Machine Learning-Driven Optimization of Driving Patterns Through for Fuel Efficiency

Abstract

Reducing fuel consumption and pollution requires optimising driving practices to boost fuel economy. This article explores how driving behaviours can be optimised for increased fuel efficiency using machine learning techniques. We employ supervised learning, reinforcement learning, and clustering to assess behaviours such as braking, acceleration, and route selection by examining vehicle sensor data. While real-time decision-making tools respond to shifting traffic conditions, our framework offers fleet management systems and individual drivers tailored advise on how to reduce fuel use. By analysing driving behaviours including speed maintenance, braking, and acceleration. We develop prediction models. To identify fuel driving techniques, machine learning techniques such as clustering, supervised learning and reinforcement learning are employed. In response to shifting traffic conditions, the proposed techniques modify driving advice.

Introduction

Overview

• Purpose: As global concerns about fuel use and emissions rise, optimizing driving habits has become vital.

• Traditional methods (e.g., driver training, route planning) are limited in adapting to real-time conditions.

• ML Solution: By analyzing data from vehicle sensors (speed, acceleration, braking, GPS), ML models can detect inefficient behaviors and recommend real-time adjustments to improve fuel efficiency, safety, and comfort.

2. Objectives

• Develop an ML-driven system to:

  • Analyze and optimize driving behavior.

  • Provide real-time feedback using data from sensors, GPS, traffic, and weather.

  • Combine AI and IoT for automated driving adjustments.

• Goal: Enable sustainable and intelligent transportation with reduced fuel consumption, emissions, and operational costs.

3. Literature Review

• Past Approaches:

  • Relied on rule-based and physics-based models (inflexible).

• ML Techniques:

  • Supervised learning: Regression, neural networks – predict fuel consumption.

  • Reinforcement learning: Optimizes braking and acceleration.

  • Clustering: Identifies driving patterns.

  • Hybrid models: Combine ML with physics models for better adaptability.

• Applications: Eco-driving assistance, fleet management, smart cities.

• Challenges: Data quality, real-world scalability, computational resources.

4. Methodology

• Data Sources:

  • Onboard sensors (speed, braking, fuel use), GPS, telematics.

  • External APIs (weather, traffic).

  • Fleet data (routes, behaviors).

• ML Techniques & Tools:

  • Regression, neural networks (TensorFlow, Scikit-learn).

  • Q-learning, Deep Q-Networks (OpenAI Gym).

  • Clustering & PCA.

  • Hybrid models and tools like Python, MATLAB.

5. Evaluation Metrics

• Prediction Metrics:

  • MAE (Mean Absolute Error)

  • MSE (Mean Squared Error)

• Classification Metrics:

  • Accuracy, Precision, Recall, F1-Score, Silhouette Score

• Real-World Metrics:

  • Fuel savings (%)

  • Emission reduction (%)

  • Driver adaptability to ML feedback

6. Results & Discussion

• Fuel Savings:

  • Regression: 10–15% gain (limited flexibility)

  • Reinforcement learning: 15–20% gain (high resource use)

  • Clustering: 8–12% gain (data-dependent)

  • Hybrid models: 12–18% gain (complex to tune)

• Driving Pattern Optimizations:

  • Speed smoothing: 8–12%

  • Predictive braking: 5–10%

  • Route optimization: 10–15%

  • Eco-driving assistance: 12–18%

7. Key Takeaways

• ML significantly improves fuel efficiency by modifying driving behaviors.

• Fleet management and eco-driving systems benefit most from ML integration.

• Future work should address real-world deployment challenges, particularly computational efficiency, data diversity, and integration with autonomous vehicles.

Conclusion

Fuel efficiency can be effectively increased by machine learning. By enhancing braking, acceleration, and route selection, strategies like clustering, reinforcement learning and hybrid models aid in the optimisation of driving Patterns. For better performance, these models interact with car systems, adjusts to changing situations and personalise recommendations. ML has demonstrated quantifiable advantages in fuel economy. This can help design policies for sustainable driving, improve driver training and use large data to make accurate decisions.ML driven systems have the potential to perform fuel efficiency and make transportation more economical and ecologically friendly with further advancements in AI and IOT. These models personalize recommendations, adapt to varying conditions, and integrate with vehicle systems for improved performance.

References

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Copyright

Copyright © 2025 Ms. Ravala Santhoshi, Ms. Palagiri Charishma, Ms. Panga Nandini, Ms. Singam Ushaswani, Mr. Pandetri Praveen, Dr. R. Karunia Krishnapriya, Mr. V Shaik Mohammad Shahil, Mr. N. Vijaya Kumar. 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.