A Novel Approach for Improved Traffic Management for Emergency Service Vehicles

Abstract

The rise in traffic congestion is one of the challenges in emergency response services. The surge in traffic congestion has led to unprecedented obstacles for emergency response services. Public safety is under threat due to the substantial number of causalities caused by the delay of emergency response vehicles. This paperproposesanIntelligentTrafficLightSystemforemergencyservicevehi- cles. This paper proposes an advanced traffic management system to prioritize emergencyvehicles.Theproposedsystemcanmodifytrafficsignalstoensureaseamlesspassageforemergencyvehicles.Theexperimentalanalysisreveals that the proposed approach uses a mathematical time-sequencing algorithm to reduce emergency response time, optimize traffic flow, and elevate road safety standards. The proposed algorithm is validated via simulations. The real-time deployment can be achieved using YOLOv8 and CNN. The proposed approach intends to encourage the efficient implementation of an intelligent traffic manage- mentsystem.Theresultsconsistofacomprehensivestudyoftheperformance of the proposed system in different scenarios and a comparative time analysis againsttheconventionalsystem.Thepresentedalgorithmsuccessfullyreduced the response time of the emergency services in various simulated circumstances. This paper includes mitigation strategies to overcome the possible challenges ofthe proposed system. The proposed approach aims to enhance the urban traffic management system by enabling emergency services to respond promptly with-out human input. The presented design intends to ensure reliability in urgent situationsbyprovidinginnovativesolutionstoconventionaltrafficsystems.

Introduction

Summary

The paper proposes an intelligent traffic management system that dynamically adjusts traffic light signals to ensure the smooth and safe passage of emergency vehicles through congested urban intersections. As urbanization increases traffic density, emergency response times are often hindered due to traditional, inflexible traffic signal systems.

Key Features of the Proposed System:

1. Dynamic Signal Adjustment:

   • Traffic lights automatically switch based on real-time emergency vehicle detection.

   • A blue light is introduced as a unique indicator for incoming emergency vehicles to alert all directions at the intersection.

2. Emergency Vehicle Detection Methods:

   • Camera-based (OpenCV + YOLOv8 + CNN): Uses existing surveillance infrastructure.

   • GPS + GSM Modules: Tracks emergency vehicles using coordinates.

   • Sound Detection: Identifies sirens using deep learning and 1D CNN models, though less practical.

3. Traffic Signal Flow:

   • Upon detection, all signals flash blue for 5 seconds to alert other drivers.

   • The lane with the emergency vehicle receives a green signal, while others turn red.

   • System resets post vehicle passage, returning to normal flow.

Algorithm and Implementation:

• Developed using Python, Pygame, and Threading.

• Simulates different congestion levels (low, normal, high) with randomized vehicle generation and object-oriented programming.

• Key performance metrics: time taken by emergency vehicles to cross intersections.

Simulation Results:

• Low Congestion: Avg. 11 sec

• Normal Congestion: Avg. 14 sec

• High Congestion: Avg. 15 sec

• Outperforms conventional systems (existing times up to 66 sec) in all scenarios.

• Reduces emergency response times while minimally disrupting regular traffic.

Comparison with Existing Systems:

• Better than RFID, GPS, and IR sensor methods as it:

  • Uses existing infrastructure (surveillance cameras).

  • Requires no modifications to vehicles or roads.

  • Is more cost-effective and easier to implement.

• Offers greater scalability, especially for underdeveloped regions lacking advanced infrastructure.

Conclusion

Implementation of this system will be beneficial for urban environments. Anunob- structed route for emergency vehicles will significantly reduce response time. This system adjusts traffic signals, optimizes traffic flow and diminishes congestion, which benefits everyday commuters. Furthermore, it contributes to environmental sustain- ability by reducing halts, thus reducing fuel consumption and emissions. It also supports smart city initiatives by utilizing advanced technologies for urban infras- tructure and constant improvements. It is cost-efficient, scalable and adaptable for variousscenarios.Additionally,publicsafetyisensuredbyspreadingawarenessregarding emergencies and promoting obedience to traffic rules. Conclusively, its ability to integrate with existing systems and smart city initiatives make it more reliable and supports widespread urban development strategies.

References

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Copyright

Copyright © 2025 Dhruvil Patel, Jiya Raval, Manav Bachani, Ritika Lohiya. 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.