# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 4 Traffic Measurement with SDN
### Traffic Prediction  
Although measuring traffic prediction is responsible for predicting the future situation of the traffic from the previous characteristics of traffic flow, measuring real-time traffic is better than traffic prediction. Previous traffic characteristics of specific applications or services allow us to organize future traffic plans such as contingencies and scheduling. Using SDN technology, many researchers have investigated the influences of prediction and optimization. In this research, SDN-based MA supports improving traffic prediction through the appropriate protocols, which enhance the MA capacities. Anteater provides the traffic measurement, which depends on the traffic prediction measured through the SDN static analysis. The network validation tool was also considered for predicting traffic depending on monitoring the errors in the OpenFlow applications. However, the authors of [22] studied traffic using another model. Therefore, they considered traffic prediction in resource allocation. Additionally, they studied end-to-end delay and bandwidth with decreasing packet loss. Although many valuable comments are related to traffic, digital techniques that monitor the priority of the transportation services and vehicular traffic management within smart cities enhance the intelligence and QoS of the ITS. Techniques based on ITS minimize the overall cost through energy-efficient architecture emerging with 5G technology, prioritized security management of transportation, and vehicular communication services.  
Automation facilities are continuous services in plans such as the timing of transportation services and the priorities of electric vehicles. To minimize the overall cost and energy consumption, the timing of each transportation service should be automated with programming, which provides the automation time intelligently. The priorities of electric vehicles depend on the user's program, such as emergency cases and long-distance traveling. For these priorities, electric vehicles need continuous charging facilities. Here, the legacy system allows us to improve the possible modifications and facilities. Some electric vehicles already have systems for charging facilities. In this case, introducing new or upgrading the software design will provide automation facilities. In some cases, auto manufacturers may have to extend the hardware from an existing system.  
Risk predictions depend on the reliability of vehicular communication and transportation services. Reliability shows the efficiency of the services and proves that the lifetime of the transportation system maintains a guarantee period without any system faults. Regarding ADVs, the following risk predictions can be considered:  
* Driverless vehicles face many different objects, which are not in the programmed list.
* With natural weather conditions, smart environments should be secured for 24 h and seven days. These environments will be the best when service providers maintain risk prediction systems.
* Emergencies depend on many unexpected problems created by many risks, such as human errors.
* Energy management provides energy according to the needs of the electronic devices used in driverless vehicles. Here, the risks are the reliability of the devices because the failure of devices causes many problems such as accidents.
* Cybersecurity is one of the potential issues created by risks, such as the failure of electronic components used in driverless vehicles.  
These risks need to be predicted for minimizing human errors and mistakes, which form security problems such as threats. Further, combinations of all these risks allow cyberattacks indirectly within the transportation system.