# Semantic Traffic Sensor Data:
## 6 Conclusions and Future Work  
Open data have the capacity to let citizens have a better understanding of what politicians are doing; on the other hand, they also stimulate the economy by encouraging companies that use open data in their business activities. This transparency can improve public services and spur inclusive \begin{table}
\begin{tabular}{l l l} \hline
**Query** & **Short Description** & **Response Time** & **Notes** \\ \hline Query Figure 11 & Data of the sensor “R001\_SM3” & 300 ms & \\ Query Figure 12 & Number of vehicles counted by sensors in Modena’s square & 2.6 s & GeoSpatial \\ \hline Query Figure A2 & Number of sensors in each city & 750 ms & \\ Query Figure A3 & Number of vehicles counted by each sensor in the datastore & 26.4 s & \\ Query Figure A4 & Number of vehicles counted by sensors on a street & 1.86 s & \\ Query Figure A5 & Number of sensors within the ring road in Modena & 650 ms & GeoSpatial \\ \hline \end{tabular}
\end{table} 
Table 4: SPARQL queries response time. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Granularity** & **Window** & **\#Iterations** & **\#Observations for** & **\#Generated** & **Loading Time of a** & **Total Time** & **Result** \\
**of Data** & **Lenght** & **Required** & **Each Iteration** & **Triples** & **Single Iteration** & **Single Iteration** & **Total Time** & **Result** \\ \hline
1-h data & 1 day & 365 & 17,500 & 122.5K & 14 s (avg) & 1.25 h & success \\
15-min data & 1 day & 365 & 70,000 & 490K & - & - & failure \\
15-min data & 12 h & 730 & 35,000 & 245K & 30 s (avg) & 6 h & success \\
1-min data & 1 day & 365 & 430,000 & 3M & - & - & failure \\
1-min data & 12 h & 730 & 215,000 & 1.5M & - & - & failure \\
1-min data & 3 h & 2920 & 54,000 & 378K & 45 s (avg) & 36 h & success \\
1-min data & 1 h & 8760 & 18,000 & 126K & 14 s (avg) & 34 h & success \\
1-min data & 1 min & 525,600 & 200 & 1400 & 0.375 s (avg) & 55 h & success \\ \hline \end{tabular}
\end{table} 
Table 3: Loading process performance for 1-year data under different granularity and window length conditions. economic development. For example, greater access to traffic data can be used to tackle sustainable mobility needs. In this paper, we have presented the mappings and tools that we have used within the context of the TRAFAIR project to model, integrate, enhance semantically, and exploit traffic data. Besides, we have evaluated the feasibility and benefits of our approach. We believe that this work represents a relevant and compelling use case concerning the collection and exploitation of semantic sensor data in real-world scenarios. Moreover, the validity of the proposed approach is not limited to the traffic sphere, as it belongs to a more generic one such as the publication of any kind of sensor data. The approach can be easily adapted and applied in different fields, especially in a smart city context. Within the TRAFAIR project, this approach will be applied also for the publication of air quality data. Regarding the exploitation of traffic data, in the TRAFAIR project, the collected traffic is used as part of a more complex process defined to estimate and predict pollutants along different areas and road segments of a city. For this purpose, additional tools such as the Graz Lagrangian Model (GRAL) [103] and other data sources (meteorological data, data about the presence of buildings in a city, air quality data provided by official monitoring stations, etc.) are used. This represents a more advanced and complex use of traffic data, and it is out of the scope of this paper. Nevertheless, the more direct exploitation techniques described in this paper are representative of the usefulness of the availability of traffic data and its semantic annotation. Several related future lines of research could be pursued. Specifically, we are currently tackling some challenges related to other types of sensor data relevant to TRAFAIR.