DriveBFR
Driver Behavior and Fuel-Efficiency-Based Recommendation System
Bibliographic Data
| ID | 22106988 |
|---|---|
| Authors | Jayant Vyas (0000-0002-9718-3338, Indian Institute of Technology Jodhpur), Debasis Das (0000-0003-0474-0842, Indian Institute of Technology Jodhpur), Santanu Chaudhury (0000-0002-5488-7773, Indian Institute of Technology Jodhpur) |
| Year | 2022 |
| Volume | 9 |
| Issue | 5 |
| Pages | 1446-1455 |
| Publication date | 2022-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3112076 |
| OpenAlex | W3203776324 |
| Language | EN |
| Citations received | 2 |
| References cited | 36 |
Despite the tremendous growth of the transportation sector, the availability of systems that ensure safe, efficient, sustainable transportation reduces traffic congestion, maintenance costs, the off-road time of the vehicle, enhances driver’s experiences, and ensures a more reliable journey are very limited. The fast evolution of our economy, lack of driver training, and the grown affordability of our society are reasons for this mismatch in developing economies. We think that the inconsistency will increase and unfavorably affect our traffic structure unless intelligent algorithm-based solutions are developed and deployed. This article presents a system for providing safe, accurate, comfortable, reliable, fuel-efficient, and economical driving behavior using Machine Learning techniques like the hidden Markov model (HMM). Our proposed system recommends subsequent trips using a multi-objective optimization (MOO) technique for the driver. It provides suggestions concerning speed limits and alerts based on the driver’s behavior score and fuel efficiency. We used a publicly available UAH-DriveSet dataset captured by the driving monitoring app DriveSafe for all of our experiments. The results reveal that the proposed model predicts behavior with 95% accuracy and calculates fuel efficiency to improve driving quality and experience. This system recommends safer, more comfortable, more reliable, more efficient, and economical rides, beneficial for everyone in our society
Advanced driver assistance systems · Business · Computer security · Intelligent transportation system · SAFER · Sustainability · Sustainable transport · Traffic congestion · Transport engineering · TRIPS architecture · Autonomous Vehicle Technology and Safety · Computer Science · Engineering · Traffic Prediction and Management Techniques · Vehicle emissions and performance · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |