Simulating Traffic Lights Control using Wireless Sensor Networks

Abstract

Wireless (magnetic) sensor networks offer a very attractive alternative to inductive loops for vehicular traffic control on freeways and at intersections in terms of cost, ease of deployment and maintenance, and enhanced measurement capabilities. In this work, we propose and simulate a simple and economic wireless sensor network architecture composed of only a single sensor node per lane, as a replacement to induction loops to be used in intelligent transportation systems. The results show that our work enhances the average vehicular waiting and travel times as compared with fixed-time signals, which produces significant change by a factor of almost 40%.

Full text

International Journal of Computer Applications (0975 8887)
Volume 104 - No. 12, October 2014
Simulating Traffic Lights Control using Wireless Sensor
Networks
Abdulmomen Kadhim Khlaif
Computer Engineering Department
University of Technology, Iraq
Muayad Sadik Croock, Ph.D
Computer Engineering Department
University of Technology, Iraq
Shaimaa Hameed Shaker, Ph.D
Computer Engineering Department
University of Technology, Iraq
ABSTRACT
Wireless (magnetic) sensor networks offer a very attractive al-
ternative to inductive loops for vehicular traffic control on free-
ways and at intersections in terms of cost, ease of deploy-
ment and maintenance, and enhanced measurement capabili-
ties. In this work, we propose and simulate a simple and eco-
nomic wireless sensor network architecture composed of only
a single sensor node per lane, as a replacement to induc-
tion loops to be used in intelligent transportation systems. The
results show that our work enhances the average vehicular
waiting and travel times as compared with fixed-time signals,
which produces significant change by a factor of almost 40%.
General Terms:
traffic lights, sensor network
Keywords:
Vehicular traffic control, Wireless sensor networks, Simuation,
Omnetpp, Sumo
1. INTRODUCTION
Traffic signals (or can be called as traffic lights, traffic control sig-
nals) control traffic by assigning right-of-way to one traffic move-
ment or several non-conflicting traffic movements at a time. Right-
of-way is assigned by turning on a green signal for a certain length
of time or an interval. Right-of-way is ended by a yellow change
interval during which a yellow signal is displayed, followed by the
display of a red signal.
The objective of traffic signal timing is to assign the right-of-way
to alternating traffic movements in such a manner to minimize the
average delay to any group of vehicles or pedestrians and reduce
the probability of accident producing conflicts. Some of the guiding
standards to signal timing can be listed as follows [1]:
—Minimize the number of phases that are used. Each additional
phase increases the amount of lost time due to starting delays
and clearance intervals.
—Short cycle lengths typically yield the best performance in terms
of providing the lowest overall average delay, provided the ca-
pacity of the cycle to pass vehicles is not exceeded. The cycle
length, however, must allow adequate time for vehicular and
pedestrian movements.
—When signals are coordinated with adjacent intersections, they
can provide for the continuous movement of traffic along a route
at a given speed.
—May reduce the occurrence of certain types of crashes, in partic-
ular, the right angle and pedestrian types.
Due to the computational complexity of traffic signals, a new jargon
has evolved to help signal professionals communicate efficiently.
These definitions are intended to make clear exposition in this arti-
cle as possible as to the reader [2].
—Traffic signal: Any power-operated device for warning or con-
trolling traffic, except flashers, signs, and markings.
—Approach: The roadway section adjacent to an intersection that
allows cars access to the intersection. An approach may serve
several movements.
—Right-of-way: The authority for a particular vehicle to complete
its manoeuvre through the intersection.
Intelligent transport systems (ITS), as been defined by [3] “are ad-
vanced applications which without embodying intelligence as such
aim to provide innovative services relating to different modes of
transport and traffic management and enable various users to be
better informed and make safer, more coordinated and ‘smarter’
use of transport networks.”
Wireless sensor networks consist of small sensor node devices that
communicate with each other to perform the required task. Because
of their constrained and compact shape, sensor nodes tends to have
unique challenges and constraints. These constraints effect the de-
sign of a wireless sensor network, leading to protocols and algo-
rithms that are different from their counterparts in other types of
systems (e.g., distributed systems) [4].
In this paper, the proposed system offers an efficient solution for
the traffic controls in terms of simple and economic ways in using
wireless sensor network. This is performed by distributing wireless
magnetic sensor along the right side of the included paths (lanes)
crossed at the underlying intersection. The collected readings of
the sensors are entered to the traffic control for processing and
decision making. The investigated system has been implemented
using Simulation of Urban MOblity (SUMO) [5] alongside with
OMNeT++ [6] simulators to accentuate the features of mixing ve-
hicular and network simulation. The obtained results explain good
performance of the proposed system in comparison with the con-
ventional methods (fixed-time traffic signals).
The following sections of the paper are organized as follows: sec-
tion 2 reviews the work that is related to traffic lights control us-
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International Journal of Computer Applications (0975 8887)
Volume 104 - No. 12, October 2014
ing wireless sensor networks. Section 3 gives the wireless sensor
network architecture and the traffic-actuated algorithm used in our
work. Section 4 provides the equipment parameters and network
setup and shows the results. Finally, section 5 concludes our work.
2. RELATED WORK
Regarding the field of intelligent transportation systems (ITS),
there are vast number of researches and work being developed un-
der the umbrella of traffic lights control using wireless sensor net-
works. In [7], an algorithm for traffic signals using sensors was
designed and implemented. This algorithm was implemented us-
ing MATLAB, whereas hardware simulation of the sensor nodes
were by LabVIEW. However, it did not show the vehicles behaviour
under the mentioned work (e.g., average waiting and travel time
of vehicles), nor shows the sensors communications-related data
(e.g., number of transmitted frames, frames collisions, MAC proto-
col used, etc.).
In [8], the authors used a wireless sensor networks of two mod-
els (one and two sensor nodes) and compared the performance be-
tween those models according to the average trip waiting time. The
authors did not provide telecommunications aspects of the sensor
nodes. In [9], an alerting system for red light crossing scenarios in
addition to the traffic light control algorithm presented for different
models, has been implemented to alert the drivers in other sides to
reduce the chance of accidents due to red light crossing violations
using sensors according to lane occupancies. It had not used spe-
cific type of sensors, instead, mentions types that can be used (ul-
trasonic vehicle detector or cameras) to calculate the queue (lane)
length. In [10], the authors addressed the intersection throughput
alongside with average vehicular waiting time by proposing an
adaptive traffic light control algorithm for isolated intersection run-
ning in multiple steps. Then, they compared the proposed algorithm
against fixed-time and traffic-actuated counterparts. Additionally,
the authors did not address the communications aspects nor specify
the type of sensors used to detect vehicles IDs and vehicles types.
In [11], a sensor network architecture that does not depend on
a centralized coordinator and separate it logically into four hi-
erarchical levels was proposed. These levels are final computa-
tions/decision (layer 4), intermediate computations (layer 3), de-
partures detection (layer 2), and arrivals detection (layer 1). It used
conflict matrix to specify the desired behaviour of each intersec-
tion. However, the cost of adding a leader election (when a sen-
sor’s battery drops below a threshold) and self-organizing protocols
were not explained enough, and no information about their bat-
teries consumption rate or sensors telecommunications properties
provided. The authors of [12] extended their previous work of [11]
with a special focus on communications and studying its reaction
to losses and delays induced by the use of wireless communication.
Although [11] provided a state-of-the-art work and proved the effi-
ciency and ease of implementation of their algorithm of [13] , their
work, and all previous [7, 8, 9, 10, 11], have not showed energy
consumption for sensor nodes batteries under their proposed sen-
sor network architecture and/or adaptive traffic signals algorithms.
3. TRAFFIC LIGHTS CONTROL ARCHITECTURE
The following section illustrates the proposed single sensor per lane
architecture and description about the inner layers of it; followed
by the traffic control algorithm that had been executed in order to
measure the effectiveness of our architecture.
Level 2
Level 1
Sensors
1: N arrival sensors
3: S arrival sensors
6: W arrival sensor
5: E arrival sensor
1 1
3
3
5
6
E
W
S
N
Fig. 1: Our hierarchical model.
3.1 Network Architecture
Our proposed wireless sensor network architecture is shown in
Fig. 1. It has no centralized station that coordinates the traffic con-
trollers’ behaviour, but rather, each traffic signal controls the in-
tersection locally without the help of external entity. It consists of
two levels of hierarchies only. Level 1 consists of one sensor node
per lane for detecting the presence of vehicle arrivals. Each sensor
node is encased in a 5” diameter glued into the pavement of the
lane. Vehicles are detected due to the change in the earth’s mag-
netic field caused by the arrival of the vehicles above the sensor
node [14]. Level 2 are the traffic signals that retrieve the sensor
nodes information and acts upon them. Level 2 are the traffic sig-
nals themselves. It means that, traffic signals are sensor nodes too,
but are externally powered, in contrast with level 1 sensors, which
use batteries (internally powered).
3.2 Traffic-actuated signals
Traffic-actuated control of isolated intersections attempts to adjust
green time continuously, and, in some cases, the sequence of phas-
ing. These adjustments occur in accordance with real-time mea-
sures of traffic demand obtained from vehicle detectors (sensor
nodes in this work) placed on one or more of the approaches to
the intersection. The full range of actuated control capabilities de-
pends on the type of equipment employed and the operational re-
quirements [1]. Fig. 2 shows its phasing diagrams [1]. This general
and simple detection algorithm acts as the main algorithm that our
architecture for this work has been applied to it.
Fig. 3 is the corresponding flowchart of it. As can be seen in the
flowchart, it is a continuous operation, since traffic lights should
control intersections all the time long.
3.2.1 Actuated phasing parameters. Each phase can either be
served or skipped. The decision to skip a phase occurs when there
is no vehicle on the detector when the previous phase turns yellow.
If a phase is served, it is served for a minimum period called the
minimum green or initial. After the initial, the phase will rest in
green until a car passes over a detector on a competing phase. At
that point, the phase can be terminated by one of two processes (in
our work) [2]:
Gap-out. As soon as a competing phase has been called, the
phase showing green will start a timer, called the extension timer,
which counts down from the extension value to zero.
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Fig. 2: Traffic-actuated phase timing diagram.
Beginning of phase due to
actuation or recall
Extensible green
Max Green? Sensed?
Yellow
Red
Gap?
Yes
Yes
Yes
Sensed?
Yes
Fig. 3: Flowchart of Fig. 2 (empty edges represent No output).
Max-out. If traffic is heavy, a sufficient gap to gap-out may not
occur in a reasonable period. Thus the actuated controller pro-
vides a maximum time to prevent excessive cycle lengths.
4. EXPERIMENTAL SETUP AND RESULTS
This study combines the results of two simulators, SUMO (de-
scribed below) that is used for vehicular simulation and OMNeT++,
which is a network simulator. Those two simulators were connected
Fig. 6: Induction loops placements.
so that information from SUMO been reflected back to OMNeT++,
on the same working area but with other type of simulation. The
following sections gives basic definitions of both SUMO and OM-
NeT++ and the area under study that have been simulated by them,
followed by the parameters that is been used for traffic signals
(fixed and actuated) in SUMO and the parameters of the channel
and wireless sensor nodes used in OMNeT++. Finally, the work
results are shown at the end of this section.
4.1 SUMO
SUMO, Simulation of Urban MOblity, is an open source, highly
portable, microscopic and continuous road traffic simulator de-
signed to handle large road networks [5]. Fig. 4 shows the map
of the city that is been used under study.
In order to use those maps with SUMO, the street/road type map
should be converted to SUMO map network file. The equivalent
SUMO map of the city is shown in Fig. 5.
4.1.1 Induction loops. Traffic-actuated signals vary their green
time based on demand at the intersection as measured on detec-
tors installed on the approach. These detectors vary in technol-
ogy, but the most common is the inductive loop detector. Induction
loops are provided in SUMO, so that when a particular vehicle is
passed above the induction loop, this information can be obtained
by means of TraCI, the short term for ”Traffic Control Interface”;
giving the access to a running road traffic simulation, it allows to re-
trieve values of simulated objects and to manipulate their behaviour
”on-line” [15]. Interfacing with TraCI can be done with Python or
Java programming languages1.
Induction loops (sensor nodes) placements. Induction loops (sen-
sor nodes) placements depends heavily on the traffic data for a
given intersection, the placements of induction loops inside SUMO
were 25 meters from the edge of the lane, so that the vehicle has
a chance of green time before the gap-out is been reached ( [16]
recommends a distance of 61 to 76.2 m in urban areas, but it also
says “distance depends on cycle length, split, and offset”). Fig. 6
shows the placement of induction loops (yellow rectangles) for one
of the intersections (intersection 3) in the city being simulated.
1This work uses Python interface with TraCI.
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(a) Sattellite map (b) Street/road map
Fig. 4: The city under simulation (Adhamiyah, Baghdad).
Fig. 5: SUMO network map for the city of Adhamiyah.
4.2 OMNeT++
OMNeT++ is an extensible, modular, component-based C++ sim-
ulation library and framework, primarily for building network sim-
ulators [6].
MiXiM is an OMNeT++ modelling framework created for mobile
and fixed wireless networks (wireless sensor networks, body area
networks, ad-hoc networks, vehicular networks, etc.) [17]. It offers
detailed models of radio wave propagation, interference estimation,
radio transceiver power consumption and wireless MAC protocols
(e.g. Zigbee).
This paper uses MiXiM framework for modelling the IEEE
802.15.4 wireless sensor nodes in OMNeT++. Wireless sensor
nodes retrieves the sensing information from the induction loops
of SUMO, by means of TCP communication between the two sim-
ulators.
Connecting SUMO and OMNeT++
In order to to transfer the presence of vehicles from induction loops
in SUMO to OMNeT++ sensor nodes, i.e., to connect the two sim-
ulators, inter-process communication by means of sockets has been
established between them. SUMO sockets were been programmed
in Python and OMNeT++ sockets were in C++. See Fig. 7.
4.3 Traffic signals parameters
Table 1 shows the parameters of both fixed-time and traffic-
actuated signals used in SUMO. Fixed-time signal parameters
Φ: Induction Loop ID: X
Sensor Node ID: X
SUMO side OMNeT++ side
x
(Inter-process communication)
x
φ
Fig. 7: Overview of simulators cooperation.
were default in SUMO, and traffic-actuated signals had been pro-
grammed and fine-tuned to get better results.
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Table 1. : Traffic signals parameters
Fixed-time signals
Parameter name Value (seconds)
Green time 31
Yellow Change Interval 9
Red time 56
Leading left green time 46
Leading left yellow interval 9
Cycle length 112
Traffic-actuated signals
Parameter name Value (seconds)
Minimum green 5
Gap-out 5
Max-out 20
Yellow Change Interval 2
Leading left green time 9
Leading left yellow interval 2
Table 2. : Sensor nodes parameters
Parameter name Value
Sensitivity −100 dBm
Maximum transmission power 1.1mW
Initial radio state TX
Use thermal noise true
Carrier frequency 2.4MHz
Modulation type MSK
MAC Protocol CSMA
Table 3. : Battery specifications
Parameter name Value
Capacity 6600 mAh
Voltage 3.3V
4.4 Sensor nodes parameters
MiXiM model framework in OMNeT++ implements the IEEE
802.15.4 narrowband protocol, which is being used as the protocol
for the wireless sensor nodes that sense the vehicles from SUMO,
and then send the data to its traffic signal in order to perform its in-
tended operation. Table 2 contains the specifications of those sensor
nodes.
Since wireless sensors nodes have no external power, i.e., they use
batteries, table 3 contains the battery specifications for the wireless
sensor nodes.
Other information that should be mentioned are the channel param-
eters, which are listed in table 4.
Traffic-actuated signals in OMNeT++ were simulated with param-
eters just like the sensor nodes, but without batteries, that is, they’re
externally powered (due to their conditions of consuming and pro-
cessing much more data than with sensor nodes, and due to the fact
the LED traffic lights cannot be powered by batteries for a very long
time, e.g., years). For setting the traffic signals phases in SUMO,
the Python interface of TraCI were used, instead of transferring
the decisions from OMNeT++ C++ code to SUMO Python, since
Table 4. : Channel parameters
Parameter name Value
Maximum sending power 2mW
used for this network
Minimum signal −100 dBm
attenuation threshold
Minimum path loss coefficient 2.5
traffic signals does not transmit any information or require specific
type of data to be simulated in OMNeT++.
4.5 SUMO results
One of the important parameters that is to be enhanced is the (av-
erage) vehicular waiting time. Fig. 8 shows the average vehicular
waiting and travel times of both fixed-time and traffic-actuated sig-
nals, where Vis the average velocity. The horizontal axes contains
the number of vehicles while the vertical contains the delay in sec-
onds.
There are six samples within each scenarios (six for fixed-time and
the same for traffic-actuated). In both scenarios, the same city (has
three intersections) and the number of vehicles and their parameters
(i.e., acceleration, length, maximum speed, driver’s imperfection,
etc...) were all the same prior to the operation of traffic signals, in
order to make the comparison with the same attributes.
As can be seen, traffic-actuated signals have better results than its
counterpart. The total average waiting time for fixed-time signal
is 495 seconds, while the other has 210 seconds, which shows
that traffic-actuated signals have enhancements by a factor of about
42%.
Induction loops (sensor nodes). Induction loops (sensor nodes in
OMNeT++) were the primary source of information in our simula-
tion results. Each induction loop in SUMO saves information to a
file by freq attribute, which is the aggregation period the values the
detector collects shall be summed up.
For our simulation study, which has three intersections, induction
loops from 0−8were assigned for the first intersection, 9−20 for
the second intersection and 21 −28 for the third intersection.
Fig. 9, 10 and 11 show the average lengths, occupancies and speeds,
respectively, of the vehicles, retrieved from induction loops infor-
mation for the second sample of the traffic-actuated signals (i.e.,
with the 1989 number of vehicles). As can be seen from the fig-
ures, there were a total of 29 (from 0−28 induction loops planted
in the city around the intersections. The horizontal axes contains
the identification numbers (ids) of induction loops (sensors).
It can be noticed that most of the sensed vehicles have a length of
5meters, which is the default vehicle length in SUMO. Our work
has added different lengths in order to have more ranges of lengths
in our simulation.
The vertical axes for the values of the occupancies is a percentage
(0−100%) of the time a vehicle was at the detector.
4.6 OMNeT++ results
After the wireless sensor nodes been defined in OMNeT++ and
been synchronized with induction loops of SUMO, another differ-
ent types of data were been given. Fig. 12 shows the number of
sensed vehicles for each wireless sensor node from the same second
sample of the simulation (i.e., with the 1989 number of vehicles).
As can be seen, sensor node with id of 11 has the highest detection
of vehicles (909).
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Fig. 8: Average waiting and vehicular times of traffic signals (Vis the average velocity).
Fig. 9: Average lengths of vehicles.
Fig. 10: Average occupancies of vehicles.
Fig. 11: Average speeds of vehicles.
Fig. 12: Number of sensed cars per sensor node.
Fig. 13, 14, 13 and 16 show the number of transmitted frames with
and without interference, the number of received frames (since the
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Fig. 13: Number of transmitted frames per sensor node.
Fig. 14: Number of frames with interference.
Fig. 15: Number of received frames per sensor node.
CSMA MAC protocol is used, that is, it tries to detect the presence
of a carrier wave from another node before attempting to transmit),
and the number of dropped frames, respectively, for each sensor
node.
Since sensor node 11 has the highest number of sensed vehicles,
it is not surprising that it is also the highest node for transmitting
frames.
Fig. 16: Number of dropped frames.
Fig. 17: Number of backoffs.
Fig. 18: Backoffs durations.
Because sensor nodes from 9−20 belong to the second intersection,
Fig 15 shows those nodes have the highest network traffic among
all other sensor nodes.
Since the MAC protocol used here is CSMA, when the medium is
busy, the node performs a backoff operation, that is, it waits for a
certain amount of time before attempting to transmit again.
Fig. 17 and 18 shows the relative backoff information of the sensor
nodes.
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5. CONCLUSION
Because of the diverse applications of sensor networks in every day
life, they’ve been employed in our work for traffic controls by re-
porting the traffic movements. We used them as means of enhanc-
ing traffic flow by reducing average vehicular waiting time, which
proves to be more efficient than with fixed-time signals. We pro-
posed and simulate a simple, cheap but efficient sensor network
architecture composed of only a single sensor node per lane. In or-
der to measure the effectiveness of our architecture, we executed a
simple actuated (adaptive) traffic algorithm so that to lengthen the
duration of sensors’ life (instead of a complex one), which can be
beneficial for countries that want to apply economic solutions to
traffic control. As a future work, we would like to simulate coordi-
nated traffic signals, as traffic signals would cooperate among them
to further enhance the traffic flow.
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