Traffic & Pollution Prediction
Amongst other things intelligent traffic control needs the proper identification and prediction of the traffic and environmental situation. In this way the right measures can be taken early enough to tackle and control the relevant traffic and environmental situations, and ideally to prevent the development of congestion right from the beginning.
For this ANDATA supplies software modules and prediction models to forecast the expected traffic load and environmental burden. The application of artificial intelligence and machine learning procedures allows local conditions to be grasped simply and quickly, as well as the ability to adapt to new and changing situations. Thus predictive control strategies can be realized easily and efficiently.
The given solutions are available in the form of general software modules, which can be integrated into traffic management and traffic control systems or operated as standalone applications.
A very useful side effect in creating prediction models with ANDATA’s development tools is the knowledge gained about the principle dependencies of all the given data and readings. Thus all the relevant correlations in the traffic flows can be identified and quantified. The same is valid for the prediction of air pollution dependent on the traffic load.
Features and Advantages
- Precise prediction and forecasting of traffic loads and pollution based on the given empirical data.
- Simple consideration of categorical, fuzzy and uncertain data and information (like weather, special events, etc.).
- Possible combination with virtual sensors.
- Self-adaptive, semi- or fully autonomous applications possible.
- Automatic identification of the relevant input parameters.
- Easy adaptation and consideration of local circumstances, even of very special issues.
- Generic implementation allows efficient integration into arbitrary systems for traffic control and management.
- Also available as a standalone solution.
- Prediction models also provide relevant answers for traffic and city planning, e.g. about dependencies and correlation in different traffic flows.