Ambulance Dispatch Route Optimizer

A comparative analysis of pathfinding algorithms for emergency vehicle dispatch systems, implementing and benchmarking Dijkstra's algorithm versus A* search for optimal ambulance routing.

📊 Key Findings

• Dijkstra outperformed A by 16%* on small road networks (< 50 nodes)
• Both algorithms achieve sub-12ms routing times for 100 emergency calls
• Heuristic overhead exceeded search space reduction benefits in dense, small networks

🚑 Project Overview

This system simulates emergency ambulance dispatch operations, processing prioritized emergency calls and computing optimal routes from staging locations to incident sites. The project compares two fundamental shortest-path algorithms to determine which performs better for real-world emergency response scenarios.

Features

• Priority-based dispatch queue using min-heap for call triage
• Real-time route calculation with traffic delay considerations
• Comparative algorithm analysis with performance benchmarking
• Comprehensive dispatch logging with route details and response times
• Graph-based road network representation with bidirectional edges

🛠️ Technology Stack

• Language: Python 3.x
• Data Processing: Pandas
• Algorithms: Dijkstra's Algorithm, A* Search
• Data Structures: Priority Queue (heapq), Graph (adjacency list)

📁 Project Structure

ambulance-dispatch-optimizer/
├── README.md
├── requirements.txt
├── config.py                    # Configuration and file paths
├── data_structures.py           # Core data structures
├── data_loader.py              # CSV data ingestion
├── dijkstra_dispatcher.py     # Dijkstra implementation
├── astar_dispatcher.py        # A* implementation  
├── performance_tester.py      # Benchmarking suite
└── data/
    ├── ambulance.csv          # Ambulance staging locations
    ├── calls.csv              # Emergency call data
    ├── call_priority.csv      # Call type priorities
    └── location_network.csv   # Road network graph

🚀 Getting Started

Prerequisites

python --version  # Python 3.7 or higher
pip --version     # pip package manager

Installation

1. Clone the repository:

git clone https://github.com/Maxencejules/ambulance-dispatch-system.git
cd ambulance-dispatch-optimizer

2. Install dependencies:

pip install -r requirements.txt

Running the Simulation

Run Dijkstra's Algorithm:

python dijkstra_dispatcher.py

Run A Algorithm:*

python astar_dispatcher.py

Run Performance Comparison:

python performance_tester.py

📈 Performance Results

Benchmark Summary (100 Emergency Calls, 10 Runs Each)

Algorithm	Average Time	Min Time	Max Time	Variance
Dijkstra	0.007652s	0.004628s	0.009766s	53%
A*	0.008879s	0.006393s	0.011228s	43%

Performance Difference: Dijkstra performed 16% faster (1.227ms improvement)

Why Dijkstra Outperformed A*

1. Network Scale: With only 42 edges and ~20 nodes, overhead costs dominate
2. High Connectivity: Average degree of 4.2 means most destinations are 3-4 hops away
3. Heuristic Overhead: A* requires coordinate lookups and distance calculations (~0.012ms/node)

🔬 Algorithm Implementations

Dijkstra's Algorithm

• Strategy: Greedy best-first expansion using min-heap
• Complexity: O((V+E)log V) time, O(V) space
• Guarantee: Optimal paths for non-negative edge weights
• Best for: Small networks, consistent performance requirements

A* Algorithm

• Strategy: Informed search using f(n) = g(n) + h(n) scoring
• Heuristic: Conservative Manhattan distance with admissibility guarantee
• Complexity: O((V+E)log V) worst case, O(b^d) average
• Best for: Large sparse networks with good spatial structure

🎯 System Design

Core Components

1. EmergencyCall: Tracks call details with priority levels
2. Ambulance: Manages vehicle state and availability
3. CallPriorityQueue: Min-heap implementation for call triage
4. RoadNetwork: Graph representation using adjacency lists

Dispatch Workflow

1. Load road network and ambulance staging locations
2. Ingest emergency calls with priority mapping
3. Process calls in priority order (lower number = higher priority)
4. For each call:
   • Calculate routes from all available ambulances
   • Select ambulance with minimum response time
   • Log dispatch details and reset ambulance

💡 Future Improvements

Dijkstra Optimizations

• Bidirectional Search: Expected 40-45% improvement
• Early Termination: Stop at destination (20-30% improvement)
• Route Caching: LRU cache for common routes (60-70% of calls are recurring)

A* Enhancements

• Improved Heuristic: Landmark-based or true road distance
• Weighted A (ε=1.5):* Trade optimality for speed
• Hierarchical Preprocessing: Multi-scale heuristics

System Extensions

• Real-time traffic updates
• Multiple ambulance coordination
• Dynamic ambulance repositioning
• Visualization dashboard

📊 Data Format

Input Files

ambulance.csv:

Ambulance Number,Staging Location
Ambulance 1,Hospital A
Ambulance 2,Fire Station B

calls.csv:

Call ID,Location,Call Type
1,Intersection D,Stroke
2,Address 456 Oak St,Cardiac Arrest

location_network.csv:

Start,End,Distance,Travel Time,Traffic Delay
Hospital A,Intersection B,2.5,3.2,1

🔍 Key Insights

The 1.227ms performance difference between algorithms is negligible compared to real-world factors:

• Ambulance travel time: 5-15 minutes
• Dispatch decision time: 15-30 seconds
• Communication overhead: 30-60 seconds

Recommendation: Use Dijkstra for networks under 100 nodes, A* for larger networks where guided search provides measurable benefits.

📚 References

• Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271.
• Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107.
• Cormen, T. H., et al. (2009). Introduction to Algorithms (3rd ed.). MIT Press.

📄 License

This project is available under the MIT License. See LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.

👤 Author

Maxence Jules

• GitHub: Maxencejules(https://github.com/Maxencejules)
• LinkedIn: Maxence Jules(https://linkedin.com/in/julesmax)
• Email: powe840@gmail.com

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This project demonstrates algorithm selection considerations for emergency response systems, showing that theoretical complexity isn't everything - implementation details and network characteristics significantly impact real-world performance.