Autonomously adapting signalling strategies to changing traffic demand in urban areas have been frequently used as application scenario for self-adapting systems. Striving for the ability to cope with the dynamic behaviour of traffic and to react appropriately to unforeseen conditions, such solutions dynamically adapt the signalisation to the monitored traffic demands. The Organic Traffic Control (OTC) system is one of the most prominent representatives in this domain. OTC implements a multi-layered observer-/controller architecture. In this paper, we extend OTC’s observer with a time series forecast component to create forecasts of future traffic developments for turning movements. These forecasts are then used to proactively adapt signali...
This study explores the possibility of developing a short-term traffic flow prediction model that ca...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Autonomously adapting signalling strategies to changing traffic demand in urban areas have been freq...
This article presents novel approaches to automatically learn the best combination of forecasts comp...
This article presents novel approaches to automatically learn the best combination of forecasts comp...
This article presents novel approaches to automatically learn the best combination of forecasts comp...
This thesis explores ways to improve the self-organised urban traffic management system Organic Traf...
This thesis explores ways to improve the self-organised urban traffic management system Organic Traf...
This report aims at investigating forecast-based control of Organic Computing (OC) systems, especial...
This report aims at investigating forecast-based control of Organic Computing (OC) systems, especial...
The target in the PRYSTINE project is to realize Fail-operational Urban Surround perceptION (FUSION)...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Floating car data present a cost-effective approach to observing the traffic state. This paper explo...
This thesis explores ways to improve the self-organised urban traffic management system Organic Traf...
This study explores the possibility of developing a short-term traffic flow prediction model that ca...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Autonomously adapting signalling strategies to changing traffic demand in urban areas have been freq...
This article presents novel approaches to automatically learn the best combination of forecasts comp...
This article presents novel approaches to automatically learn the best combination of forecasts comp...
This article presents novel approaches to automatically learn the best combination of forecasts comp...
This thesis explores ways to improve the self-organised urban traffic management system Organic Traf...
This thesis explores ways to improve the self-organised urban traffic management system Organic Traf...
This report aims at investigating forecast-based control of Organic Computing (OC) systems, especial...
This report aims at investigating forecast-based control of Organic Computing (OC) systems, especial...
The target in the PRYSTINE project is to realize Fail-operational Urban Surround perceptION (FUSION)...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Floating car data present a cost-effective approach to observing the traffic state. This paper explo...
This thesis explores ways to improve the self-organised urban traffic management system Organic Traf...
This study explores the possibility of developing a short-term traffic flow prediction model that ca...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...
Reliable and accurate short-term traffic state prediction can improve the performance of real-time t...