The document discusses adaptive traffic control systems (ATCS). ATCS automatically adjust traffic light timings based on real-time traffic conditions to maximize traffic flow and reduce congestion. Sensors collect traffic data which is analyzed to determine vehicle clusters and improve signal timings. Current ATCS use intelligent computing methods and are being implemented in various cities to detect congestion, synchronize lights, update timings and speeds in real-time, reducing traffic. Future areas of development include emergency vehicle detection, signalless networks, and dynamic routing guidance.

1. ADAPTIVE
TRAFFIC CONTROL SYSTEM
Nelson Kandel
BCE 3rd year
Pulchowk Campus

2. Presentation Outline
INTRODUCTION
PRESENT
DEVELOPMENTS
ADVANTAGES
FUTURE SCOPES

3. Traffic Control System
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4. • supervision of the movement of people, goods,
or vehicles to ensure efficiency and safety.
INCLUDES FOLLOWING ASPECTS:
• road design and marking
• barriers or channelizers
• signs
• signals
Traffic Control System
https://play-lh.googleusercontent.com/emzYfny8_fEnhPqEQ
slXw9XMOVsXU_KjkBFewXI7BV3FuNEs-WJN4sxQocB6YTVpxcQ
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5. Adaptive Traffic Control System (ATCS)
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6. • automatically adapts the timings of traffic lights
based on real-time traffic conditions
• allows signals to interact with each other
• maximize vehicular movement and reduce
congestion
Adaptive Traffic Control System (ATCS)
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https://www.efftronics.com/imgv2/smart-cities/smart-
transport/atcs/atcs-solution.png

7. How does ATCS Work?
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8. • use of cameras/ sensors to acquire traffic data
• raw data processed to determine the vehicular clusters
• data evaluated to improve signal timings at a particular junction.
How does ATCS Work?
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9. Present Development Status
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10. • The comprehensive index method considers the delay, the number of
stops, and the length of queues to obtain the best overall efficiency of
the network.
Present Development Status
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METHODS BASED ON MATHEMATICAL MODEL

11. • The comprehensive index method considers the delay, the number of
stops, and the length of queues to obtain the best overall efficiency of
the network.
• The green wave band method is designed to maximize the number of
nonstop platoons of the main line.
Present Development Status
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METHODS BASED ON MATHEMATICAL MODEL

12. • Fuzzy Logic
• Genetic Algorithms and Ant Colony Optimization
• Artificial Neural Network (ANN)
• Reinforcement Learning Control
Present Development Status
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METHODS BASED ON INTELLIGENT COMPUTING

13. Present Development Status
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ATCS IS ACTIVELY USED IN FOLLOWING PLACES:
• Smart City of Gandhinagar, India
• Washington DC, District of Colombia, USA
• Smart Traffic in Kent, USA
https://www.efftronics.com/adaptive-traffic-control-system

14. Advantages over conventional practices
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15. • Detecting congestion.
• Synchronizing activity between traffic lights.
• Updating traffic light timing in real time.
• Updating and informing drivers of ideal speeds.
• Reduce congestion and time spent on the road.
Advantages over conventional practices
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16. Future Scopes of ATCS
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17. • Emergency Vehicle Detection
• Signalless Road Network
• Automatic Fining
• Dynamic Route Guidance
Future Scopes of ATCS
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18. • ATCS is exciting as well as rewarding topic to be adapted.
• It is a multidisciplinary topic.
• It may help a lot in regulating traffic in developing countries.
• Development of this technology is never ending anytime soon.
Conclusion:
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19. • [1] Robertson, D. I. (1969). TRANSYT: a traffic network study tool.
• [2] Bell, Michael G.H. (1992). Future directions in traffic signal control. Transportation Research Part A:
Policy and Practice..
• [3] N. H. Gartner and C. Stamatiadis. (2002). Arterial-based control of traffic flow in urban grid networks,
Mathematical and Computer Modelling, vol. 35.
• [4] Pham, V. C., Alam, F., Potgieter, J., Fang, F. C., & Xu, W. L. (2013). Integrated fuzzy signal and
ramp-metering at a diamond interchange. Journal of Advanced Transportation, vol. 47
• [5] Yizhe Wang, Xiaoguang Yang, Hailun Liang, Yangdong Liu. (2018). A Review of the Self-Adaptive
Traffic Signal Control System Based on Future Traffic Environment, Journal of Advanced Transportation,
vol. 2018
• [6] Hoogendoorn, S. P., Knoop, V. L., & van Zuylen, H. J. (2008). Robust control of traffic networks under
uncertain conditions. Journal of advanced transportation, 42(3).
• [7] Zlatkovic, M., & Zhou, X. (2015). Integration of signal timing estimation model and dynamic traffic
assignment in feedback loops: system design and case study. Journal of Advanced Transportation, 49(6).
References
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