Used Kerner three-phase traffic theory to establishing an Intelligent Traffic System that will provide automatic management of traffic lights based on the concept of the Internet of Things which will resolve the traffic jam issues which will in turn reduce CO2 emissions and also the mobility metrics like the travel time.

1. An IOT based
Dynamic Traffic
Signal Control
Submitted by: Gautham SK
1st Semester Mtech
Date:08-01-2020 in Computer Science
& Engineering
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2. CONTENTS
1. Introduction
2. Three phase traffic theory
3. Different Transition
4. Working
5. Implementation
6. Simulation
7. Results
8. Conclusion
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3. INTRODUCTION
• A traffic light is a device for the regulation of traffic between vehicles and pedestrians. The
setting and synchronization of traffic lights of an area are very complex, and sometimes
unsatisfactory.
• Several studies have shown that poorly regulated traffic lights are responsible for half of the
traffic jams leading to fuel consumption thereby releasing CO2 emission and other pollutants
causing pollution.
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4. SOLUTION
The solution is to apply the Kerner three-phase traffic theory to establishing
an Intelligent Traffic System that will provide automatic management of
traffic lights based on the concept of the Internet of Things which will resolve
the traffic jam issues which will in turn reduce CO2 emissions and also the
mobility metrics like the travel time.
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5. THREE PHASES TRAFFIC THEORY
• Three phase traffic theory was developed by a Russian physicist Boris Kerner who explains the
congestion by the phase transition in traffic system.
• He considered traffic as a complex system having similarities with physical particle flow. For
example, One can treat a single vehicle like a particle flowing through pipe.
• The three phases in traffic consist :
-> Free flow
-> Two congestion phases:
Synchronized flow
Wide moving jam
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6. Three parameters for understanding the features of three phases:
-> Velocity v
-> Density ρ
-> Flow rate q of vehicles.
The related intuitive expression can be given as:
q=ρv
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7. Fundamental Diagram
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8. F->S transition
• The transition from free flow to congested state(synchronized flow) is called breakdown
phenomenon.
• When the density reaches limit point(maximum density of free flow), the transition from free
flow to synchronized flow occur.
• The main reason are deceleration of vehicle, lane changing, any random behavior of drivers
can cause this fluctuation. This is called metastability of the free flow.
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10. S->J transition
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11. Working
• As in the method of Kerner, the calculation of two parameters indicate the traffic condition,
the density and the flow of traffic.
• These two parameters can be obtained by the sensors are installed on the edges of two
intersecting roads and are transferred to the traffic light controller, here the data's are
processed and turns ON the green light for the road which is in congested state and turns ON
the red light to the other, till it becomes synchronized state.
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12. IMPLEMENTATION OF THE SYSTEM
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13. SIMULATION (SUMO)
• The approach is conceived by the means of simulation experiments. The analysis is done using
SUMO (Simulation of Urban Mobility), a open source simulator which refers to urban mobility.
• It allows the user to build a custom route topology and also generate a real traffic simulation
and as the capabilities to import road networks of different cites from OpenStreetMap.
• In order to calculate pollutant emissions, we use the EMIT model a statistical model for
instantaneous emissions and fuel consumption of vehicles integrated in SUMO.
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14. SIMULATION ALGORITHM
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15. RESULTS
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17. CONCLUSION
• From the experimental results, I would like to conclude that in terms of the
performance the technique proposed, takes the lead compared with the
standard traffic model and offers the opportunity to exploit the terms of
smart mobility with a contribution for more efficient traffic and cleaner
environment.
• As future work, plan to design a routing protocol inspired by this approach,
while ensuring a good performance of network metrics.
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