# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## Appendix A  
The Appendix includes two tables that provide additional detail on the reviewed studies. Table A1 summarizes each study's authorship, location, goals, methods, and main findings. Table A2 lists the monitoring technologies used, measured variables, monitoring frequency, and time frame of data collection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{ \begin{tabular}{c} **Institution,** \\ **Country** \\ \end{tabular}  } & \multirow{2}{*}{**Objective**} & \multicolumn{1}{c}{**Data**} & \multirow{2}{*}{**Data Analysis**} & \multicolumn{1}{c}{**Main Findings**} \\ \hline \multirow{5}{*}{[PERSON] et al. (2024)[35]} & \multirow{5}{*}{ \begin{tabular}{c} **[PERSON]** \\ **University,** \\ **Israel** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **To assess whether** \\ **ADHD-related risky** \\ **driving behaviors** \\ **generalize to** \\ professional drivers** \\ **monitored** \\ **by supervisors** \\ \end{tabular}  } & \multirow{5}{*}{ \begin{tabular}{c} **Generalized Linear** \\ **Model, Poisson** \\ **regression, and** \\ **Relative** \\ **Risk analysis** \\ \end{tabular}  } & \multirow{5}{*}{ \begin{tabular}{c} **Professional drivers with ADHD** \\ **exhibited a 113\% higher rate of \\ **speeding violations and significantly** \\ more safety events (e.g., hard braking, \\ **swering) compared to non-ADHD \\ **peers, despite supervisory monitoring.** \\ \end{tabular}  } \\ \hline \hline \multirow{5}{*}{\ \begin{tabular}{c} **[PERSON] et al. (2021)[44]** \\ **Germany** \\ \end{tabular}  } & \multirow{5}{*}{
\begin{tabular}{c} **To analyze the usability** \\ **and effectiveness of an** \\ **HMI in facilitating** \\ **cooperative truck** \\ **Munich, Germany** \\ **the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Institution, Country** & **Objective** & **Data Collection** & **Data Analysis** & **Main Findings** \\ \hline [PERSON] & Virginia Tech & To assess the safety & & & & Electronic logging devices (ELDs) \\ Hickman & Transportation Institute, & To assess the safety & & & reduced total crash rates by 11.7\% and \\ Hickman & Transportation Institute, & benefits of electronic & & & preventable crashes by 5.1\%, while also \\ [PERSON] et al. (2017) [40] & Virginia Tech & To assess the safety & & & lowering driving-related and \\  & Transportation Institute, & logging devices (ELDs) & & & non-driving-related HOS violations by 53\% and 49\%, respectively. \\ [PERSON] et al. (2018) [50] & United States & in reducing crashes and & & & 53\% and 49\%, respectively. \\  & United States & in reducing crashes and & & & 52\% and 49\%, respectively. \\  & & & & & EDL-equipped trucks demonstrated \\  & United States & in reducing crashes and & & & clear safety benefits, enhancing \\  & & & & compliance with hours-of-service \\  & & & & regulations and reducing \\  & & & & fatigue-related risks. \\ \[PERSON] & Virginia Tech & To compare the Large & & & \\ [PERSON] et al. & TrustCash Causation & & & & \\  & & Study (LTCCS) and & & & Truck drivers following too closely \\ [PERSON] et al. (2018) [50] & Virginia Tech & Naturalistic Driving & & were 1.34 times more likely to crash, & \\  & Transportation Institute, & (ND) data for evaluating crash causation and & & & based on combined LTCCS and \\  & United States & exposure to risky & & & naturalistic driving data. FCW systems \\  & & behaviors, focusing on the associated factor & & & are recommended to mitigate this risk. \\  & & & & & \\  & & \"Following Too Closely.\" & & & \\ \[PERSON] & Monash & To design and evaluate a & & & A human-centered design process \\  & University & document-centered HMI to & & & created a two-level fatigue and \\  & Accident & address fatigue and & Interview & & escalating distraction warning system \\  & Centre, & distraction in & & and human-centered & with multimodal alerts.