Telematics and Usage-Based Insurance (UBI) Tutorial

Telematics and Usage-Based Insurance (UBI) use data collected from devices such as GPS trackers or smartphone apps to assess the behavior of policyholders (e.g., how safely they drive, how often they drive, etc.) to calculate premiums. By monitoring driving habits, insurers can offer personalized premiums based on real-time usage rather than traditional risk factors (such as age or location).

In this tutorial, we’ll create a basic telematics-based UBI system that:

• Collects simulated telematics data (e.g., speed, distance traveled, time of day).
• Analyzes the driving behavior (e.g., average speed, harsh braking, acceleration).
• Calculates a personalized insurance premium based on the data.

We’ll use:

• Python for data simulation and analysis.
• Flask for the backend API to handle telematics data and provide a premium estimate.
• HTML/CSS/JavaScript for the frontend to simulate collecting data and displaying results.

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Tutorial Overview:

1. Telematics Data Simulation
2. Backend API with Flask
3. Insurance Premium Calculation Logic
4. Frontend Interface (HTML/CSS/JS)
5. Testing the System

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Step 1: Telematics Data Simulation

We will simulate telematics data for a car that tracks:

• Speed (km/h)
• Distance traveled (km)
• Time of travel (e.g., day or night)
• Harsh braking events

For simplicity, we’ll simulate a few trips using random data.

1.1 Generate Telematics Data (Python)

pythonCopy codeimport random
import json
from datetime import datetime, timedelta

# Generate random telematics data
def generate_telemetrics_data():
    trips = []
    for i in range(5):  # Simulate 5 trips
        trip = {
            "trip_id": i + 1,
            "start_time": (datetime.now() - timedelta(days=i)).isoformat(),
            "end_time": (datetime.now() - timedelta(days=i, hours=random.randint(1, 5))).isoformat(),
            "distance_km": random.uniform(10, 50),  # Random distance between 10 to 50 km
            "avg_speed_kmh": random.uniform(40, 120),  # Random average speed between 40 and 120 km/h
            "harsh_braking": random.randint(0, 3),  # Random number of harsh braking events
            "time_of_day": "day" if random.randint(0, 1) == 1 else "night"  # Random time of day (day or night)
        }
        trips.append(trip)
    return trips

# Save data to JSON file (simulating collected telematics data)
telemetrics_data = generate_telemetrics_data()
with open("telemetrics_data.json", "w") as f:
    json.dump(telemetrics_data, f, indent=4)

print("Telematics data generated and saved!")

Explanation:

• We simulate 5 trips, each with:
  • Start and end times (randomly generated).
  • Distance traveled (between 10 to 50 km).
  • Average speed (between 40 to 120 km/h).
  • Harsh braking events (random number between 0 and 3).
  • Time of day (day or night).
• This is a simple example to simulate telematics data; you can replace this with actual telematics device data in a real-world scenario.

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Step 2: Backend API with Flask

We’ll now create a Flask backend API that will:

• Receive telematics data (e.g., from the frontend or directly from a telematics device).
• Analyze the data and calculate the risk score.
• Calculate the insurance premium based on the user’s driving behavior.

2.1 Install Flask

If you haven’t already, you need to install Flask and Flask-RESTful for creating the API:

bashCopy codepip install Flask Flask-RESTful

2.2 Create the Flask Application (app.py)

pythonCopy codefrom flask import Flask, request, jsonify
import json

app = Flask(__name__)

# Define a function to calculate insurance premium based on telematics data
def calculate_premium(telemetrics_data):
    base_premium = 100  # Base premium (this is a hypothetical starting point)
    risk_score = 0
    
    # Analyze telematics data
    for trip in telemetrics_data:
        # Calculate risk based on speed
        if trip["avg_speed_kmh"] > 100:  # Risk factor if average speed is greater than 100 km/h
            risk_score += 20
        elif trip["avg_speed_kmh"] < 50:  # Risk factor if average speed is lower than 50 km/h
            risk_score += 5
        
        # Calculate risk based on harsh braking
        risk_score += trip["harsh_braking"] * 10
        
        # Calculate risk based on time of day (driving at night might increase risk)
        if trip["time_of_day"] == "night":
            risk_score += 15
    
    # Calculate final premium: base premium + risk score
    final_premium = base_premium + risk_score
    return final_premium

# Define an endpoint to receive telematics data and return the calculated premium
@app.route('/calculate-premium', methods=['POST'])
def calculate():
    try:
        # Get the telematics data from the request
        data = request.get_json()
        
        # Calculate the premium based on the data
        premium = calculate_premium(data["telemetrics_data"])
        
        # Return the calculated premium as a JSON response
        return jsonify({"premium": premium}), 200
    except Exception as e:
        return jsonify({"error": str(e)}), 400

if __name__ == '__main__':
    app.run(debug=True)

Explanation:

• The calculate_premium function takes the telematics data and calculates a personalized insurance premium based on the user’s driving behavior.
• The premium is influenced by:
  • Speed: Higher speeds lead to higher premiums.
  • Harsh braking: More harsh braking events increase the premium.
  • Time of day: Driving at night increases the premium.
• The API expects the telematics data to be sent in the body of the POST request.

Example of the telematics data structure:

jsonCopy code{
    "telemetrics_data": [
        {
            "trip_id": 1,
            "start_time": "2023-10-10T08:00:00",
            "end_time": "2023-10-10T08:45:00",
            "distance_km": 30,
            "avg_speed_kmh": 80,
            "harsh_braking": 1,
            "time_of_day": "day"
        },
        {
            "trip_id": 2,
            "start_time": "2023-10-11T19:00:00",
            "end_time": "2023-10-11T20:00:00",
            "distance_km": 45,
            "avg_speed_kmh": 110,
            "harsh_braking": 2,
            "time_of_day": "night"
        }
    ]
}

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Step 3: Insurance Premium Calculation Logic

• The premium is calculated based on the driving behavior.
• Risk Score: The system calculates the risk score by analyzing each trip’s data:
  • High-speed driving results in higher premiums.
  • Harsh braking and driving at night increase the premium due to higher risk.
• The final premium is calculated as the base premium plus the risk score.

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Step 4: Frontend Interface (HTML/CSS/JS)

We will create a simple frontend to allow users to input telematics data and see the calculated insurance premium.

4.1 Frontend (index.html)

htmlCopy code<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Telematics & UBI Insurance</title>
    <link rel="stylesheet" href="style.css">
</head>
<body>
    <div class="container">
        <h1>Telematics & Usage-Based Insurance (UBI)</h1>
        <form id="telematicsForm">
            <label for="tripId">Trip ID:</label>
            <input type="number" id="tripId" required>

            <label for="distance">Distance (km):</label>
            <input type="number" id="distance" required>

            <label for="avgSpeed">Average Speed (km/h):</label>
            <input type="number" id="avgSpeed" required>

            <label for="harshBraking">Harsh Braking Events:</label>
            <input type="number" id="harshBraking" required>

            <label for="timeOfDay">Time of Day:</label>
            <select id="timeOfDay">
                <option value="day">Day</option>
                <option value="night">Night</option>
            </select>

            <button type="submit">Submit Telematics Data</button>
        </form>

        <div id="premiumResult"></div>
    </div>

    <script src="script.js"></script>
</body>
</html>

4.2 Styles (style.css)

cssCopy codebody {

4o mini