This document proposes an emergency vehicle route optimization system using graph theory and Dijkstra's algorithm. The system converts road network data into a graph with nodes representing areas and edges representing travel time. Dijkstra's algorithm is then used to determine the shortest path for emergency vehicles by calculating the total travel time along routes using weights assigned to edges. The aim is to reduce emergency vehicle travel time and enhance effectiveness of routes through congested traffic situations.

1. “Emergency Vehicle Route
Optimization System: Ensuring Rapid
Response in Critical Situations”
ANANYA V 1CD20AI004
MALAVIKA G 1CD20AI028
MEHAK FATHIMA 1CD20AI033
Project Guide: Dr. Rajani K C

2. PROBLEM STATEMENT
“Emergency Vehicle Route Optimization
System: Ensuring Rapid Response in
Critical Situations”
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3. ABSTRACT
● This study focuses on optimizing traffic flow in busy areas to facilitate the
movement of emergency vehicles efficiently.
● The proposed method utilizes graph theory, converting data into a graph with
nodes representing areas and edges representing travel time.
● The total travel time is calculated using these weights, and Dijkstra's algorithm
is employed to determine the shortest path for emergency vehicles.
● “This approach aims to reduce travel time and enhance the effectiveness of
emergency vehicle routes in congested traffic scenarios”
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4. INTRODUCTION
• In this modern era approximately 60% of the people get struck in the
traffic in their day-to-day life which results in major problems.
• To introduce an algorithm that preciously finds the shortest path and
guide the emergency vehicle to reach the destination at faster rate.
• The solution helps not only emergency vehicle but also the common
people who wishes to reach their destination on time.
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5. LITERATURE SURVEY
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Paper Title Journal
Name
Methodology
State-of-the-art review in traffic
control-strategies for emergency
vehicles
IEEE Involves deep reinforcement learning methods and the A* algorithm as
Dijkstra algorithm for optimizing vehicle routes in complex urban
environments.
Emergency Logistics Scheduling
Under Uncertain Transportation
Time Using Optimization
Methods.
IEEE MOEA, which stands for Multi-Objective Evolutionary Algorithm, is a
methodology used in the field of computational optimization to solve
problems with multiple conflicting objectives or criteria
A routing model for emergency
vehicles using the real time
traffic data
IEEE This paper proposes a method for estimating total travel time in a
Vehicle Routing Problem (VRP) network.

6. OBJECTIVES
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●Real-Time
Updates
●Minimize
Response Time
●Enhance
Safety
●Integration
with GIS and
Mapping
●Public
Awareness and
Communication
●Consideration
of Special
Circumstances

7. SYSTEM REQUIREMENTS ANALYSIS
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• Processor
• RAM
• Hard Disk
HARDWARE
REQUIREMENTS
• Operating System : Windows
• Platform : Jupyter Notebook
• Backend : Anaconda
• Language : Python
SOFTWARE
REQUIREMENTS

8. SYSTEM ARCHITECTURE
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9. DESIGN
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●GRAPH ALGORITHM
●DIJKSTRA’S ALGORITHM
●TIME ALGORITHM

10. GRAPH ALGORITHM
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• This method is based on graph theory, where the road network is
first converted into graph with nodes and edges.
• Here, nodes represent the area and the edge represents the time
required to travel through.
• The edges are given with different weight i.e. the distance and the
traffic percentage in that route.

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12. DIJKSTRA’S ALGORITHM
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• Dijkstra’s algorithm is used to find the shortest path among all the
available paths.
• This algorithm uses the weights of the edges to find the path that
minimizes the total distance between the source node and all other
nodes.

14. TIME ALGORITHM
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• The time taken to move from one location to another is obtained from the calculation of the
distance and weightage of the traffic in that route along with the standard average speed
calculation.
T = D / (S – ((S*P)/100))
D - Distance between two nodes
P - Percentage of traffic flow
S - Average speed
T – Time taken to reach destination

15. CONCLUSION
• In conclusion, it is evident that in the current world with developing
technology, efficiency and reliability are critical.
• The system developed shows that the fastest route from one point to
another can be determined with a suitable algorithm and also helps
to save many lives by its efficient traffic flow control methodology.
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16. REFERENCE
● W. Yu, W. Bai, W. Luan and L. Qi, "State-of-the-Art Review on Traffic Control Strategies for Emergency
Vehicles," in IEEE Access, vol. 10, pp. 109729-109742, 2022, doi: 10.1109/ACCESS.2022.3213798.
● X. Zhang, X. Yu and X. Wu, "Exponential Rank Differential Evolution Algorithm for Disaster Emergency
Vehicle Path Planning," in IEEE Access, vol. 9, pp. 10880-10892, 2021, doi: 10.1109/ACCESS.2021.3050764.
● Z. -L. Wang, Z. -y. Feng, G. -z. Rao, Y. Wang and C. Zhang, "Public emergency oriented SOA-based logistics
system," 2010 International Conference on Computer Application and System Modeling (ICCASM 2010),
Taiyuan, China, 2010, pp. V1-588-V1-591, doi: 10.1109/ICCASM.2010.5620634.
● Y. Zhang and J. Liu, "Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online
Optimization Methods," in IEEE Access, vol. 9, pp. 36995-37010, 2021, doi: 10.1109/ACCESS.2021.3061454.
● N. Rathore, P. K. Jain and M. Parida, "A ROUTING MODEL FOR EMERGENCY VEHICLES USING THE
REAL TIME TRAFFIC DATA," 2018 IEEE International Conference on Service Operations and Logistics, and
Informatics (SOLI), Singapore, 2018, pp. 175-179, doi: 10.1109/SOLI.2018.8476771.
● H. Wen, Y. Lin and J. Wu, "Co-Evolutionary Optimization Algorithm Based on the Future Traffic Environment
for Emergency Rescue Path Planning," in IEEE Access, vol. 8, pp. 148125-148135, 2020, doi:
10.1109/ACCESS.2020.3014609.
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17. THANK YOU
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