# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 6 Validation of the CCAM for Autonomous Driving
### Dataset Cleaning and Visualization  
The considered dataset is a collection of road dynamics measurements, which are recorded on a set of road intersections using three distinct types of sensors deployed at various locations on and around the target areas. The three types of sensors used are Kiwi Traffic Analyzer, Cisco Aironet Wi-Fi & CMX, and Clever Citi Parking Sensor. The Traffic measurement was conducted over a set of road segments leading up to an intersection at the Ernst-Reuter Square (ERS), where the traffic sensors are deployed. These road segments are as follows:  
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throughout the day, where we kept an average sum window of 60 units of time-steps. Using this method, traffic counts were averaged over an hour and significant differences between maximum and minimum values were reduced.  
To visualize the dataset, the traffic flow is visualized for three days, as shown in Figure 12. Therefore, we may observe that the traffic flow increases steadily in the early hours of the day, after which it reduces and builds up again in the late afternoon, remaining consistently high during the evening hours. It reduces once more in the late hours of the night as the day ends. In addition to measuring traffic flow counts for the intersections, we also recorded parking space availability for a parking area next to the intersection. Next, we measured the number of available parking areas in the lot. Thus, it provided an additional layer of mobility monitoring and helped us in visualizing the mobility management requirements in terms of parking spaces. To this end, we used parking sensors to measure the number of parked cars and recorded the values for an interval of every hour during the day and every minute during the hour, as observed in Figure 13. The parking lot measurements indicate that the parking spots are occupied throughout the day but are vacant as the day progresses. The density of WiFi-enabled devices for each hour of a day are shown in Figure 14.  
Figure 11: Scatter plot of five minute average vehicle count of two selected locations highlighting a traffic interruption on 25 January 2020.  
Figure 12: Average traffic volume (number of vehicles per hour) data as provided by the system over a period of three days.  
The total Wi-Fi device counts over a period of a single day is shown in Figure 15. It shows that the number of Wi-Fi devices increase starting at 6 A.M. and continue to be so until 7 P.M. in the evening, where it reaches a peak between 12 P.M. and 5 P.M. This aligns with everyday foot traffic as expected in a campus setting. Thus, it shows that there is much foot traffic during those hours and the need for connectivity is increased during these peak hours. As observed, it is steady in the early hours of the day and steadily declines as the day progresses, indicating that earlier in the day communication requirements tend to be high.  
Figure 14: Heat maps of number of WiFi devices at two selected locations.  
Figure 13: Heat maps of parking spaces for spot #6. For every hour of the 25 days (**Left**). For every minute of the 20 h (**Right**).