Abstract Intelligent Transportation Systems (ITS) are designed to take advantage of recent
advances in communications, electronics, and Information Technology in improving the …

Traffic flow optimisation is a distributed complex problem. Traditional traffic and logistics flow
control algorithms operate on a system level and address mostly switching cycle adaptation …

Automatic learning techniques stand as promising tools to respond to the need of higher
efficiency of traffic network, even more so at times of mounting pressure from economic and …

Urban traffic congestion has become a serious issue, and improving the flow of traffic
through cities is critical for environmental, social and economic reasons. Improvements in …

This dissertation develops and evaluates a new adaptive traffic signal control system for
arterials. This control system is based on reinforcement learning, which is an important …

There is strong commercial interest in the use of large scale automated transport robots in
industrial settings (eg warehouse robots) and we are beginning to see new applications …

Traditional methods for traffic signal control at an urban intersection are not effective in
controlling traffic flow for dynamic traffic demand which leads to negative environmental …

Urban mobility is a major challenge in modern societies. Increasing the infrastructure's
physical capacity has proven to be unsustainable from a socio-economical perspective …

Intelligent traffic control is a key tool to achieve and to realize resource-efficient and
sustainable mobility solutions. In this contribution, we study a promising data-based control …

We study approaches to use (real-time) data, communicated between cars and
infrastructure, to improve and to optimize traffic flow in the future and, thereby, to support …