intelligent traffic management

Intelligent Traffic Management Using Wireless Sensor Networks

Ramakrishna Sakhamuri

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Presentation Objective:

The main objective of this project is to maximizethe traffic flow by reducing the Average queuelengths and Average wait times using dynamictraffic flow data read from Wireless SensorNetworks.

2Intelligent Traffic Management System

Intelligent Traffic Management System 3

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.

Background:

The most prevalent traffic signaling system in developing countries is thetimer based system. This system involves a predefined time setting foreach road at an intersection.

But these days traffic flows varying a lot for each road through out anygiven day.

The growing vehicle population in all developing and developed countriescalls for a major change in the existing traffic signaling systems.

Hence we are in a great need of Traffic system which adapts to the varyingtraffic flow and takes the decisions accordingly to reduce the queuelengths and maximize the traffic flow.


Introduction to the Intelligent TrafficManagement 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 aswell 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 thetime for the lanes to be given Green and lanes to be changed to red light.


Flow Chart showing the data flow in ITS

Once TSTMA done with these calculations it gives this data to the TCB whichtriggers the traffic lights accordingly.

continued…


What is a Phase?

When intersections are signalized, that is, stop lights are installed, movementsare often lumped together to run at the same time. These sets of movements areknown as phases. There may be more than one movement served in a phase, butat least one movement must be assigned.


What are the possible Phases in an intersection?


Assumptions: The data regarding traffic volume from various lanes, requires for the

implementation of ITS algorithm, is assumed to be getting from theWSNs. The working of WSN is not a part of this presentation.

To formulate a solution, we assume that the right-turn (R) yield isallowed all the times.

In this presentation we will be talking only about four road intersectionand it can implied to lesser ones also.


Architecture of ITMS

Traffic WSN complete architecture10Intelligent Traffic Management System

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 whichvehicle 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


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…


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)


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, d ̕ } ∈ D ;{l, l ̕ } ∈ 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.


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)


X. Maximum Phase Time :

Maximum phase time, denoted as TPmax, gives the maximum time for

which green light is allotted.


ITMS Algorithm://Initialization – Here parameters like Max Time and threshold time is initialized1. 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 lanes2. 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 Selection3. Compute WT(d,l) " (d,l), where d ∈ D, l ∈ L.4. If there exists a path (d, l ) with WT (d, l ) Tthreshold then5. 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.


continued…

7. ElseAssign 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 light9. 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


Bibliography

1. http://psrcentre.org/images/extraimages/313113.pdf

2. http://ezproxy.latech.edu:2063/stamp/stamp.jsp?tp=&arnumber=5594435

3. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, 753-768 (2010)

4. International Journal of Computer Science & Information Technology

http://psrcentre.org/images/extraimages/313113.pdf

http://ezproxy.latech.edu:2063/stamp/stamp.jsp?tp=&arnumber=5594435

intelligent traffic management

Design

traffic lights

prevalent traffic

traffic volume

existing traffic

varying traffic flow

traffic sensor nodes

dynamic traffic flow

days traffic flows