## 1 Introduction
### Related Works
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study [Ref.]** & **Focus Area** & **Key Aspects** \\ \hline
[18; 33] & Unsafe Driving Behavior & SVM, Neural Networks, over 90\% accuracy \\ \hline
[19] & Dangerous Driving Events & Random Forests, RNN, categorization of Driving Behavior \\ \hline
[21] & Driving Style Classification & k-Means Clustering, SVM, Focus on Speed and Throttle \\ \hline
[22] & Near-Crash Prediction & Vehicle Kinematics, Multiple ML Algorithms \\ \hline
[23] & Driver Trip Identification & Historical Data, Decision Trees, 75–100\% Accuracy \\ \hline
[24; 34] & Road Pothole Detection & Vehicle Accelerometer, GPS Data, k-Means, Random Forests \\ \hline
[25] & Pothole Detection (Computer Vision) & Stereo Vision Cameras, Deep Learning \\ \hline
[26] & Review of Pothole Detection Techniques & Summary of Various ML Approaches \\ \hline
[27] & Low-Cost Traffic Detection & In-Vehicle Sensors like Infrared, Accelerometers, Radars \\ \hline
[25; 26] & Vehicle Theft Prevention & - \\ \hline
[31] & Driver Drowsiness Prediction & Cabin Air Quality Analysis \\ \hline
[20] & Traffic Signal Detection & Vehicle Speed Profiles \\ \hline
[35; 36] & comparison purposes & linear regression (LR) \\ \hline
[37] & predict travel time & support vector regression (SVR), linear kernel \\ \hline
[38] & map complex relationships & SVR, artificial neural networks (ANN) \\ \hline
[39] & queuing theory & machine learning, Kalman filter \\ \hline
[40] & driving safety & machine learning, Prediction tool 94.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related works utilizing in-vehicle data with ML applications. As a result of developments in computer vision and artificial intelligence, USRs have evolved from performing straightforward tasks to taking on roles like hotel concierges, museum guides, and autonomous delivery drones. These digital agents utilize SC data to perform tasks effectively while also serving as mobile sensors and actuators for the SC. The authors proposed a conceptual solution to address common urban issues such as traffic congestion and parking shortages, suggesting the use of USRs in combination with blockchain technology for intelligent parking lot management. Prior literature has proposed a variety of machine learning-based prediction methods, such as linear regression (LR), support vector regression (SVR), artificial neural networks (ANN), random forest (RF), and deep learning models. Most studies typically only use LR models for comparison purposes. SVR and ANN have emerged as the most commonly employed models due to their robust nonlinear fitting capabilities and capacity to map complex relationships. For instance, one study successfully applied SVR for the first time to predict travel time, demonstrating its suitability for traffic data analysis [37]. Some other researchers have used support vector machines and SVR with linear kernel functions, suggested travel time prediction models based on SVM regression, and support vector machines [38]. Despite the high accuracy demonstrated by certain models, their long computation time renders them unsuitable for real-time prediction. Researchers have explored hybrid models that combine machine learning methods with other techniques like the Kalman filter and queuing theory to address this issue [39]. However, while these studies aim to enhance prediction accuracy, they fail to provide information regarding confidence levels in travel time predictions. In response to this limitation, the integration of explainable artificial intelligence (AI) has been proposed, with a focus on enhancing transparency and trustworthiness. Tools like the tree interpreter facilitate the breakdown of feature forgetting factors, and different algorithms like random forest and projection pursuit contribute to predictions, reducing the time required for decision-making in connected vehicles. The study [40] focused on enhancing prediction performance by utilizing reduced attributes as input, resulting in a higher accuracy of 94.34%.