NW Logistics: System Architecture and Design for Sustainable Road Logistics

• Published in: International journal of advanced computer science & applications Vol. 16; no. 4
• Main Authors: Younesse, OUAHBI, Soumia, ZITI
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2025
• Subjects: Algorithms; Architecture; Artificial intelligence; Carbon; Carbon dioxide; Computer science; Data analysis; Decision making; Design; Driver behavior; Efficiency; Emissions; Internet of Things; Logistics; Logistics management; Optimization; Real time; Roads & highways; Route optimization; Supply chains; Sustainability; Tracking; Vehicle safety
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2025.01604105

The logistics industry is under increasing pressure to reduce carbon emissions and enhance efficiency in response to environmental and regulatory demands. However, optimizing road logistics to achieve these goals requires innovative solutions that balance operational efficiency with sustain-ability. This study addresses this need by introducing NW Logistics, an AI-powered platform that optimizes road logistics to lower CO2 emissions and improve fleet performance. In order to achieve these objectives, real-time CO2 tracking, route optimization, and driver behavior monitoring were integrated into NW Logistics. The system enables precise, real-time tracking of deliveries and vehicle locations, allowing logistics managers to monitor fleet performance with enhanced accuracy. Additionally, onboard cameras and sensors generate individualized driver reports, tracking infractions and fostering safer driving behaviors. Initial simulations of NW Logistics indicate a significant reduction in carbon emissions, along with improvements in route efficiency, delivery tracking accuracy, and driver safety. These results demonstrate the transformative potential of AI to advance sustainable and efficient logistics management.