# Intelligent Total Transportation Management System for Future Smart Cities
## 4 Discussion on the Possible Use and Preliminary Evaluation of the Concept
### Action Made in Concept Implementation  
Generally, this paper introduced a new concept related to intelligent total transportation management that needs several studies and developments to improve its effectiveness. The authors of this paper worked in this field and published several scientific reports. Three contributions regarding this study are presented as follows. A total transport management system in smart cities was proposed [18]. * The proposed system uses the vast distributed network of sensors for surveillance and recognition of the non-cooperating, cooperating, contract-based, and priority transport vehicles, including three layers: physical, info-communication, and control generation. * The ICT concept was analyzed and developed based on the wireless network as distributed sensors and actuators and the Internet as IoT, which integrated into vehicles, infrastructure, individual vehicles, and a conventional single control system. * The control layer was a hierarchically organized software set, which was used to recognize and classify vehicles, traffic situation awareness, conflict detection, and resolution, including the sense and avoidance of obstacles, other vehicles, and people. * The proposed system allowed us to optimize the total traffic depending on various objectives ranging from effective energy use to environmental impact minimization. Drone-following models for managing drones in smart city air traffic flow were developed and evaluated [66; 67]. * The drone-following models are based on the principle that they keep a safe distance based on relative velocity. * Another developed model, called the Markov drone-following model, is based on the approximation with the stochastic diffusion process of speed decision. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Areas and Indicators** & **Passive** & **Active** & **Intelligent** \\ \hline vehicle, infrastructure, and transportation performance & & & \\ (compared to the traditional transportation) & 100\% & 110\%–120\% & 115\%–130\% \\ (e.g., vehicle speed, fuel consumption, operational cost) & & & \\ \hline supporting systems (\% of vehicles, drivers supported by service providers) & & & \\ (e.g., mass transport, service provider), energy support & 5\%–10\% & 15\%–30\% & 95\% \\ system, parking availability, accessibility, multimodal & & & \\ transport hubs, emergency, accident handling) & & & \\ \hline legal control (\% of regulation the problems, aspects) & & & \\ (e.g., traffic rules, requirements for infrastructure, legal control of autonomous transport) & 35\%–50\% & 50\%–80\% & 75\%–95\% \\ \hline economy (compared to the intelligent system = 100\%) & 40\%–80\% & 75\%–90\% & 100\% \\ (e.g., supply chain support, just in time arrives, delays) & & & \\ \hline society (\% of society needs being considered/covered) & & & \\ (e.g., demand, affordability, door-to-door speed, acceptance, safety, security) & 40\%–70\% & 60\%–90\% & 85\%–96\% \\ \hline environment (change in the environmental impact using the recently available vehicles and infrastructures, compared to the traditional transportation solution) & 100\% & 80\%–95\% & 70\%–85\% \\ (e.g., emissions, noise, greenhouse impact, energy sources, energy mix in sources) & & & \\ \hline cultural aspects (\% of service provided for non-business travels) & 20\%–40\% & 35\%–65\% & 60\%–80\% \\ (e.g., recreation, health, sport, transport) & & & \\ \hline future generation (ratio of common solution compared to the solution optimized as a sustainable transport) & 50\%–75\% & 70\%–90\% & 85\%–95\% \\ (e.g., used materials, used energy) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simplified comparative analysis of different transportation systems. * The numerical simulation results demonstrated that the safe distance between drones is maintained; there was no accident in the traffic flow. * This approach can be applied to dense traffic flow. Additionally, the first model can be useful for studies of local stability.