Traffic Signal Control and Management System

Abstract

The congestion of vehicles on the road is increasing day by day and also the management of such large traffic by traditional approach isn\'t adequate enough. In today\'s scenario the traditional approach works efficiently only if the count is sparse, as the density of vehicles on a particular side of road increases or if the traffic is comparatively larger on one side than other side in such case the approach fails. Hence, we aim to redesign the traffic signal system that is static switching to signal switching, which can performs real-time signal monitoring and handling. So, in this project the switching time of signal will be decided based on real time image detection with good accuracy in dense traffic. This practice can prove its most effectiveness in releasing the congested traffic at an efficient and faster rate.

Introduction

I. INTRODUCTION

With the exponential growth of urban populations, the challenges associated with traffic congestion have become increasingly prevalent, necessitating innovative solutions to optimize traffic flow and enhance overall commuting experiences. In the complex landscape of urban traffic management, conventional methods encounter significant challenges, necessitating a paradigm shift towards more intelligent and adaptable solutions.

Traditional manual control, dependent on a substantial workforce, becomes impractical given the limited strength of traffic police. Scaling this approach across the expanses of a city or town proves logistically challenging, prompting the exploration of alternative, more efficient systems.

Likewise, static traffic control, governed by fixed timers, lacks the agility to respond dynamically to real-time traffic variations, resulting in suboptimal traffic flow regulation. In recent years, the integration of video monitoring and surveillance systems has become integral to the field of traffic management, contributing significantly to various aspects such as security and real-time information dissemination for travelers. This technological advancement has enabled the development of sophisticated mechanisms for traffic optimization and congestion alleviation.

One of the key advantages of employing video monitoring in traffic management is its ability to influence the operation of traffic signals. By utilizing the data obtained through traffic density estimation and vehicle classification, authorities can dynamically adjust the timers of traffic signals. This adaptive approach allows for the optimization of traffic flow in real-time, ensuring that signal timings align with the actual demand on the road. Consequently, this responsive control mechanism aims to minimize congestion and enhance the overall efficiency of the transportation network. Furthermore, the information gathered through video monitoring systems goes beyond mere traffic signal control. These systems serve as a foundation for comprehensive traffic management strategies, providing a basis for informed decision-making. For instance, authorities can use the data to implement targeted interventions, reroute traffic, or deploy resources effectively during peak hours. 2 In line with this trajectory of innovation, our proposed system aims to revolutionize traffic light control through the implementation of Computer Vision technology.

By harnessing live feeds from CCTV cameras situated at traffic junctions, the system dynamically calculates realtime traffic density. What sets our system apart is its utilization of the YOLO algorithm, enabling efficient vehicle detection. This granular information is pivotal for precisely estimating the optimal green signal time. The envisioned system doesn't merely stop at detection; it seeks to redefine traffic control by dynamically adjusting signal timers based on the live traffic conditions. This adaptability promises to optimize green signal times, facilitating a more efficient and rapid clearance of traffic compared to static systems. In essence, the proposed traffic light controller represents a significant leap towards intelligent and adaptive traffic management, addressing the limitations of conventional methods and aligning with the urgent need for sustainable and efficient urban mobility. The ultimate goal is to curtail delays, minimize congestion, and reduce waiting times, thereby contributing to lower fuel consumption and a decrease in pollution levels.

II. MODEL ARCHITECTURE

A. Proposed Model

Our system works by using images captured by CCTV cameras at traffic junctions. It's like taking a snapshot of the traffic. Once we have this picture, our system steps in to determine how busy the road is right at that moment. This is important because it helps us manage the traffic better. We employ a special tool called YOLO, which stands for "You Only Look Once." YOLO is like a digital detective that quickly scans the picture to count the number of vehicles. It gives us a clear idea of how packed the road is with vehicles. After YOLO does its counting, our system considers this information, along with a few other factors. It then makes decisions about how long each traffic signal should show green or red. Think of it as our system orchestrating the traffic lights to match the actual flow of vehicles on the road. In essence, it's a smart way of adapting traffic signals to the real-time conditions on the road, making sure everyone moves efficiently and smoothly. This innovative approach aims to reduce congestion and make the whole traffic experience much better for everyone on the road.

B. Signal Switching Model

The Signal Switching Module plays a pivotal role in our traffic management system, functioning as the conductor orchestrating the traffic lights at intersections. When the CCTV cameras at traffic junctions capture real-time images, this module springs into action. Utilizing the YOLO algorithm, it efficiently detects the number of vehicles. This data is then fed into the Signal Switching Algorithm, a smart decision-maker that considers various 3 factors in determining how long each traffic signal should stay green or red. One of the key considerations in this algorithm is the traffic density calculated from the detected vehicles. It takes into account the number of lanes, the total count of vehicles for each class, and the time lag experienced by vehicles, especially those at the back during start-up. The average speed of each vehicle class when the green light starts, and the minimum and maximum time limits for green light duration, are also factored in. The algorithm, scalable for any number of signals at an intersection, ensures a comprehensive and dynamic approach to traffic signal control.

The system employs a clever multitasking approach, with a main thread handling the current signal's timer and a separate thread managing the vehicle detection for the next signal. This ensures a seamless transition between signals, preventing any delays. The cyclic switching pattern, mirroring the traditional Red → Green → Yellow → Red sequence, maintains simplicity and familiarity in traffic signal operation. In essence, the Signal Switching Module will stand as a testament to the system's adaptability and efficiency in optimizing traffic flow based on real-time conditions, ultimately contributing to a smoother and more responsive urban traffic management experience.

III. APPLICATIONS

1. Urban Traffic Management: Implementing adaptive traffic signals in busy urban areas addresses the perennial issue of traffic congestion. By dynamically adjusting signal times based on real-time traffic conditions, the system aims to alleviate bottlenecks and enhance the overall flow of vehicles. This application holds the potential to significantly reduce travel times, mitigate delays, and improve the efficiency of urban transportation networks.
2. Smart Cities:  As part of a broader smart city initiative, the adaptive traffic signal system contributes to creating more intelligent and sustainable urban environments. By optimizing traffic flow, the system plays a crucial role in enhancing the overall quality of life for city residents. This initiative aligns with the vision of smart cities, where technology is harnessed to improve various aspects of urban living, including transportation and environmental sustainability.
3. Public Transportation: The adaptive traffic signal system can be instrumental in coordinating traffic signals to prioritize public transportation modes such as buses or trams. By giving preference to these vehicles, the system facilitates more efficient and reliable public transportation services. This not only encourages the use of public transport but also contributes to reducing traffic congestion and environmental impact associated with individual vehicle usage.
4. Emergency Response: In emergency situations, every second counts. The adaptive traffic signal system can be a valuable tool for emergency response services. By quickly adjusting signal timings to create a clear path for emergency vehicles, the system aids in navigating through traffic more efficiently. This application is crucial for improving emergency response times and ensuring the swift movement of ambulances, fire trucks, and police vehicles.
5. Tourist Areas: Tourist destinations often experience fluctuations in visitor numbers, especially during peak seasons. The adaptive traffic signal system can adapt to these seasonal variations, optimizing traffic flow based on the varying demand. This not only enhances the experience for tourists by minimizing travel delays but also ensures that local traffic adapts to the changing dynamics of tourist influx, contributing to a smoother and more organized traffic management in these areas.

Conclusion

In conclusion, our proposed traffic management system is designed to adaptively set green signal times based on real-time traffic density at intersections. The idea is to give more green signal time to the direction with heavier traffic and less time to the one with lighter traffic. This strategic adjustment aims to minimize delays, reduce congestion, and cut down on waiting time, contributing to lower fuel consumption and pollution. While our system is still in the development phase, initial simulation results are promising. We acknowledge that further refinement and calibration are needed, and we plan to enhance the system\'s performance by utilizing actual CCTV data during the development process. What sets our system apart is its cost-effectiveness. Leveraging existing CCTV cameras at traffic signals eliminates the need for additional hardware, making it a budget-friendly solution. This stands in contrast to other traffic monitoring systems, like pressure mats, which often require significant investments and can incur higher maintenance costs due to wear and tear. As our system evolves, we envision seamless integration with citywide CCTV networks, particularly in major urban areas, providing a practical and efficient approach to enhance traffic management. While the system is still in the developmental stages, we believe that, once implemented and fine-tuned, it has the potential to revolutionize traffic control methods, offering a sustainable and cost-efficient solution for better traffic flow in our cities.

References

[1] Craig B. Rafter, Bani Anvari, Simon Box, Tom Cherrett “Augmenting Traffic Signal Control Systems for Urban Road Networks With Connected Vehicles” IEEE transactions on Intelligent Transportation Systems, vol. 21, no. 4, April 2020. [2] “Global traffic scorecard,” INRIX, Kirkland, WA, USA, Tech. Rep. 1, 2017. [3] B. Waterson and S. Box, “Quantifying the impact of probe vehicle localisation data errors on signalised junction control,” IET Intell. Transp. Syst., vol. 6, no. 2, p. 197, 2012. [4] D. I. Robertson, “TRANSYT: A traffic network study tool,” Transp. Road Res. Lab., Tech. Rep. LR253, 1969. [5] G. R. Vincent and J. R. Peirce, “MOVA: Traffic responsive, selfoptimising signal control for isolated intersections,” Transp. Road Res.Lab., Tech. Rep. RR170, 1988. [6] M. G. Bell, “Future directions in traffic signal control,” Transp. Res. A, Policy Pract., vol. 26, no. 4, pp. 303–313, Jul. 1992. [7] P. Diakaki, D. Kotsialos, and Wang, “Review of road traffic control strategies,” Proc. IEEE, vol. 91, no. 12, pp. 2041–2042, Dec. 2003. [8] S. J. Agbolosu-Amison, A. W. Sadek, and W. Eldessouki, “Inclement weather and traffic flow at signalized intersections: Case study from Northern New England,” Transp. Res. Rec., vol. 1867, no. 1,pp. 163–171, Jan. 2004. [9] H. C. Council, “Intelligent transport systems strategy package report,” Hertfordshire County Council, Hertford, U.K., Tech. Rep. 1, 2011. [10] P. Hunt, D. Robertson, R. Bretherton, and R. Winton, SCOOT: A Traffic Responsive Method Coordinating Signals. Crowthorne, U.K.: TRRL, 1981. [11] P. R. Lowrie, “Scats, sydney co-ordinated adaptive traffic system: A traffic responsive method of controlling urban traffic,” Roads Traffic Authority New South Wales, Darlinghurst, Austrailia, Tech. Rep. 1, 1990. [12] R. Engineering, “InSync: The traffic Bot,” Rhyhtm Eng., Lenexa, KS, USA, Tech. Rep. 1, 2019

Copyright

Copyright © 2024 Viraj Tapkir, Mitesh Shetkar, Shubham Shinde, Sonali Dalvi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.