14-08-2012, 10:00 AM
Adaptive Traffic Light Control with Wireless Sensor Networks
Adaptive Traffic Light Control with Wireless Sensor Networks.pdf (Size: 238.52 KB / Downloads: 65)
Abstract
In this paper, we propose a novel decentralized
traffic light control using wireless sensor network. The system
architecture is classified into three layers; the wireless sensor
network, the localized traffic flow model policy, and the higher
level coordination of the traffic lights agents. The wireless
sensors are deployed on the lanes going in and out the
intersection. These sensors detect vehicles’ number, speed,
etc. and send their data to the nearest Intersection Control
Agent (ICA) which, determines the flow model of the intersection
depending on sensors’ data (e.g., number of vehicles
approaching a specific intersection). Coping with dynamic
changes in the traffic volume is one of the biggest challenges in
intelligent transportation system (ITS). Our main contribution
is the real-time adaptive control of the traffic lights. Our aim
is to maximize the flow of vehicles and reduce the waiting
time while maintaining fairness among the other traffic lights.
Each traffic light controlled intersection has an intersection
control agent that collects information from the sensor nodes.
An intersection control agent manages its intersection by
controlling its traffic lights. Multiple intersection agents can
exchange information among themselves to control a wider
area.
I. INTRODUCTION
We envision a smart road system were the total trip time
is minimum due to minimizing the average waiting time on
traffic lights. In addition to minimizing the average traffic
waiting time, we would like to see a road system which
can optimize the traffic flow by utilizing the free roads.
Tremendous amount of time and power is wasted due to a
green traffic light with no cars passing on its lane.
Many solutions were proposed to solve the traffic jam.
Most conventional traffic surveillance systems use intrusive
sensors, including inductive loop detectors, micro-loop probes,
and pneumatic road tubes. However, these sensors disrupt
traffic during installation and repair, which leads to a high
cost installation and maintenance. In addition, over the ground
sensors like videos, radars, and ultrasonic were used. These
systems are also high cost and their accuracy depends on
environment condition [1]
This paper presents a real-time adaptive system based on
wireless sensors that has the potential to establish a new era of
traffic control and surveillance because of its low cost and potential
for large scale deployment. Our system consists, mainly,
of the wireless sensor network and the intersection control
agents. The wireless sensor network composed of group of
nodes, each comprising one or more sensors, a processor, a
radio and a battery. They generate traffic information such as
number of cars, speed and length of the vehicles, based on
processing of the sensor data. The information is then sent to
the nearest intersection control agent over the radio. The intersection
control agent collects the information from the sensor
nodes to analyze traffic conditions and take actions such as
adjusting the traffic light durations or exchanging information
with other intersection agents for better optimization of traffic
flow.
In the field of Multiagent Systems (MAS) [2][3], controlling
intersections is studied with intelligent system on mind. Although
these systems has the potential to revolutionize traffic
surveillance they are still far from being adopted by the ITS.
Using wireless sensor network along with intelligent transportation
system is still in its preliminary stage. To compete
with current technologies, however, the data provided by the
system must be accurate, delivered to the traffic intersection
agents within a certain time for real-time applications, and the
lifetime of the system must be on the order of several years.
We use Green Light District (GLD) 1 simulator [4] to test
our model. GLD allows us to create road maps and add our
own intersection traffic flow policy.
In the next section, we briefly review some approaches that
coordinate traffic lights. Section 3 presents wireless sensor
networks model. Section 4 discuses intersection control agent.
In section 5 discuss the simulation and we conclude in section
6.
II. RELATED WORK
To replace the costly and high maintenance classic traffic
surveillance such as inductive loops, Cheung et al. [1] built
a traffic surveillance technology system based on wireless
sensors. Their system is deployed in freeways and at intersections
for traffic measurements such as vehicle count,
occupancy, speed, and vehicle classification which can’t be
1GLD is an open-source software and can be downloaded from
{http://sourceforgeprojects/stoplicht/}
1-4244-0667-6/07/$25.00 © 2007 IEEE 187
Authorized licensed use limited to: INDIAN INSTITUTE OF SCIENCE. Downloaded on June 22, 2009 at 09:33 from IEEE Xplore. Restrictions apply.
obtained from standard inductive loops. The experiments in
[1] shows that deploying wireless sensor network for traffic
monitoring provides %99 of detection rate in real time.
Using wireless sensor network for transportation applications
provides measurements with high spatial density and
accuracy. A network of wireless magnetic sensors [1] offers
much greater flexibility and lower installation and maintenance
costs than loop, video or radar detector systems.
Chen et al. [5] propose a prototype of Wireless sensor
network for Intelligent Transportation System (WITS). WITS
system is used for the information gathering and data transferring.
In this system three types of WITS nodes are used;
1) the vehicle unit on the individual unit, 2) the roadside unit
along both sides of road, and 3) the intersection unit on the
intersection. The vehicle unit measures the vehicle parameters
and transfers them to the roadside units. The roadside unit
gathers the information of the vehicles around, and transfers
it to the intersection unit. The intersection unit receives and
analyzes the information from other units, and passes them to
the strategy sub-system, which in turn calculates an appropriate
scheme according to the preset optimization target (such
as maximum throughput, minimum waiting time, etc.) Mainly,
the intersection unit wants to know how many vehicles in every
lane will reach the intersection before the signal phase ends.
But there is no enough discussion about how this information
helps the intersection unit.
Hull et al. [6] designed a mobile distributed sensor computing
system called CarTel. A CarTel node is a mobile embedded
computer coupled to a set of sensors. Each node gathers and
process sensor readings locally before delivering them to a
central portal, where the data is stored in a database for further
analysis and visualization. In general, CarTel makes it easy
to collect, process, deliver, and visualize heterogenous data
from a intermittently connected mobile nodes. In [6] CarTel
is deployed on six cars for over a year to analyze commute
times, metropolitan Wi-Fi deployments, and for automative
diagnostics. Although this system has potentials for smoother
commute time by collecting information about the traffic, but
it does not solve the traffic problem. That is, only vehicles
with CarTel node can benefit from this system.
III. WIRELESS SENSOR NETWORK MODEL
In this section, we discuss the wireless sensor network
model [1] we will use in our system.
A. Sensor Node hardware
The sensor nodes consist of a processor, a radio, a magnetometer,
a battery and a cover for protection from the vehicles.
The microprocessor is Atmel ATmega128L with 128kB of
programmable memory and 512kB of data flash memory. It
runs TinyOS, an operating system developed at UC Berkeley,
from its internal flash memory. TinyOS enables the single
processor board to run the sensor processing and the radio
communication simultaneously.
The radio is ChipCon CC1000 916MHz, frequency shift
keying (FSK) RF transceiver, capable of delivering up to
40kbps. The RF transmit power can be changed in software.
There are two HMC1051Z magnetic sensors, based
on anisotropic magnetoresistive (AMR) sensor technology.
To receive one sample, the magnetometer is active for 0.9
msec and the energy spent for taking one sample is 0.9J.
The magnetometer is turned off between samples for energy
conservation. The battery is Tadiran Lithium TL5135, with
1.7Ah capacity in a compact size. The entire unit is encased
in a SmartStud cover, designed to be placed on pavement and
able to withstand 16,000 lbs. So the node is protected and can
be glued on anywhere on the pavement.
B. Vehicle Detection
We use magnetometer sensor for vehicle detection. The
sensor detects distortions of the Earths field caused by a large
ferrous object like a vehicle. Since the distortion depends
on the ferrous material, its size and orientation, a magnetic
signature is induced corresponding to the vehicles shape and
configuration.
For detecting the presence of a vehicle, measurements of the
(vertical) z-axis is a better choice as it is more localized and
the signal from vehicles on adjacent lanes can be neglected.
C. Communication protocol
Several proposals have been advanced for random access
schemes to reduce the effects of energy consuming operations
such as constantly listening to the channel, overhearing
packets not destined for them, and transmissions collisions.
These proposals achieve power savings up to a factor of 10
at the cost of considerable increase in hardware or control
complexity. The TDMA schemes on the other hand are more
power efficient since they allow the nodes in the network to
enter inactive states until their allocated time slots. However,
previously proposed TDMA schemes do not take advantage
of the fact that all sensor data are destined for a single access
point and introduce distributed synchronization overhead.
We adopt PEDAMACS (Power Efficient and Delay Aware
Medium Access Protocol for Sensor Networks) [1] for our
traffic system. PEDAMACS is a TDMA scheme that discovers
the topology of the network and keeps the nodes synchronized
to validate the execution of a TDMA schedule. It is designed to
meet both delay and energy requirements of traffic applications
by exploiting the special characteristics of sensor networks.
The data at the sensor nodes in the wireless network is
periodically transferred to a distinguished node called access
point (AP) for purposes of control. The AP then transfers the
data to the traffic management center. Moreover, the sensor
nodes have limited (transmit) power and energy, but the access
point is not so limited. Consequently, communication from
nodes must travel over several hops to reach the access point,
but packets from the access point can reach all nodes in a
single hop.
PEDAMACS protocol operates in four phases: the topology
learning phase, the topology collection phase, the scheduling
phase and the adjustment phase. In the topology learning
phase, each node identifies its (local) topology information,
1-4244-0667-6/07/$25.00 © 2007 IEEE 188
Authorized licensed use limited to: INDIAN INSTITUTE OF SCIENCE. Downloaded on June 22, 2009 at 09:33 from IEEE Xplore. Restrictions apply.
i.e. its neighbors and its interferers, and its parent node in
the routing tree rooted at the AP obtained according to some
routing metric. In the topology collection phase, each node
sends this topology information to the AP so, at the end
of this phase, the AP knows the full network topology. At
the beginning of the scheduling phase, the AP broadcasts a
schedule. Each node then follows the schedule: In particular,
the node sleeps when it is not scheduled either to transmit a
packet or to listen for one. The adjustment phase is included
if necessary to learn the local topology information that was
not discovered in topology learning phase or that changed,
depending on the application and the number of successfully
scheduled nodes in scheduling phase.
The determination of the schedule based on the topology
of the network at the AP is performed according to the
PEDAMACS scheduling algorithm. The scheduling algorithm
ideally should minimize the delaythe time needed for data
from all nodes to reach the access point. However, this
optimization problem is NP-complete. PEDAMACS instead
uses a polynomial time scheduling algorithm which guarantees
a delay proportional to the number of packets in the sensor
network to be transferred to the AP in each period. The
algorithm assigns a group of non-conflicting nodes to transmit
in each time slot, in such a way that the data packets generated
at each node reaches the AP by the end of the scheduling
frame.