a are radio frequency
(RF), optical communication and infrared. Lasers
require less ener
gy, but need line-of-
sight for communication and are sensitive to
atmospheric conditions.
Infrared, like
lasers, needs no antenna but it is limited in its
broadcasting capacity. Radio
frequency-
based communication is the most relevant that fits
most of
the WSN applications. WSNs tend
to use license-free communication frequencies:
173, 4
33, 868, and 915 MHz; and 2.4
GHz. The functionality of bothtransmitter and
receiver are
combined into a single
deviceknown as a transceiver [3].
To bring about uniqueness in transmitting and
receiving toany particular device
vari
ous protocols/algorithms are
devised. The Motes are often are often provided
with powerf
ul transmitters and
receivers collectively known as transceivers for
better long range oper
ation and also
toachieve better quality of transmission/reception
in any environmental co
nditions.
F. Power Source
The sensor node consumes power for
sensing, communicating and data
processing. More energy is required for
data communication than any other
process.
Power is stored either in
batteries or capacitors. Batteries, both
rechargeable and non-re
chargeable, are
the main source of power supply for sensor nodes.
Current sensors are
able to renew their
energy from solar sources, temperature
differences, or vibration. Two
power
saving policies used are Dynamic Power Management
(DPM) and Dynamic Voltag
e Scaling
(DVS). DPM conserves power by shutting down parts
of the sensor node which
are not
currently used or active. A DVS scheme varies the
power levels within the senso
r node
depending on the non-deterministic workload. By
varying the voltage along with th
e
frequency, it is possible to obtain quadratic
reduction in power consumption.
G.
Tmote Sky
Tmote Sky is an ultra
low power wireless module for use in sensor
networks,
monitoring applications, and
rapid application prototyping. Tmote Sky leverages
indu
stry standards like USB and
IEEE802.15.4 to interoperate seamlessly with other
devices.
By using industry standards,
integrating humidity, temperature, and light
sensors, and pr
oviding flexible
interconnection with peripherals, Tmote Sky
enables a wide range of mes
h network
applications [4]. The TMote is one of the most
commonly used motes in wirele
ss sensor
technology. Any type of sensor can be used in
combination with this type of mo
te.
Tmote Sky features the Chipcon
CC2420 radio for wireless communications. The
CC2420 is an IEEE 802.15.4 compliant
radio providing the PHY and some MAC
function
s [5]. With sensitivity
exceeding the IEEE 802.15.4 specification and low
power operation,
the CC2420 provides
reliable wireless communication. The CC2420 is
highly configurabl
e for many
applications with the default radio settings
providing IEEE 802.15.4 complianc
e.
ZigBee specifications can be implemented using the
built-in wireless transmitter in the
Tmote Sky.
H. Tmote Key Features
?
250kbps 2.4GHz IEEE
802.15.4 Chipcon Wireless Transceiver
?
Interoperability with
other IEEE 802.15.4 devices.
?
8MHz Texas Instruments
MSP430 microcontroller (10k RAM, 48k Flash Memory)
?
Integrated ADC, DAC,
Supply Voltage Supervisor, and DMA Controller
?
Integrate
d
onboard antenna with 50m range indoors / 125m
range outdoors
?
Integrated Humidity
, Temperature, and
Light sensors
?
Ultra low
current consumption
?
Fast
wakeup from
sleep
(<6μs)
?
Hardware link-layer
encryption and authentication
?
Programming and data
collec
tion via USB
?
16-pin expansion support
and optional SMA antenna connector
?
TinyOS support : mesh
networking and communication implementation
?
Compli
es with
FCC Part 15 and Industry Canada regulations
?
Environmentally friendly
–
compl
ies with
RoHS regulations [4].
I. ZigBee
Wireless Technology
ZigBee is a
specification for a suite of high level
communication protocols using
small,
low-power digital radios based on an IEEE 802.15.4
standard for personal ar
ea networks
[6] [7]. ZigBee devices are often used in mesh
network form to transmit data
over
longer distances, passing data through
intermediate devices to reach more distant
o
allows ZigBee networks to be formed
ad-hoc, with no centralized control or
high
-power transmitter/receiver able to
reach all of the devices. Any ZigBee device can be
tas
ked with running the network. ZigBee
is targeted at applications that require a low
data ra
te, long battery life, and
secure networking. ZigBee has a defined rate of
250kbps, best s
uited for periodic or
intermittent data or a single signal transmission
from a sensor or input device.
Applications include wireless light switches,
electrical
meters with in-home-
displays, traffic management systems, and other
consumer and ind
ustrial equipment that
requires short-range wireless transfer of data at
relatively low rates
. The technology
defined by the ZigBee specification is intended to
be simpler and less e
xpensive than
other WPANs, such as Bluetooth.
J.
Types of ZigBee Devices ZigBee devices are of
three types:
?
ZigBee
Coordinator (ZC): The most capable device, the
Coordinator forms the
root of the
network tree and might bridge to other networks.
There is exactly one Zig
Bee Coordinator
in each network since it is the device that
started the network originally.
It
stores information about the network, including
acting as the Trust Center & repository
for security keys. The ZigBee
Coordinator the central controller is in this
system.
?
ZigBee Router
(ZR): In addition to running an application
function, a device
can act as an
intermediate router, passing on data from other
devices.
?
ZigBee End
Device (ZED): It contains just enough
functionality to talk to the
parent
node. It cannot relay data from other devices.
This relationship allows the no
de to be
asleep a significant amount of the time thereby
giving long battery life. A ZED
re
quires the least amount of memory,
and therefore can be less expensive to manufacture
t
han a ZR or ZC.
K. ZigBee
Protocols
The protocols build on
recent algorithmic research to automatically
construct a low-s
peed ad-hoc network of
nodes. In most large network instances, the
network will be a clu
ster of clusters.
It can also form a mesh or a single cluster. The
current ZigBee protocols
support beacon
and non-beacon enabled networks. In non-beacon-
enabled networks, an
un-slotted CSMA/CA
channel access mechanism is used. In this type of
network, ZigBee
Routers typically have
their receivers continuously active, requiring a
more robust power
supply. However, this
allows for heterogeneous networks in which some
devices receive
continuously, while
others only transmit when an external stimulus is
detected. In beacon
-enabled networks,
the special network nodes called ZigBee Routers
transmit periodic be
acons to confirm
their presence to other network nodes. Nodes may
sleep between beac
ons, thus lowering
their duty cycle and extending their battery life.
Beacon intervals depe
nd on data rate;
they may range from 15.36ms to 251.65824s at 250
kbps. In general, th
e ZigBee protocols
minimize the time the radio is on, so as to reduce
power use. In beac
oning networks, nodes
only need to be active while a beacon is being
transmitted. In non
-beacon-enabled
networks, power consumption is decidedly
asymmetrical: some devices
are always
active, while others spend most of their time
sleeping.
V. PROPOSED ALGORITHM
A. Basic Algorithm
Consider a left
side driving system (followed in UK, Australia,
India, Malaysia and 72
other
countries). This system can be modified for right
side driving system (USA, Canada
, UAE,
Russia etc.) quite easily. Also consider a
junction of four roads numbered as node
1, 2, 3 and 4 respectively. Traffic flows from
each node to three other nodes with varied
densities. Consider road 1 now given
green signal in all directions.
1)
Free left turn
for all roads (free right for right side driving
system).
2)
Check densities at all other nodes and
retrieve data from strip sensors.
3)
Compare the
data and compute the highest density.
4)
Allow the
node with highest density for 60sec.
5)
Allowed node
waits for 1 time slot for its turn again and the
process is repeated f
rom
step 3.
B.
Advanced Algorithm
Assume road three
is currently given green to all directions. All
left turns are always f
ree. No
signals/sensors for left lane. Each road is given
a time slot of maximum 60 secon
ds at a
time. This time can be varied depending on the
situation of implementation. Consi
der 4
levels of sensors Ax, Bx, Cx, Dx with A having
highest priority and x representing
ro
ads 1 to 4. Also consider 3 lanes of
traffic: Left (L), Middle (M) and Right(R)
correspondin
g to the direction of
traffic. Since left
turn is free, Left
lanes do not require sensors. So sensors form 4x2
arrays with 4 levels of
traffic and 2
lanes and are named MAx, RAx, MBx, RBx and so on
and totally 32 sensor
s are following
flow represents the sequence of operation done by
the sign
al.
1) Each
sensor transmits the status periodically to the
controller. 2) Controller recei
ves
the signals and computes the following
3) The sensors Ax from each road
having highest priority are compared. 4) If a
sin
gle road has traffic till Ax, it is
given green signal in the next time slot. 5) If
multiple road
s have traffic till Ax,
the road waiting for the longest duration is given
the green.
6) Once a road is given
green, its waiting time is reset and its sensor
status is negle
cted for that time slot
7) If traffic in middle lane, green is
given for straight direction, based on traffic,
either
right side neighbor is given
green for right direction, of opposite road is
give green for str
aight direction.
8) If traffic in right lane, green is
given for right, and based on traffic, left side
neighb
or is given green for straight or
opposite is given green for right.
9)
Similar smart decisions are incorporated in the
signal based on traffic density and
directional traffic can be controlled.
C. Implementation and
Restrictions
This system can be
implemented by just placing the sensor nodes
beneath the road
or on lane divider and
interfacing the central controller to the existing
signal lights and co
nnecting the sensor
nodes to the controller via the proposed wireless
protocol. The only r
estriction for
implementing the system is taking the pedestrians
into consideration. This h
as to be
visualized for junctions with heavy traffic such
as highway intersections and amo
unt of
pedestrians is very less. Also major intersections
have underground or overhead fo
otpaths
to avoid interaction of pedestrians with heavy
traffic.
ACKNOWLEDGMENT
The Authors would like to take this
opportunity to thank Ms. P. Sasikala, Assistant
Pr
ofessor, ECE department, Sri
Venkateswara College of Engineering,
Sriperumbudur, wh
o gave the basic
insight into the field of Wireless Sensor
Networks. We also thank Mrs. G
.
Padmavathi, Associate Professor, ECE department,
Sri Venkateswara College of
Engin
eering, Sriperumbudur, who with
her expertise in the field of networks advised and
guide
d on practicality of the concept
and provided helpful ideas for future
modifications. We als
o express our
gratitude to Dr. S. Ganesh Vaidyanathan, Head of
the department of ECE,
Sri Venkateswara
College of Engineering, Sriperumbudur, who
supports us for every inn
ovative
project and encourages us
“think
beyond”
for better use of
technology. And finall
y we express our
heart filled gratitude to Sri Venkateswara College
of Engineering, which
has been the
knowledge house for our education and introduced
us to the field of Engine
ering and
supports us for working on various academic
projects.
Adaptive urban
traffic control
Adaptive signal
control systems must have a capability to optimise
the traffic flow by
adjusting the
traffic signals based on current traffic. All used
traffic signal control methods
are
based on feed-back algorithms using traffic demand
data -varying from years to a co
uple of
minutes - in the past. Current adaptive systems
often operate on the basis of ada
ptive
green phases and flexible co-ordination in
(sub)networks based on measured traffic
conditions (e.g., UTOPIA-spot,SCOOT). These
methods are still not optimal where
traffic
demand changes rapidly within a
short time interval. The basic premise is that
existing si
gnal plan generation tools
make rational decisions about signal plans under
varying condi
tions; but almost none of
the current available tools behave pro-actively or
have meta-rul
es that may change
behaviour of the controller incorporated into the
system. The next log
ical step for
traffic control is the inclusion of these meta-
rules and pro active and goal-orie
nted
behaviour. The key aspects of improved control,
for which contributions from artificia
l
intelligence and artificial intelligent agents can
be expected, include the capability of
dea
ling with conflicting objectives;
the capability of making pro-active decisions on
the basis
of temporal analysis; the
ability of managing, learning, self adjusting and
responding to n
on-recurrent and
unexpected events (Ambrosino et al.., 1994).
What are intelligent agents
Agent technology is a new concept
within the artificial intelligence (AI). The agent
pa
radigm in AI is based upon the notion
of reactive, autonomous, internally-motivated
entiti
es that inhabit dynamic, not
necessarily fully predictable environments (Weiss,
1999). Aut
onomy is the ability to
function as an independent unit over an extended
period of time, p
erforming a variety of
actions necessary to achieve pre-designated
objectives while respo
nding to stimuli
produced by integrally contained sensors (Ziegler,
1990). Multi-Agent Sys
tems can be
characterised by the interaction of many agents
trying to solve a variety of pr
oblems
in a co-operative fashion. Besides AI, intelligent
agents should have some additio
nal
attributes to solve problems by itself in real-
time; understand information; have goals
and intentions; draw distinctions
between situations; generalise; synthesise new
concept
s and / or ideas; model the
world they operate in and plan and predict
consequences of a
ctions and evaluate
alternatives. The problem solving component of an
intelligent agent c
an be a rule-based
system but can also be a neural network or a fuzzy
expert system. It
may be obvious that
finding a feasible solution is a necessity for an
agent. Often local opt
ima in
decentralised systems, are not the global optimum.
This problem is not easily solv
ed. The
solution has to be found by tailoring the
interaction mechanism or to have a
supe
rvising agent co-ordinating the
optimisation process of the other agents.
Intelligent agents in UTC,a helpful
paradigm
Agent technology is
applicable in different fields within UTC. The
ones most importa
nt mentioning are:
information agents, agents for traffic simulation
and traffic control. Curr
ently, most
applications of intelligent agents are information
agents. They collect informati
on via a
network. With special designed agents user
specific information can be provided
.
In urban traffic these intelligent agents are
useable in delivering information about
weath
er, traffic jams, public
transport, route closures, best routes, etc. to
the user via a Person
al Travel
Assistant. Agent technology can also be used for
aggregating data for further
di
stribution. Agents and multi agent
systems are capable of simulating complex systems
fo
r traffic simulation. These systems
often use one agent for every traffic participant
(in a si
milar way as object oriented
programs often use objects). The application of
agents in (Ur
ban) Traffic Control is
the one that has our prime interest. Here we
ultimately want to use
agents for pro-
active traffic light control with on-line
optimisation. Signal plans then will be
determined based on predicted and measured
detector data and will be tuned with
adjoi
ning agents. The most promising
aspects of agent technology, the flexibility and
pro-activ
e behaviour, give UTC the
possibility of better anticipation of traffic.
Current UTC is not th
at flexible, it is
unable to adjust itself if situations change and
can't handle un-programme
d situations.
Agent technology can also be implemented on
several different control layer
s. This
gives the advantage of being close to current UTC
while leaving considerable free
dom at
the lower (intersection) level.
Designing agent based urban traffic
control systems
The ideal system that
we strive for is a traffic control system that is
based on actuate
d traffic controllers
and is able to pro actively handle traffic
situations and handling the diff
erent,
sometimes conflicting, aims of traffic
controllers. The proposed use of the concept
of agents in this research is
experimental.
Assumptions and
considerations on agent based urban traffic
control
There are three aspects where
agent based traffic control and -management can
im
prove current state of the art UTC
systems:
- Adaptability. Intelligent
agents are able to adapt its behaviour and can
learn from e
arlier situations.
- Communication. Communication makes it
possible for agents to co-operate and
tun
e signal plans.
- Pro-
active behaviour. Due to the pro active behaviour
traffic control systems are abl
e to
plan ahead.
To be acceptable as
replacement unit for current traffic control
units, the system sho
uld perform the
same or better than current systems. The agent
based UTC will require o
n-line and pro-
active reaction on changing traffic patterns. An
agent based UTC should b
e demand
responsive as well as adaptive during all stages
and times. New methods for tr
affic
control and traffic prediction should be developed
as current ones do not suffice and
cannot be used in agent technology. The
adaptability can also be divided in several
differ
ent time scales where the system
may need to handle in a different way (Rogier,
1999):
- gradual changes due to
changing traffic volumes over a longer period of
time, - abr
upt changes due to changing
traffic volumes over a longer period of time,
- abrupt, temporal, changes due to
changing traffic volumes over a short period of
ti
me,
- abrupt, temporal,
changes due to prioritised traffic over a short
period of time
One way of handling the
balance between performance and complexity is the
use of
a hierarchical system layout. We
propose a hierarchy of agents where every agent is
res
ponsible for its own optimal
solution, but may not only be influenced by
adjoining agents
but also via higher
level agents. These agents have the task of
solving conflicts between l
ower level
agents that they can't solve. This represents
current traffic control implementat
ions
and idea's. One final aspect to be mentioned is
the robustness of agent based syste
ms
(if all communication fails the agent runs on, if
the agent fails a fixed program can be
executed.
To be able to
keep our first urban traffic control model as
simple as possible we have
made the
following assumptions: we limit ourselves to inner
city traffic control (road seg
ments,
intersections, corridors), we handle only
controlled intersections with detectors
(int
ensity and speed) at all road
segments, we only handle cars and we use simple
rule base
s for knowledge
representation.
Types of agents in
urban intersection control
As we
divide the system in several, recognisable, parts
we define the following 4 typ
es of
agents:- Roads are represented by special road
segment agents (RSA),
- Controlled
intersections are represented by intersection
agents (ITSA), - For specifi
c, defined,
areas there is an area agent (higher level),
- For specific routes there can be
route agents, that spans several adjoining road
se
gments (higher level).
We
have not chosen for one agent per signal. This may
result in a more simple soluti
on but
available traffic control programs do not fit in
that kind of agent. We deliberately
ch
oose a more complex agent to be able
to use standard traffic control design algorithms
a
nd programs. The idea still is the
optimisation on a local level (intersection), but
with local
and global control. Therefor
we use area agents and route agents. All
communication ta
kes place between
neighbouring agents and upper and lower level
ones.
Design of our agent based system
The essence of a, demand responsive and
pro-active agent based UTC consists of
s
everal ITSA's (InTerSection
Agent).,some authority agents (area and route
agents) and o
ptional Road Segment
Agents (RSA). The ITSA makes decisions on how to
control its int
ersection based on its
goals, capability, knowledge, perception and data.
When necessar
y an agent can request for
additional information or receive other goals or
orders from its
authority agent(s).
For a specific ITSA, implemented to
serve as an urban traffic control agent, the
follo
wing actions are incorporated
(Roozemond, 1998):
- data collection /
distribution (via RSA - information on the current
state of traffic; fro
m / to other
ITSA's - on other adjoining signalised
intersections);
- analysis (with an
accurate model of the surrounds and knowing the
traffic and traffi
c control rules
define current trend; detect current traffic
problems);
- calculation (calculate
the next, optimal, cycle mathematically correct);
- decision making (with other agent
deciding what to use for next cycle; handle
curre
nt traffic problems);
- control (operate the signals
according to cycle plan).
In figure 1
a more specific example of a simplified, agent
based, UTC system is give
n. Here we
have a route agent controlling several
intersection agents, which in turn
mana
ge their intersection controls
helped by RSA's. The ITSA is the agent that
controls and op
erates one specific
intersection of which it is completely informed.
All ITSA's have direct c
ommunication
with neighbouring ITSA's, RSA's and all its
traffic lights. Here we use the a
gent
technology to implement a distributed planning
algorithm. The route
agents’
tasks ar
e controlling, co-ordinating
and leading the
ITSA’s
towards a more global optimum. Using
all available information the ITSA (re)calculates
the next, most optimal, states and
contro
l strategy and operates the
traffic signals accordingly. The ITSA can directly
influence the
control strategy of their
intersection(s) and is able to get insight into
on-coming traffic
The internals of the
ITSA model
Traffic dependent
intersection control normally works in a fast
loop. The detector
data is fed into the
control algorithm. Based upon predetermined rules
a control strategy i
s chosen and the
signals are operated accordingly. In this research
we suggest the introd
uction of an
extra, slow, loop where rules and parameters of a
prediction- model can be c
hanged by a
higher order meta-model.
ITSA model
The internals of an ITSA consists of
several agents. For a better overview of the
inter
nal ITSA model-agents and agent
based functions see figure 2. Data collection is
partly p
laced at the RSA's and partly
placed in the ITSA's. The needed data is collected
from diff
erent sources, but mainly via
detectors. The data is stored locally and may be
transmitted
to other agents. The actual
operation of the traffic signals is left to an
ITSA-controller age
nt. The central part
of the ITSA, acts as a control strategy agent.
That agent can operate
several control
strategies, such as anti-blocking and public
transport priority strategies. T
he
control strategy agent uses the estimates of the
prediction model agent which estimat
es
the states in the near future. The ITSA-prediction
model agent estimates the states in t
he
near future. The prediction model agent gets its
data related to intersection and road
s
egments - as an agent that
‘knows’
the forecasting
equations, actual traffic conditions
an
d constraints - and future traffic
situations can be calculated by way of an
inference engin
e and
it’s
knowledge and data
base. On-line optimisation only works if there is
sufficient q
uality in traffic
predictions, a good choice is made regarding the
performance indicators a
nd an effective
way is found to handle one-time occurrences
(Rogier, 1999).
Prediction model
We hope to include pro-activeness via
specific prediction model agents with a task
of
predicting future traffic
conditions. The prediction models are extremely
important for the
development of pro
active traffic control. The proposed ITSA-
prediction model agent esti
mates the
states of the traffic in the near future via its
own prediction model. The predicti
on
meta-model compares the accuracy of the
predictions with current traffic and will
adju
st the prediction parameters if the
predictions were insufficient or not accurate. The
predi
ction model agent is fed by
several inputs: vehicle detection system, relevant
road conditi
ons, control strategies,
important data on this intersection and its
traffic condition, commu
nication with
ITSA’s
of nearby
intersections and higher level agents. The agent
itself has
a rule-base, forecasting
equations, knows constraints regarding specific
intersections and
gets insight into
current (traffic) conditions. With these data
future traffic situations shoul
d be
calculated by its internal traffic forecasting
model. The predicted forecast is valid for
a limited time. Research has shown that
models using historic, up-stream and current
link
traffic give the best results
(Hobeika & Kim, 1994).
Control
strategy model
The prediction of the
prediction model is used in the control strategy
planning phase.
We have also included a
performance indicating agent, necessary to update
the
control parameters in the slower
loop. The control strategy agent uses the
estimates of th
e prediction model agent
to calculate the most optimal control strategy to
pro-act on the f
orecasts of the
prediction model agent, checks with other
adjoining agents its proposed tr
affic
control schema and then plans the signal control
strategy The communication sche
ma is
based on direct agent to agent communication via a
network link. The needed nego
tiation
finds place via a direct link and should take the
global perspective into consideratio
n.
Specific negotiation rules still have to be
developed. Some traffic regulation rules and
data has to be fed into the system
initially. Data on average flow on the links is
gained by
the system during run-time.
In the near future computer based programs will be
able to d
o, parts of, these kind of
calculus automatically. For real-time control the
same basic com
puter programs, with some
artificial knowledge, will be used. Detectors are
needed to giv
e information about queues
and number of vehicles. The arrival times can also
be given
by the RSA so that green on
demand is automatically covered.
Intersection Traffic Channelization
Characteristics Based on Harmonious
Traffic
In
forward that the strategic concept is
to construct harmonious traffic, of which two
basic characteristics are humanism and
environmentalism and three critical
objectives are safety, convenience and
environmentalism are as follows.
1)
Service pattern which fully protects the weak
transportation community
In view of
released traffic energy risk degree of different
traffic modes, the
pedestrians and
bicyclists are classified as weak transportation
community and the
service level
gradually increases according to the rank of
private car,public
transportation,
bicyclist and pedestrian respectively.
2) Safety
With the aid of
traffic island and legible traffic signs, the
distinct distinction of
temporal and
spatial rights among pedestrians, bicyclists and
motors can be achieved
to enhance
traffic safety.
3) People oriented
The design of traffic islands and non-
barrier facility and so on should meet the
demand of human behavior, and pay
attention to the demand of disable group.
4) The Optimization of
capacity
It is effective to optimize
intersection capacity by reducing conflicting
area,
advancing the stop line,
reasonably arranging the entry and exit
approaches.
5) Environment friendly
Traffic channelization should be
connected with landscape design and the
humanity design to improve the
environmental benefit of intersections.
3 A Case Study of Chengdu
3.1 Analysis on intersection and its
traffic demand characteristics of Chengdu
Table1. Related characteristics of
arterial road in Chengdu
level of
road
Road
Restriction
Line(m)
?
?
Arterial
road
40~60
3000~
4500
1000~2000
3000~5000
2500~450
0
2000~300
0
Area
(m
?
)
Volume of
Bicycle
Volume
of
Conflict
Area
(m2)
pedestrian ( p/h)
Volume (b/h)
vehicles
two-way
?
two-way
?
(pcu/h)
Walking and
bicycling accounts for a large proportion in
travel behavior, which
respectively is
27.2% and 36.0%. The non-motor traffic volume on
Arterial road
reaches to 5000 bicycles
each peak hour and the acreage in most arterial
intersections
is above 2500
m
?
. Though the commodious
intersection space resources accommodate
theinterchanging
demand
between
pedestrians
and
bicycles
or
motors,
the
lack
of
reasonable
trafficchannelization, the low utility of space
resources,and the serious conflict
greatly restrict the intersection
capacity(table.1).
Fig.1 Temporal and
spatial separation of motor and non-motorized
traffic
3.2
Temporal and spatial separation of motor and non-
motor
According to the commodious space
resources and the serious traffic conflict,
temporal and spatial separation of
motor and non-motor should be conducted at
intersections to classify the road
rights of pedestrians, bicycles and motors in time
and space. With regard to the spatial
road rights the motors, pedestrians and bicycles
should be restricted to travel in
different areas and bicycles travel anticlockwise
while the pedestrians get a
bidirectional crossing (fig.1).
3.3
Placement of Entry and Exit lanes
The
number of entry lanes should more than the exit.
Methods that can be
adopted to add
entry lanes include offsetting median pavement
marking, enlarging
the entry approaches
or removing the median closure while considering
matching the
number of entry lanes with
road capacity. The suggested proportion of the
number of
entry lanes to that of lanes
on road in signal direction is 1:1~2:1 (fig.2).
Fig.2 methods to add entry
lanes
Table 2 Relationship
between lanes on road (one way) and entry lanes at
intersection
Roadways of road
?
one
way
?
corresponding intersection entrance
roadways
3.4
Traffic Island Design
1)the location of
traffic island
The location of traffic
island should reasonably guide motor and non-motor
traffic flow, reduce the severity of
conflict, ensure security and enhance the
capacity.
The location of island is
related to the placement of advanced right-turn
lane. When
≥2
1
?
2
2
≥
3
3
≥
4
4
≥
5
5
≥
the angle of two crossing
roads is less than 75
°
and
there is enough space resources
for
pedestrian refuge, advanced right-turn lane should
be adopted, or else not(fig.3,
fig.4,tab.3).
Fig3 Location of traffic island
Fig4 location of traffic island
(without advanced right-turn lane)
(with advanced
right-turn lane)
Tab.3
Parameters related to the location of island
Design
speed
km/h
?
Bicycle lane
width
corner
D1
?
m
?
>40
D1
≥
D4
Safe
inward
offsetting
width
D3
?
m
?
Bicycle lane
width on
roadway
D4
?
m
?
Vehicle
lane
width at
corner
D5
?
m
?
(
L
?
/
R
+
0.1
V
at
offsetting
width
D2
?
m
?
0.4
R
’
-
2
/
2
R
’
≥
3.5
-
L
’
/2
/
R
)2
+<
/p>
2.8
(
L
?
/
R
+
0.1
V
≤
40
D1
≥
D4
0.2
R
’
-
2
/
2
R
’
≥
3.0
-
L
’
/2
/
R
)2
+<
/p>
2.8
?
footnote:
R’
—
the minimal
turning radii
L’
—
vehicle width
R
—
turning radii
of advanced right turn
lane
L
—
vehicle
length
2) The refuge area of traffic
island
The refuge area of traffic
island should be no less than 5.0m2. Pavement
markings can be adopted while the area
is too limited. The area on safety island
should be large enough to accommodate
the minimal need of pedestrians and the
bicyclists waiting in red time.
Model lists as follow:
A=(Q
p
·
T
p
·
A
p
+Q
b
·
T
b
·
A
b
< br>)S
a
·
S
l
/3600
(1)
In the
equation:A
—
The refuge area
of traffic island (m
?
);
Q
p
,
Q
b
—
the arrival rate of
pedestrians and bicyclists
(person/hour,bicycles/hour);
T
p
,
T
b
—
the maximal red time that the
pedestrian and bicyclist have waited for
(second);
A
p
,
A
b
—
the static refuge areafor one person and
onebicyclist. They are respectively
0.6
m
?
and 1.6
m
?
;
S
a
—
safety coefficient 1.3;
S
l
—
the
coefficient of servicelevel,0.85
—
congestion,1
—
relative
congestion ,2
—
ge
neral,3
—
relative
comfort,5
—
comfort.
3
)
traffic island
design
The contour of traffic island is
the combination of beeline and circular curve.
The refuge area should be set inside
traffic island for crossing pedestrians and
bicyclists. The end of traffic island
should be observable and smooth enough to direct
vehicles. Considering the demand on
comfort and convenience for pedestrian and
bicyclist entrancing and
exiting traffic island
:
the
refuge area should higher 3-5cm
than
the ground, with a 1:10 conjunctive grade. Blind
way should be set on the
refuge area
and connected with pavement (fig.5, tab.4).
Fig.5 Traffic island design
Table.4 Traffic island
traffic channelization design
longitudinal
width
L1(m)
?
Transverse
width
L2(m)
?
Entry
Width
L3(m)
?
Central
landscape
Entry
landscape
Exit
landscape
island(m)
?
grade of
traffic island
island(m)
?
island(m)
?
R11
R1
2
R21
R22
R31
R32
H
R
Max[10,Q
Max[10,Q
2
/
2
L
1
?
L
2
2
>8
>8
>1
>2
>1
>2
< br>3
1
1
/4+5 ]
4+5 ]
~5cm
:10
p>
Footnote:Q
1
,Q
2
—
the maximal
longitudinal and transverse bicycles waiting
during a cycle
at peak hour
4) Landscape design of traffic island
The triangular area on traffic island
can be utilized to design urban greening and
landscape, and the background and
pattern design of refuge area can be connected
with the local historical culture.
4 Traffic Channelization Design of the
Yu Daiqiao Roundabout Flyover
4.1
Traffic Channelization Design
The Yu
Daiqiao intersection is located in the Luo Mashi
CBD of Chengdu.
Before reconstruction
the roundabout model restricted the capacity,
traveling
conditions under flyover is
bad and the conflict between bicyclists and
pedestrians is
serious. The one-way
traffic control in western and northern entry
approaches
reduces the accessibility of
the road and results in excess concentration of
traffic flow.
The level of service of
the intersection is difficult to accommodate the
demand on
safety, efficiency,
convenience and environmentalism. Based on the
traffic demand
characteristic, the
channeling measures mainly include (tab.5):
Table 5The Yu Daiqiao intersection
traffic channelization design
Contrast
key points of traffic
channelization
Remove the roundabout flyover
for grade crossing.
Optimize
lanes
of
entry
and
After
exit.
Set traffic island to guarantee the
safe
and
convenientcrossing
of
pedestrians and bicyclists.
Set greening islands at the ends
of
traffic
island
toensure
the
safety
of
pedestrians
and
the
bicyclists
as
wellas
beautifying
urban
environment.
Divide
traveling
area
for
Before
traffic
channelization design
pedestrians and
the bicyclists.
Conduct
barrier
free
design
on
crosswalks
?
Set
storage
area
for
left
turn
vehicles
Set
legible
and
perfect
traffic
signs and markings
Set
isolation
pegs
to
reduce
conflicts
between
vehiclesand
the bicyclists.
4.2 Evaluation on traffic
channelization effect
A
quantitative
evaluation
is
conducted
by
combining
the
theoreticalcalculation
and
traffic
micro-simulation
(table.6).
Besides
the
safety
and
convenienceof
pedestrian
crossing is largely enhanced, the
landscaping benefit at intersection ispromoted,
and the
road accessibility is improved
through optimizing the organizationof traffic flow
while the
business turnover around the
area increases by 12 percents.
Table.6
Before & after effects of traffic channelization
at intersection
Items
Lane
Capacit
y(pcu/h)
Function
Before
After
Before
After
Befor
e
Eastern
entry
approac
h
Left
turn
Through
Right
turn
1
—
—
1
1
1
364
—
—
279
537
545
1.86
—
—
0.73
0.46
0.46
93
—
—
54
35
34
16
—
—
9
9
7
Saturati
on
After
Delay(s/c
ycle)
Before
After
Length(pcu
/cycle)
Before
After
Southern
Left turn
entry
approac
h
Western
entry
approac
h
Northern
Left turn
entry
approac
h
Through
Right
turn
Left
turn
Through
Right
turn
Through
Right
turn
—
2
1
2
2
1
—
560
945
787
642
750
—
1.21
0.96
0.89
0.77
0.89
—
46
21
41
31
19
—
13
5
9
8
4
1
2
2
1
2
2
910
1201
1850
279
537
503
1.13
1.55
0.64
0.73
0.90
0.70
65
52
23
59
46
20
3
15
5
3
12
4
—
—
1
2
—
1
—
—
—
207
640
678
—
—
—
0.98
0.62
0.62
—
—
—
65
36
31
—
—
—
6
7
3
footnote:“Before, After”
respectively represents before and after traffic
channelization.
Typical
urban traffic signal control system
With the development of computer and
automatic control technology, and the
consta
nt improvement of traffic flow
theory, transportation organizations and
optimization theory
continuously
improve the technical level, the function of
traffic control is enhanced more
and
more advanced means of control, the formation of a
number of high-level the effectiv
e
urban road traffic control system.
Representative countries in the world
most widely used and effective traffic control
s
ystem the TRANSYT systems, SCATS and
SCOOT system.
Domestic urban traffic
signal control system status
The late
start in China in the development and application
of traffic signal control sys
tem,
started in 1973 the development of single - point
signal control of urban traffic by-wir
e
system in
“Beijing
three
Main Street
As the
versity
has developed a set of the domestic intelligent
traffic signal control system, due to
various reasons, a number of important functions,
such as real-time adaptive timing did
not use. Tianjin University (1989-1991)
research and development of urban traffic control
system, TICS (Traffic Intelligent
Control system) successfully for the first time
since learni
ng intelligent principle
applied to the traffic signal control system.
Jilin University in 2000-
2002, under
the auspices of Professor Yang Zhaosheng research
and development of ur
ban mixed traffic
characteristics of adaptive signal control system
and its software mixed
traffic adaptive
signal control system, the system included bicycle
traffic flow detection, c
onsider
bicycle traffic during signal timing optimization
program with practical engineering
applications , and achieved good results.
Fuzzy logic - based urban traffic
control
Zadeh fuzzy set theory to
solve the complex process uncertainty provides a
concept
ual framework for those people
only a rough approximation of the process of
modeling an
d analysis provides a strong
conceptual basis, which can decision-making within
a certai
n range by artificial
experience. Has a very complex characteristics of
urban traffic control
, while the long-
term practice has accumulated a lot of experience
in manual control, whic
h is very
suitable for the application of fuzzy control
method.
Fuzzy control has become one
of the intelligent automation control study of the
most
active and fruitful fields and has
attracted the attention of the world's scientists.
A lot of fu
zzy technology products have
been applied in industrial and civil. In 1986, for
example, th
e fuzzy controller in Japan
already become a commodity. In 1987, based on
fuzzy control
Sendai subway opened,
then, various home appliances fuzzy successively
successfully
developed and put into the
market.
The research of significance
1, The theoretical significance
In-depth study of the traffic signal
control theory, control method of combining fuzzy
control and timing control, and
programmable controllers used in industrial
applicatio
ns mature, the design ideas
achieve regional control reference significance.
The implementation of the system to
maximize the use of the city existing
transportat
ion facilities, improve the
utilization of the road network, the effective
control of urban traff
ic conditions,
reduce traffic congestion, reduce environmental
pollution, improve economi
c efficiency
has important significance; while our smart ITS
research and development of
the
transportation system is also of great
significance.
The basic parameters of
the traffic signal control
Easy paper
described in this section is a brief introduction
to the basic terms of the tr
affic
signal control.
Signal cycle: for
directing traffic signal always step-by-step cycle
change a loop by a
finite number of
steps. Step and each step within a cycle is known
as a signal cycle, repr
esented by C. If
a loop n steps, each step by step length tl, t2,
?
?
?
, tn, then
C=t1+t2+···+tn
(2.1)
Phase : traffic control in order
to avoid conflict between the Intersection on the
traffic
flow in all directions, usually
using the method of time-sharing traffic
intersection on a one
-
-
-
-
-
-
-
-
-
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