A Review of Deep Learning Applications in Energy-efficient Transportation Systems

Abstract

Deep learning has emerged as a powerful tool in the development of energy-efficient transportation systems. This study investigates the applications of deep learning in the context of improving energy efficiency in transportation, with a particular focus on Intelligent Transportation Systems (ITS), vehicle diagnostics, energy management systems, fleet management, and predictive maintenance. Through a systematic analysis of these applications, the study aims to assess the potential of deep learning algorithms in optimizing transportation systems and reducing energy consumption.The first application explored is Intelligent Transportation Systems (ITS), where deep learning algorithms are employed to analyze traffic data from cameras and sensors. The findings indicate that deep learning can effectively optimize traffic flow, reduce congestion, and enhance fuel efficiency while simultaneously decreasing emissions. Furthermore, deep learning algorithms have demonstrated their capability to optimize routes and minimize energy consumption in autonomous vehicles, offering great potential for energy-efficient transportation systems.Vehicle diagnostics represent another critical area where deep learning can contribute to energy efficiency. The study reveals that deep learning techniques are capable of efficiently detecting faults in vehicles and accurately predicting maintenance requirements. By enabling early intervention, deep learning facilitates the reduction of energy wastage caused by inefficient vehicles, ensuring optimal performance and minimizing energy consumption.Energy management systems, particularly in the context of electric vehicles and public transportation systems, can significantly benefit from deep learning algorithms. Through the analysis of energy consumption patterns and the prediction of energy demand, battery life, and charging requirements, deep learning enables effective optimization of energy consumption in transportation systems. The results indicate that deep learning algorithms have the potential to enhance the energy efficiency of electric vehicles and public transportation systems, leading to reduced operational costs and improved sustainability.Fleet management represents a crucial aspect of transportation systems, and the study demonstrates that deep learning can be instrumental in optimizing fleet operations. By accurately predicting vehicle usage patterns and optimizing routes, deep learning contributes to the reduction of fuel consumption and overall efficiency improvement. The findings emphasize the potential of deep learning algorithms in fleet management for achieving significant energy savings and enhancing transportation sustainability.Predictive maintenance emerges as a prominent application of deep learning in transportation systems. By analyzing extensive data from sensors and vehicle components, deep learning algorithms can effectively predict potential failures and proactively schedule maintenance activities. This proactive approach reduces downtime, enhances energy efficiency, and minimizes the overall impact on transportation operations.The study concludes that deep learning is a promising technology for improving energy efficiency in transportation systems. The ability of deep learning algorithms to analyze large amounts of data and make accurate predictions provides an ideal foundation for optimizing transportation systems and reducing energy consumption. The findings of this study highlight the significant potential of deep learning in enhancing energy-efficient transportation systems, which can lead to reduced environmental impact and improved sustainability in the transportation sector.