FleetPulse

Landing Page
Dashboard Main
Dashboard Main .1
Dashboard 2
Dashboard 2.1
Dashboard 2.2

Inspiration

FleetPulse was inspired by the urgent need to address climate change, particularly the significant impact of transportation on greenhouse gas emissions. According to the U.S. Environmental Protection Agency, transportation is responsible for approximately 29% of total greenhouse gas emissions, marking it as one of the largest contributors to pollution. With global vehicle usage projected to rise by over 30% by 2040, there’s an unprecedented opportunity to leverage technology to promote environmentally conscious driving behaviors.

Studies show that optimizing routes can reduce fuel consumption by up to 10%, and FleetPulse aims to amplify this impact with an AI-driven approach. By combining real-time data analysis with actionable recommendations, FleetPulse empowers both individuals and businesses to reduce their carbon footprints. The ultimate goal is to inspire a shift towards sustainable transportation solutions that combine convenience with ecological responsibility, targeting at least a 20% reduction in emissions through smarter driving practices and efficient route planning.

What it does

FleetPulse is an AI-powered web application that optimizes routes, monitors vehicle health, and educates drivers on eco-friendly practices. By analyzing real-time data from transportation networks and offering personalized recommendations, FleetPulse allows users to make informed decisions that lower fuel consumption and reduce environmental impact. Beyond route optimization, FleetPulse provides a holistic perspective on sustainable driving by delivering insights into vehicle maintenance and driver behavior. Key features include:

Fuel-Efficient Route Suggestions: Real-time route optimization to minimize carbon emissions, with predictive calculations comparing new routes to historical data. Vehicle Maintenance Prediction: AI-driven maintenance tracking across five key areas to prevent breakdowns and ensure safety. Driving Skills Analysis: Detailed driver evaluations highlighting strengths and improvement areas to encourage eco-friendly habits. Personalized AI Suggestions: Targeted recommendations for each driver using Claudflare’s GenAI, ensuring actionable insights based on historical data.

How we built it

FleetPulse was developed with a comprehensive and scalable tech stack to ensure optimal performance, seamless user experience, and accurate real-time analytics. Here's an overview of the core technologies used:

Tech Stack

Frontend: Next.js, for a fast, responsive, and user-friendly interface that adapts to various device screens.

Backend:

Databricks: Hosting and running multiple machine learning models, including Random Forest algorithms for vehicle maintenance prediction and driver skill analysis.

Cloudflare Workers & Cloudflare AI: Cloudflare provides robust, scalable hosting and API integration, ensuring security and speed in processing data and delivering insights.

Authentication: Clerk, used to handle secure, seamless user authentication.

Machine Learning Models:

Vehicle Maintenance Model: Tracks maintenance needs like engine, battery, tires, oil, and coolant, scored on a scale of 0-10 for proactive maintenance.

Driver Behavior Analysis Model: Evaluates driving behaviors such as acceleration, braking, and eco-driving to promote safer, more sustainable habits.

AI Integration: OpenAI’s GenAI for personalized driver suggestions, helping users improve their eco-driving techniques.

Data Sources: Real-time traffic data APIs, vehicle data, and user inputs for route optimization and emissions calculations.

Cloud Services FleetPulse is hosted on Cloudflare, leveraging its capabilities for rapid scaling, low latency, and secure data processing. Cloudflare Workers handle routing, security, and request processing efficiently, ensuring a smooth user experience even under high traffic.

Challenges we ran into

Developing FleetPulse presented several technical and logistical challenges:

Data Accuracy and Reliability: Ensuring precise data collection for machine learning models required rigorous testing and refinement. The reliability of traffic and vehicle data was essential for accurate maintenance and route suggestions. Real-time Synchronization: Integrating APIs for live traffic and vehicle data posed challenges in maintaining real-time synchronization without delays. Performance vs. Complexity: Balancing the complexity of multiple data-driven features with the need for quick response times required strategic architectural decisions to avoid lag.

Accomplishments that we're proud of

We’re proud of the fully functional FleetPulse prototype that provides actionable insights into carbon emissions, route optimization, and driver safety. Key accomplishments include:

Machine Learning Model Integration: Successfully implementing Random Forest models in Databricks for accurate maintenance and behavior predictions. Seamless Cloudflare Integration: Utilizing Cloudflare Workers and AI to handle data at scale, ensuring a responsive experience for users. Real-time Traffic and Emissions Analysis: The ability to analyze live data and make instant eco-friendly route suggestions for users, representing a significant step forward in sustainable transportation technology.

What we learned

Building FleetPulse provided invaluable insights into collaborative problem-solving and designing user-centric applications. Key lessons include:

Data Quality in Machine Learning: The importance of rigorous testing for data accuracy, especially in predictive analytics. User-Centric Design: Prioritizing features that address user needs made the application more effective and intuitive. Effective Communication and Project Management: Consistent team communication and clear project goals helped streamline the development process, ensuring timely progress.

What's next for FleetPulse

Looking ahead, FleetPulse aims to expand and enhance its functionality:

Predictive Analytics for Fuel Savings: Additional models to forecast potential fuel savings based on historical data, helping businesses set and track emission reduction goals. Partnerships with Fleet Management Companies: Integrating FleetPulse with existing fleet management systems to broaden its reach and impact. Community Insights Platform: A community-driven feature for sharing eco-driving tips, fostering a network focused on environmental responsibility. Expanded AI Capabilities: Further enhancing OpenAI GenAI integration to provide more detailed driver recommendations.

FleetPulse is committed to continuous improvement and growth, positioning itself as a vital tool in the shift toward sustainable transportation. Together, we can drive the future of eco-friendly mobility, one mile at a time.