## 6 Conclusions and Lessons Learned  
Improving the efficiency of the TMSs (traffic management systems) becomes a hot topic and constitutes a challenging research direction due to a crucial need to monitor the transportation infrastructure in smart cities. This paper first presented various existing technologies usable for traffic data gathering as well as emphasized the emerging communications technologies that are able to significantly improve the accuracy of the collected information. We especially focused on the long-range technologies of today i.e., using the 4G (LTE) cellular networks and Low Power Wide Area (LPWA) solutions, such as SIGFOX and LoRa. We also reviewed some of the many routing protocols in VANET systems ready to disseminate the collected (received) data among vehicles. Based on the findings from the studied literature, today's TMS options are still insufficient to construct a reliable and secure TMS capable of meeting the anticipated increase of population as well as numbers of vehicles in smart cities. Therefore, we presented our proposed modular environment for road traffic modeling that allows to easily recreate the examined road infrastructure in its full resemblance. Fueled by the ongoing proliferation of the M2M devices in the IoT domain, the developed environment aims at engaging the embedded (low-power) devices upfront, which plays an important role in our research. To be as close as possible to the real road traffic conditions, our created scenarios follow the structure of the practical urban intersections in Brno (the second largest city in Czech Republic). For the purposes of our performance evaluation, we developed a unified test environment in Java programing language, where the considered scenarios were implemented. Further, we selected the well-known embedded devices, such as Raspberry Pi 1 (Model B+), Raspberry Pi 2 (Model B), Intel(r) Edison, and Intel(r) Galileo Gen 2 for our performance evaluation campaign. Further, we concluded that the preferred embedded device that is comparable (in terms of its performance) with today's computers is the Raspberry Pi 2 Model B (see Table 4 for the corresponding results). This device therefore becomes an attractive candidate to serve as a computation unit in the realistic TMS deployments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Time [s]** & **Difference [s]** & **Ratio [-]** \\ \hline \multirow{4}{*}{**T-shaped**} & Raspberry Pi 1 Model B+ & 186 & 156 & 5.16 \\ \cline{2-5}  & **Raspberry Pi 2 Model B** & **30** & **0** & **1** \\ \cline{2-5}  & Intel Galileo Gen 2 & 138 & 108 & 4.6 \\ \cline{2-5}  & Intel Edison & 188 & 158 & 6.26 \\ \hline \multirow{4}{*}{**Real-life**} & Raspberry Pi 1 Model B+ & 2242 & 1940 & 7.42 \\ \cline{2-5}  & **Raspberry Pi 2 Model B** & **302** & **0** & **1** \\ \cline{2-5}  & Intel Galileo Gen 2 & 1793 & 1491 & 5.93 \\ \cline{2-5}  & Intel Edison & 2105 & 1803 & 6.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing results for both scenarios. The main challenge in the modeling and implementation part was to capture the necessary properties of the actual traffic behavior, while having the limited resources of the embedded devices in mind. An event-based discrete dynamic modeling approach was followed, since it constitutes an elegant solution method enjoying the adaptive time step discretization. It is known that the computation time requirements of real-world traffic problems grow rapidly with the increasing complexity of the scenario at hand. However, with the developed solution method, the experienced time-related parameters can be made acceptable. Together with the solution performance, the behavior of the conducted GA (genetic algorithm) based optimization was studied. It was shown (see Section 5.3) that the GA optimization is convergent and--depending on its parameters--after several generations leads to the desired optimized setup of traffic signals.