# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods
### General Optimization Method
Of course, the minimum energy used matrices must be determined for the grid representation. The number of vehicles might be reduced by the definition of a series of standard vehicles. For example, depending on the required accuracy, 10-25 personal cars can be defined with different sizes, engine, and drivers. The minimum-energy-used matrices must be predetermined for any changes in the environmental (weather) characteristics, \(\mathbf{z}\), and the noise vector, \(\mathbf{\xi}\) (defining the road construction, accident, or traffic jump occurrences). Traffic intensity and situations change very quickly. Therefore, the minimum energy used matrix must be recalculated at least every minute, depending on the real traffic situation. Most developed traffic management systems may use simulation and forecast of future traffic. It is a short-term forecast that may use the statistical data measured. However, the data provided by the cooperative  
Figure 3: Graph model of a road system fragment. vehicles might be applied to make a better, more accurate forecast--this approach is required to use the hierarchical approach to monitor and manage the traffic. Traffic management may use partial optimizations of special parts or sub-system elements like traffic lights or conjunction optimization of the supply chains, optimization of deliveries of goods, and optimization for priority vehicles. By taking into account all these discussed aspects, the total energy consumption is  
\[E_{t}=f(x)=\frac{1}{N_{T}}\sum_{i=1}^{N_{\text{ev}}}w_{s_{\text{ev}_{i}}}\sum_{ k=\tau_{s_{\text{ev}_{i}}}/T}e_{v_{i_{f}}}\Big{(}E_{min,sv_{i_{f}}}[k]\Big{)}+ \Delta e[k] \tag{4}\]  
where \(T\) is a time step, \(N_{T}=\tau/T\) number of steps, index \(sv\) depicts the standard vehicles, \(N_{sv}\) is the number of standard vehicles, \(\tau_{s_{\text{ev}_{i}}}\), \(\tau_{s_{\text{ev}_{i_{f}}}}\) time of starting and finishing the motion of the \(i\)-th standard vehicle, \(k\) is the number of time step (\(k=1,\ 2,\ \ldots,\ N_{T}\)), and \(\Delta e\) extra energy consumption caused by managing with priority vehicles. Nevertheless, the optimization problem is reduced in size, and depending on partial objectives, the active and dynamic optimization of the traffic needs real information on the traffic condition. Such information might be measured and collected by widely distributed sensors and integrated into one system as the IoT. The recommended concept is a total transport and total management system. This means that all motions of people and goods (including walking, sport, travels, freight transport) realized by any type of vehicles (from electric scooters, up to supersonic business jets) as elements of the single transportation system are monitored and controlled by a special hierarchical concept. Such a system (Figure 4) has three levels:  
* physical levels including all the objects, vehicles, and even stakeholders;
* digital level that transmits the data/information and uses it for multi-directional infocommunication; and
* virtual or computational center, which supports the operation center and situation awareness, evaluation, decision making, and realized active dynamic control. Figure 4: Representation of management with smart city total transportation system. In such a system, especially nowadays, the sensors and their integration in the transportation system represent a central problem. The digital level realizes the IoT concept, while the third level is fully integrated into the total transportation system, namely, it uses computing and ubiquitous computing (when computing may appear locally anywhere, anytime).