Research has shown that GLOSA systems can help reduce the emission of $CO_2$ by motor vehicles and improve flow on urban road networks. However, existing GLOSA systems only work in combination with a limited selection of Traffic Signal Controllers (TSCs) and therefore have not been widely implemented. This research describes and evaluates the design of a system to model the behaviour of the most common types of TSCs using data that is shared by road users, also known as Floating Car Data (FCD). Almost every road user is able to share his location using a smartphone, so this means that a system of this kind can be implemented everywhere in the world where people are willing to participate, without requiring any changes to the infrastructure. It is chosen to model TSC behaviour using Hidden Markov Models (HMMs), because they can be generated from unlabelled data, as opposed to other machine learning techniques. More specifically, Explicit Duration Hidden semi-Markov Models (EDHSMMs) are used, so that timing behaviour can be captured more accurately than with standard HMMS. An adaptation to an existing learning algorithm is made to decrease amount of data required for learning and to make sure the algorithm can cope with the sparse nature of FCD. Additionally, a system is developed to combine generally known intersection data and FCD to generate models that have transition parameters with low entropy and thus high predictive powers. In this way, the system produces models that are accurate enough to be used for the generation of GLOSA from relatively sparse data.
In a simulated setting, it is shown that the system can identify the true switching behaviour of the most common types of TSCs and that it produces models whose duration probability distributions converge to the true situation when provided with realistic amounts of data.