Automatic real-time control of traffic perturbations has recently become a central issue for many European railway infrastructure managers. The aim is to make use of mathematical algorithms to maintain the required service availability during unplanned disturbances to operations. In the literature many tools for real-time traffic control are proposed, but their effects on traffic have never been studied neither in real life nor in fully realistic simulation environments. We can mention only a few pilot tests and a unique installation in the Lötschberg Base tunnel in Switzerland, which uses a replanning framework that is generally applicable but does not consider a standard data communication format. Standardisation of railway data is instead one of the main requirements of the European Railway Traffic Management System (ERTMS) to enable traffic interoperability across different countries along the Trans-European Transport Network (TEN-T). Following this direction, this paper introduces one of the outputs of the European project ON-TIME: a framework for the automatic real-time management of railway traffic, designed for being standard and interoperable across different European railways. We make use of a web-based Service-Oriented Architecture to ensure scalability and flexibility of application. A standard railML interface is used for the input/output data of the modules, allowing immediate applicability of the framework to any network having a railML representation. A proof-of-concept is provided where the framework is tested in a closed-loop with the simulation environment HERMES for perturbed traffic scenarios on different networks in the UK, the Netherlands and Sweden. Tests are performed for two different replanning algorithms (ROMA and RECIFE) used for the automatic detection and optimised resolution of train conflicts. The two algorithms are compared on the test case in Sweden. Results show that the design of the proposed framework is effective in managing traffic perturbations and is extendible to real-life systems.Automatic real-time control of traffic perturbations has recently become a central issue for many European railway infrastructure managers. The aim is to make use of mathematical algorithms to maintain the required service availability during unplanned disturbances to operations. In the literature many tools for real-time traffic control are proposed, but their effects on traffic have never been studied neither in real life nor in fully realistic simulation environments. We can mention only a few pilot tests and a unique installation in the Lötschberg Base tunnel in Switzerland, which uses a replanning framework that is generally applicable but does not consider a standard data communication format. Standardisation of railway data is instead one of the main requirements of the European Railway Traffic Management System (ERTMS) to enable traffic interoperability across different countries along the Trans-European Transport Network (TEN-T). Following this direction, this paper introduces one of the outputs of the European project ON-TIME: a framework for the automatic real-time management of railway traffic, designed for being standard and interoperable across different European railways. We make use of a web-based Service-Oriented Architecture to ensure scalability and flexibility of application. A standard railML interface is used for the input/output data of the modules, allowing immediate applicability of the framework to any network having a railML representation. A proof-of-concept is provided where the framework is tested in a closed-loop with the simulation environment HERMES for perturbed traffic scenarios on different networks in the UK, the Netherlands and Sweden. Tests are performed for two different replanning algorithms (ROMA and RECIFE) used for the automatic detection and optimised resolution of train conflicts. The two algorithms are compared on the test case in Sweden. Results show that the design of the proposed framework is effective in managing traffic perturbations and is extendible to real-life systems.