Intelligent Traffic Management System

1. Intelligent Traffic Management
Using Wireless Sensor Networks
Ramakrishna Sakhamuri
Presentation by

2. Presentation Objective:
The main objective of this project is to maximize
the traffic flow by reducing the Average queue
lengths and Average wait times using dynamic
traffic flow data read from Wireless Sensor
Networks.
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Input AND Output
 Inputs are one time initializing variables in the Algorithm and Traffic flow data
from the WSN
 Output is Green and Red lights to the respective lanes.

4. Background:
 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 intersection.
 But these days traffic flows varying a lot for each road through out any
given day.
 The growing vehicle population in all developing and developed countries
calls for a major change in the existing traffic signaling systems.
 Hence we are in a great need of Traffic system which adapts to the varying
traffic flow and takes the decisions accordingly to reduce the queue
lengths and maximize the traffic flow.
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5. Introduction to the Intelligent Traffic
Management using Wireless Sensors:
 Intelligent Traffic Management System (ITMS) can also be called as Intelligent
Transportation System (ITS).
 ITS could be achieved using units like Wireless Sensor Networks (WSN), Base Station
(BS), Traffic Control Box (TCB) and algorithms like TCSA and TSTMA.
 WSN is nothing but a network of small nodes known as Traffic Sensor Nodes.
 WSN captures the traffic flow data and communicates the data to Base Station(BS).
A Communication system (TSCA) manages the communication from WSN to BS as
well as interfacing with TCB in a simple and efficient manner.
TCSA – Traffic System Communication Algorithm
 Traffic Signal Time Manipulation Algorithm (TSTMA) uses this data to calculate the
time for the lanes to be given Green and lanes to be changed to red light.
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6. Flow Chart showing the data flow in ITS
 Once TSTMA done with these calculations it gives this data to the TCB which
triggers the traffic lights accordingly.
continued…
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7. What is a Phase?
When intersections are signalized, that is, stop lights are installed, movements
are often lumped together to run at the same time. These sets of movements are
known as phases. There may be more than one movement served in a phase, but
at least one movement must be assigned.
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8. What are the possible Phases in an
intersection?
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9. Assumptions:
 The data regarding traffic volume from various lanes, requires for the
implementation of ITS algorithm, is assumed to be getting from the
WSNs. The working of WSN is not a part of this presentation.
 To formulate a solution, we assume that the right-turn (R) yield is
allowed all the times.
 In this presentation we will be talking only about four road intersection
and it can implied to lesser ones also.
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10. Architecture of ITMS
Traffic WSN complete architecture
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11. Proposed Methodology:
I. Notations used in Algorithm:
The notations depicting directions, lanes and phases are given as:
i. D= {North, South, East, West}.
ii. L= {Forward, Left}.
iii. P= {a,b,c,d,……,l}.
Here, D, L and P denote the set of directions, lanes and phases respectively
II. Expectant Phase:
Expectant Phase, denoted as Ef, of a vehicle is defined as the phase in which
vehicle pass the intersection.
i. Phase 01: Ef(E,F)=Ef(E,L)= fa
ii. Phase 02: Ef(W,F)=Ef(W,L)= fb
iii. Phase 03: Ef(S,F)=Ef(S,L)= fc
iv. Phase 04: Ef(N,F)=Ef(N,L)= fd
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12. v. Phase 05: Ef(E,L)=Ef(W,L)= fe
vi. Phase 06: Ef(E,F)=Ef(W,F)= ff
vii. Phase 07: Ef(N,L)=Ef(S,L)= fg
viii. Phase 08: Ef(N,F)=Ef(S,F)= fh
ix. Phase 09: Ef(E,F)=Ef(S,L)= fi
x. Phase 10: Ef(W,F)=Ef(N,L)=fj
xi. Phase 11: Ef(S,F)=Ef(W,L)= fk
xii. Phase 12: Ef(N,F)=Ef(E,L)= fl
continued…
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III.Lane Waiting Queue
Lane Waiting Queue, denoted as Q(d, l), defined
as the number of vehicles waiting on the path C= {d, l}
, d ∈ D and l ∈ L.
Q (N,L)
Q (W,F)

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IV. Phase waiting Queue
Phase Waiting Queue, denoted as Qx, is the length of queue for phase x
Qx = MAX( Q (d,l), Q (d̕,l̕) )
where { Ef (d,l) = Ef (d̕,l̕) = fx };
{d, d̕ } ∈ D ;
{l, l̕ } ∈ L ;
x ∈ P.
V. Queue Passing Time
Queue Passing Time, represented as TQ (d,l), is the time taken by all waiting
vehicles to pass the intersection.
TQ (d,l) = T1 + (ᴦ * ( Q(d,l) – 1 ))
T1 is the time taken by first vehicle to cross the intersection.
ᴦ is the time taken by a vehicle to move to the place of front vehicle.

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VI. Phase Queue Passing time
Phase Queue passing time, denoted as TQx , is calculated as-
TQx = MAX( TQ (d,l), TQ (d̕,l̕) )
where { Ef (d,l) = Ef (d̕,l̕) = fx };
{d, d̕ } ∈ D ;
{l, l̕ } ∈ L ;
x ∈ P.
VII. Waiting Time:
Waiting Time, WT ( d,l ), gives the waiting time for the vehicle in front.
VIII.Threshold Waiting Time:
Threshold waiting time, Tthreshold , sets the maximum waiting time.
IX. Phase Time :
Phase time, TPx, defines duration of green light for phase x
TPx = MIN (TQx , TPmax)

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X. Maximum Phase Time :
Maximum phase time, denoted as TPmax, gives the maximum time for
which green light is allotted.

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ITMS Algorithm:
//Initialization – Here parameters like Max Time and threshold time is initialized
1. Initialize Tmax = 90, Tthreshold =120.
Set Q(d,l) " (d,l), where d ∈ D, l ∈ L.
Q(d,l) contains the number of vehicles on the path C={d,l}
//Traffic Volume Detection – Computing the queue length for each lanes
2. Compute Qx = MAX(Q(d,l)) " x, where x ∈ {a,b,…,l}, d ∈ D, l ∈ L.
Qx contains the maximum queue length of each of the 12 phases
//Traffic Phase Selection
3. Compute WT(d,l) " (d,l), where d ∈ D, l ∈ L.
4. If there exists a path (d, l ) with WT (d, l )  Tthreshold then
5. Compute Qx = MAX(Q(d′,l′),Q(d,l)), such that {Ef(d,l)= Ef(d,l)=fx},
for every possible x, , x{a, b,c,…,l}, d  D ; l  L.
6. Assign green light to phase fx having maximum value of Qx computed
above, next.
And if more than one phase having the same value equal to MAX value of Qx "
x, then select the phase which is having the maximum Qx out of the other lanes
of those respective phases. If still finds more than one then pick one randomly.

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continued…
7. Else
Assign green light to phase fx having maximum value of Qx computed in step 2,
next.
And if more than one phase having the same value equal to MAX value of Qx "
x, then select the phase which is having the maximum Qx out of the other lanes
of those respective phases. If still finds more than one then pick one randomly.
8. End if
//Determination of Green Light Duration – Calculating the duration of green light
9. Compute TQx= T1 + (τ * (Qx -1)) for the phase selected above.
10. Compute TPx = MIN(TQx, TPmax).
11. Set the green light time for phase fx for time TPx.
Note : Qx as well as TQx changes dynamically according to the traffic flow.
Summary of Steps in ITMS Algorithm:
I. Determining the queue length (volume) of traffic.
II. Select most suitable phase to assign the green light.
III. Calculating how much time must be allotted to the phase

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