Revolutionizing Road Safety and Optimization with AI: Insights from Enterprise Implementation

• 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: Accident prevention; Algorithms; Artificial intelligence; Carbon dioxide emissions; Complexity; Computer science; Cost control; Digital transformation; Efficiency; Emission standards; Emissions; Emissions control; Energy consumption; Environmental impact; Fatalities; Greenhouse gases; Logistics; Logistics management; Optimization; Quantitative research; Roads & highways; Route optimization; Small business; Statistical analysis; Statistical methods; Technology adoption; Tracking systems; Traffic accidents & safety; Traffic safety; User training; Variance analysis; Vehicles; Morocco
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2025.0160498

This study explores the key factors influencing the adoption of artificial intelligence (AI) in the logistics sector, with a particular emphasis on road logistics management. It examines the technological, organizational, and environmental contexts that shape AI integration, as well as the challenges faced by logistics managers, including the need for digital transformation, carbon emissions reduction, and advanced parcel tracking management. The objective is to identify technological and human-related barriers to AI adoption and to assess the level of interest and readiness among logistics companies, especially in the Moroccan context. A quantitative research approach was adopted, based on an online survey targeting logistics professionals and decision-makers, mainly from European and Moroccan small and medium-sized enterprises (SMEs). The collected data were analyzed using statistical methods, including linear regression and ANOVA, to evaluate the relationships between company characteristics, perceived complexity of AI tools, and the avail-ability of qualified human resources. The findings indicate that perceived complexity and limited access to specialized skills significantly hinder AI adoption. Moreover, the perception of tangible performance benefits—such as increased operational efficiency and reduced CO2 emissions—emerges as a major driver for acceptance. These insights offer practical implications for logistics companies seeking to leverage AI technologies to optimize operations, reduce environmental impact, and enhance parcel tracking systems. A strategic roadmap is proposed to overcome the identified barriers and promote effective AI integration.