## 3 The Proposed Vehicle Dataset
### The Uses of the VehDS-LA Dataset  
The proposed VehDS-LA is appropriate for use with applications related to 5G technology, machine learning techniques and transportation systems. The main uses of the VehDS-LA are:  
* **5G network studies:** A heterogeneous ultra-dense network is a 5G-enabling technology that consists of a high density of small cells in addition to the legacy Long-Term Evolution (LTE) macro cells. HUDN aims to meet the requirements of increased capacity, low latency, and distributed traffic load with low installation cost [23, 29]. The major issues associated with 5G HUDNs are cell selection, interference mitigation, and resource allocation [30]. Cell selection refers to the process of choosing the serving base station to which a vehicle will connect. The conventional approach of selecting cells is  
Figure 12: The proposed LA vehicle dataset. based on the received signal strength indicator (RSSI) value. In fact, this approach is inefficient in 5G HUDNs due to the existence of a large number of cells with different distribution and sizes [31]. Figure 13 shows the cell selection issue in an HUDN, where a red vehicle should select a serving cell, and RSSI values are not enough. HUDNs suffer from two types of interference: co-tier and cross-tier interference. Co-tier interference occurs between homogeneous cells, while cross-tier interference happens between heterogeneous cells [32], as shown in Figure 14. The proposed VehDS-LA dataset can be used in studies related to 5G HUDNs. Figure 14: Interference issue in HUDNs. Figure 13: Cell selection issue in HUDNs. * **Automation and driverless vehicles studies:** Nowadays, vehicle automation is becoming a solution that is used to provide road safety and to prevent accidents [33]. The Society of Automotive Engineers (SAE) defines six levels of vehicle automation, as illustrated in Figure 15. The first three levels, i.e., levels 0 to 2, require driver attention. On the other hand, levels 3 to 4 give part of the responsibility for driving and monitoring roads to the vehicle itself, while level 5 provides full automation of vehicles [34]. Thus, the proposed dataset includes the essential vehicle features, i.e., geographical latitude and longitude coordinates, azimuths, and speeds of vehicle samples, which can be used in research related to vehicle automation. * **ML-based vehicle mobility studies:*
* ML techniques provide remarkable opportunities in several fields, including transportation [35]. A good machine learning model needs a large number of samples to train the ML model [36]. Recent works that focus on vehicle movement issues, including [2; 37], relied on solving research problems using machine learning algorithms, such as artificial neural networks (ANN) and support vector machine (SVM), Naive Bayes (NB), and Tree-based techniques. Figure 16 represents the process of building a machine learning model that is based on supervised learning to solve a vehicle mobility issue. The building process passes through many phases: data cleaning, data labeling, data dividing, ML model training, and ML model testing [2]. * Data cleaning: In this phase, data that will not be used to solve the research problem are removed [38]. * Data Labeling: This refers to the process of tagging vehicle samples so that the ML model can learn from it [39]. * Data Dividing: This refers to splitting  
* **Intelligent transportation system studies in the LA smart city:*
* Smart city and intelligent transportation system are recently developed concepts [41]. The term ITS is defined as a comprehensive system that consists of vehicles and transportation infrastructure and it performs communication, controlling, and information processing in smart cities to facilitate their environmental sustainability [42; 43].