Projects

Today’s urban environments are prone to daily congestions due to dense traffic. The development of Machine Learning (ML) based traffic control systems for such environments gathered interest to create intelligent systems with the potential to improve the existing transport network efficiency. Applying ML in control of complex urban environments is prone to the curse of dimensionality. The controller behavior is influenced by the number of observed traffic parameters describing the environment in which it acts. Rising the number of parameters causes an exponential increase of possible state-action space, making it nearly impossible to find an optimal control policy in a reasonable time. The scalability of the same space becomes very important. It is also necessary to gain trust or confidence in the ML control system’s performance in unforeseen situations. Having a control policy that performs well in all relevant traffic states is more important than superior performance in some states. Thus, tuning of such systems for significant transport demand changes is very problematic or even infeasible for operators without computer support. The main benefit of the project DLASIUT is the proposed learning framework and structure of an agent-based traffic controller capable of learning the optimal control policy from microscopic simulations containing realistic models of a real-world urban road network. Additionally, support to Connected and Autonomous Vehicles (CAVs) will be added using them as an extra control output ensuring the applicability in future mixed traffic flows containing classic vehicles and CAVs. In-depth testing using realistic simulation models and Structured Simulation Framework from transport technology point of view to identify possible poor controller behavior will improve the state of the art of ML-based traffic controllers. The benefit for the citizens of urban environments is better traffic management and reduction of congestions and vehicle emissions.

Creating a traffic model of driver information systems to facilitate more efficient, safer, sustainable, and environmentally friendly traffic flow.

Development of a theoretical model for multi-criteria optimization of highway traffic management and exploration of the possibilities and concept of applying spatiotemporal databases for travel time prediction based on a large set of historical data.

Development and testing of a system for cooperative adaptive management of interconnected intersections based on learning.

Development of a method for congestion detection aiming to assess the level of traffic flow anomaly and development of a method to address routing problems for electric vehicles with full or partial charging.

Development and testing of a concept for a system managing spatiotemporally variable speed limits in the environment of networked vehicles on highways.

Development of models and strategies for the implementation of intelligent mobility in cities.