# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods
### Data Processing  
The total transportation management uses a special data processing and decision support sub-system. It is special because it synthesizes the latest computing methods (artificial intelligence) with ubiquitous computing (locally distributed partially optimized, embedded sub-systems) to reach high ambition goals: operate an effective, sustainable transportation system that meets the needs of society and the economy, and has a minimum impact on the environment, while being safe and secure. Total transport management uses sensors integrated into the infrastructure, environment, cooperating and contract-based vehicles, drivers, enterprises, institutions, surveillance systems, and technology developments. The information, depending on its value, is used in different management concepts as follows. * passive control--applies data provided by the stakeholders (including historical data and transportation system network capabilities)* active control--uses the additional information obtained from real traffic measurements (namely passive monitoring, primary surveillance, information available from stakeholders/users like a mobile for positioning)
* dynamic management--uses information from the secondary surveillance (provided by the cooperative and contract-based vehicles, drivers, companies) for passive and active monitoring. * proactive management--top management that uses the results of predictive simulations (on the possible occurrence of the traffic jam caused by accident, weather changes, demonstrations) as feedforward, or internal model control, as well as using free routes for prioritized vehicles or simulating the vehicle motion in exceptional environmental cases. Generally, such a computing system must solve the following cascade of computing (Figure 4) independently on the role and size of computing being used locally for sub-systems, elements (even to the individual vehicle or cross-section), or the entire system. * data collection/preliminary data processing:
* data collection and noise filtration--reducing effects of noise on measurements
* primary (preliminary) warning--detecting the crosses of signals in their defined borders on tolerance zones, appearing not prescribed situation (that may result in an error, accident)
* data harmonization--conversion of the measured records to comparable forms of data, selecting windows on data series with the same time frames
* statistical analysis--assessment of the primary statistical values and trends, while saving them to permit further investigations
* Data processing, situation awareness-simulation-evaluation:
* automated situation awareness--the perception of the environment with time and space, predicting the state in the near future
* study of special situations--situations being out of the normal operational circumstances, like accidents
* simulations--using the available simulation software to simulate local event or sub-system operation, or total system
* evaluations--study the simulation results, providing inputs for optimization  
Situation awareness forms the critical inputs to the operator's decision making. If situation awareness is incomplete or inaccurate, the decisions will be wrong. Whereas, a drive may accurately understand what is occurring in the environment. Based on the data collection/preliminary processing, the picture of the environment is assessed, with the comprehensive set of objects and events. Information grouping should tie multiple attributes to each object while minimizing the number of objects presented. For example, the position of the other vehicles and obstacles around the vehicle displayed on the driver's screen. The system trains the special situations, which allows it to predict the future state of the environment to ensure focused attention after pre-attentive processing. The design of the information displays should allow reorientation to the situation in the event of real-world information.