# Semantic Traffic Sensor Data:
## 5 Experimental Evaluation  
The sensors and observations R2 RML models, created with Karma, have been successfully applied over traffic data of Modena and Zaragoza. The following statistics refer to a Debian 9 machine with 32 Intel(R) Xeon(R) Silver 4108 CPU at 1.80 GHz and 64 GB of RAM. For the city of Zaragoza, in these experiments, the data of the 46 static devices described in Section 3.2.1 have been considered: the information relating to those devices resulted in 506 triples. The process for generating and loading the triples took less than 1 s. Sensors' data for the city of Modena corresponded to data about 400 traffic sensors, which resulted in 4400 triples. The process for generating and loading the triples took about 5 s. A summary of the performance of the loading process of the information about sensors is provided in Table 1.  
Statistics related to the publication of traffic observations in the two cities are displayed in Table 2. Each traffic observation is transformed into a set of seven RDF triples following the approach described \begin{table}
\begin{tabular}{l c c c} \hline \hline
**City** & **\#Sensors** & **\#Triples** & **Loading Time** \\ \hline Zaragoza & 46 & 506 & \(\sim\)0.75 s \\ Modena & 400 & 4400 & \(\sim\)5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensor data performance evaluation: loading time. in Section 4.1. The loading process is the entire process that loads the data into Virtuoso through Karma: a query is executed on the database, the extracted data is stored in a CSV file, and the file is processed by Karma, that loads the data in Virtuoso. Each Zaragoza's traffic sensor generates one observation every hour, and therefore a total of 24 observations in a day. Considering that all the sensors are working correctly, more than 1000 observations are gathered daily. From January 2019 to December 2019, the number of observations arises to 383 thousands for a total of 2.5 million triples. The triple generation and loading process took about 1.5 min. This information is reported in the first row of Table 2.  
The situation in Modena is quite different due to a higher number of sensors and observations' rate. Indeed, in Modena, 1-min observations, which means about 1440 measurements for each sensor in a day, are gathered. In order to compare the statistics of Modena and Zaragoza, the data have been aggregated hourly. Some of the sensors in Modena collect more fine-grained data, since they can recognize up to 10 types of passing vehicles; on the other hand, not all of the sensors provide one measure per min. Therefore, about \(17,500\) hourly observations are gathered daily. From January to December 2019, the total number of observations arises to 6.5 million observations for a total of over 46 million of triples. This information is reported in the second row of Table 2.  
Some tests on the loading process, to understand the capabilities of Virtuoso and Karma, the scalability of the loading process, and the variation of loading time in diverse configurations, have been performed. Traffic data generated in Modena, that are fine-grained and can be further aggregated, have been exploited. The loading process has been tested using different granularities (1-h aggregated observations, 15-min aggregated observations, and 1-min observations) and different window lengths, i.e., the time period of data to load in one iteration (1 day, 12 h and 3 h). 1-h granularity data is usual in some open data initiatives (e.g., the City Council of Zaragoza currently publishes pollution data with 1-h granularity and traffic and mobility indicators with granularity not smaller than 1 h); however, tests with lower granularity data have been performed in order to stress the system. The aim of these tests is to compare the loading time and performance. Selecting suitable granularities and window lengths for the traffic observations that have to be shared as Linked Open Data belongs to the public administration which owns the data.