## 2 Related Work  
Detecting driver behavior plays a crucial role in enhancing road safety and optimizing transportation systems. Numerous studies have tackled this challenge by leveraging vehicle sensors and smartphone technology. [PERSON] [18] presented a system that utilizes the vehicle's CAN-Bus as a source of sensory data and a smartphone as the processing unit to detect road artifacts and monitor driver behavior using machine learning. [PERSON] et al. [19] developed a window-based SVM model to classify drivers based on OBD data. [PERSON] et al. [20] presented a vehicular data acquisition and analytics system for real-time driver behavior monitoring, anomaly detection, and alerting by utilizing a K-means clustering algorithm. [PERSON] et al. [21] proposed machine learning approaches, including decision trees, random forests, and SVM, to detect road conditions, traffic conditions, and driving behavior usingOBD data and smartphone data. [PERSON] et al. (2020) proposed a system that segments in-vehicle CAN frames and evaluates each segment with a scoring method to detect driving style. [PERSON] et al. (2020) provided a machine learning model based on XGBoost and an ANN model to detect driving behavior using an Android smartphone connected to the OBD bus. [PERSON] and [PERSON] (2020) proposed SVM and feed-forward neural networks to detect driving behavior. [PERSON] and [PERSON] (2020) presented a literature survey of recent research related to driving behavior, including how to obtain data from onboard diagnostics (OBD-II), analyze data, model data, and evaluate models or systems.  
Numerous methods exist for monitoring road traffic conditions using computer vision. These models require vision sensors such as cameras and radar. For instance, [PERSON] et al. (2020) proposed a two-tier edge computing-based model that uses video data to analyze road traffic conditions and speed under different weather conditions. [PERSON] et al. (2020) proposed a video-based approach for road traffic monitoring by utilizing a Haar feature-based Adaboost classifier. [PERSON] et al. (2020) proposed an enhanced YOLOv3 model to detect slow-moving or stopped trains at highway-railroad grade crossings. [PERSON] et al. (2020) proposed a deep learning CNN for predicting traffic status as dense traffic or low traffic. [PERSON] et al. (2020) provided a review of the latest road condition monitoring using computer vision and artificial intelligence.  
Road traffic can also be predicted using smartphone and in-vehicle sensor data. [PERSON] and [PERSON] (2020) employed Mel Frequency Cepstral Coefficients (MFCCs) and Wavelet Packet Transform (WPT) features to monitor road traffic based on smartphone sensor data. Similar approaches were proposed to detect road conditions using smartphone data in ([PERSON] et al., 2020; [PERSON] et al., 2020). [PERSON] et al. (2020) conducted a survey on approaches that use smartphone sensor data to detect road traffic conditions. Utilizing smartphone and in-vehicle sensor data for estimating road traffic conditions requires less computational power compared to computer vision approaches, yet it still offers reasonably accurate estimations of road conditions. This advantage enables the design of cost-effective monitoring systems.  
In this study, we introduce a deep learning model based on neural networks to forecast driving styles and traffic conditions using in-vehicle data sourced from OBD buses and smartphone sensors. The system employs deep learning methodologies to develop a highly accurate detection model capable of handling imbalanced data labeling across various classes, given the uneven distribution of labels. Moreover, the system is designed to minimize computational demands, facilitating implementation in embedded systems and enabling integration within vehicles themselves.