# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 4 Application of Smart Mobility with Big Data
### Connected Vehicles  
Connected vehicles can connect to nearby devices using wireless internet. Connected vehicles are an important factor in the IoT industry.  
As many sensors are attached to connected vehicles, more data are generated, and not only are location and speed shared, but data collection and analyses are also conducted. The collected data are exploited for traffic flow detection, faster path recommendation, and reduced emissions. Figure 2 presents the concept of a connected vehicle. The connected vehicle is associated with many objects, generating considerable data. In this section, we review the applications of data generated by connected vehicles in depth, as well as research conducted to date.  
The authors of [15] introduced the concept of data management for vehicles by proposing an onboard data management layer so that the vehicle can serve as a data platform that can store, process, and share data. Moreover, the authors of [76] proposed ThinGs in a fog (TGIF), a system designed to support interdisciplinary research within a wide range of IoT applications. The authors of [77] reported on the open-source Pikalert(r) system, which combines weather information with real-time data from connected vehicles to provide important information to improve the safety and efficiency of ground transport systems. This framework can be applied to a wide variety of user communities and is designed to quickly collect datasets as they become available. In addition, the authors of [78] proposed an architecture of connected vehicle platforms that can efficiently structure and analyze data by collecting raw data generated by various devices. They also proposed an adaptive memory-based data storage algorithm to be applied to the data reception layer of connected vehicle platforms.  
Figure 2: Connected vehicle concept.  
Furthermore, the authors of [79] proposed a distributed intelligent transportation system that applies intelligence to connected vehicles in dynamic decision making to pass through specific areas such as rotations and intersections. This transportation system assumes that each connected vehicle is an ant and focuses on dynamic decision making, exploiting the ACO (Ant Colony Optimization) algorithm. Moreover, the authors of [80] proposed an adaptive signal control optimization method aimed at minimizing the time to board vehicles by taking advantage of the fact that connected vehicles can easily use each other's vehicle location, speed, and other traffic information.  
The authors of [81] developed a CAN (controller area network) bus and a 4G-LTE-based module that transmits vehicle powertrain information by utilizing the connectivity and network of connected cars. The proposed system can check vehicle failure with a mobile application by exploiting the manufacturer's diagnostic code. A new modular FL (federated learning) method was studied in [82] to enhance road user/object classification based on LiDAR data for the integration of connected car decision making.  
A methodology for quantification of changes in vehicle motion and for the study of instantaneous driving behavior for collision accident types at intersections was presented in [83], using longitudinal and lateral variability. More than 125 million basic safety message data transmitted between more than 2800 connected vehicles were analyzed and integrated with historical crash and road inventory data at 167 intersections. Furthermore, the authors of [84] proposed an easy-to-implement origin and destination (OD)-based segmentation technique to improve trunk signal adjustment by utilizing automatically collected vehicle trajectory data from connected vehicles.