# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 1 Introduction  
Road traffic accidents remain a global concern, resulting in significant human and economic impacts. Annually, over 1.19 million lives are lost, and millions more sustain long-term injuries due to road crashes [1]. These incidents impose substantial financial burdens, often exceeding 3% of a country's gross domestic product, particularly in developing economies with limited resources to address these challenges [2]. Among the most vulnerable are professional drivers, particularly truck drivers, who face heightened risks due to the demanding nature of their occupation, behavioral factors, and challenging environmental conditions. These unique vulnerabilities underscore the need for tailored safety interventions to address this issue. Truck drivers are integral to global logistics and freight systems, yet their work environment exposes them to significant safety risks. Prolonged working hours, irregular sleep schedules, and extensive highway driving contribute to fatigue, cognitive impairments, and slower reaction times [3, 4]. Delivery pressures and economic incentives further increase theprevalence of risky behaviors such as speeding, distracted driving, and stimulant use [5,6]. Research indicates that human behavior contributes to up to 90% of road accidents, underscoring the importance of addressing these behavioral factors through effective safety measures [7,8]. Traditional safety interventions, such as driver screening, training programs, and vehicle maintenance, have had limited success in addressing the multifaceted risks associated with truck driving. Safety monitoring systems offer a technology-driven alternative by enabling continuous or event-triggered data collection of critical metrics, including speed, acceleration, braking patterns, and contextual factors surrounding unsafe events [9,10]. These systems deliver real-time feedback to drivers and support post-event analysis, facilitating risk mitigation strategies and the development of evidence-based safety protocols. In addition to their safety benefits, safety monitoring systems provide significant operational advantages for commercial fleets. These systems document unsafe driving behaviors, enable prompt corrective actions, and deliver frequent, objective feedback to drivers. They establish benchmarks for fleet-wide safety norms, incentivize adherence to safe driving practices, and reduce the need for in-person evaluations. Moreover, safety monitoring systems enhance regulatory compliance, improve productivity, and support liability management, positioning them as essential tools for modern fleet operations [11]. Despite their potential, safety monitoring systems face barriers to widespread adoption in commercial trucking. High implementation costs, technical complexities, and concerns regarding data privacy and security remain significant challenges [12,13]. Addressing these obstacles is critical to achieving the full potential of safety monitoring systems in enhancing road safety and operational efficiency. This systematic review examines the effectiveness, benefits, and limitations of safety monitoring systems for truck drivers. It consolidates existing evidence to evaluate their role in addressing safety challenges, encouraging safer driving behaviors, and identifying barriers to their widespread adoption. The review is structured around the following research questions (RQ):  
RQ1: Which monitoring technologies are currently employed to evaluate and improve the driving behavior and safety performance of professional truck drivers? RQ2: Which physiological, behavioral, and environmental variables are tracked by these technologies in the context of professional truck driving? RQ3: How do monitoring technologies affect safety outcomes among professional truck drivers, including accident mitigation and behavior modification? RQ4: What are the primary strengths and limitations of current monitoring technologies for professional truck drivers?