Traffic congestion at signalized intersections is a big economical and ecological problem. Handcrafted traffic light controllers (TLCs) are currently used to minimize the impact, but they are expensive to design and maintain and their performance degrades over time. Predictive TLCs and advanced driver assistance systems (ADAS) form a potential solution but are still unfeasible in practice today because of their computational complexity and unpredictability. The distributed predictive TLC developed in this thesis, called DIRECTOR, is feasible and enables time to green/red and green light optimal speed advice (GLOSA) systems. DIRECTOR utilizes predictions of the arriving traffic flows and a model of the current queue length to optimize the traffic light schedule. It can operate in two modes; Ad-hoc mode, where the schedule is generated and applied right away, and fixed-ahead mode, where the schedule is fixed in advance to enable ADAS. DIRECTOR's design makes it scalable and suitable for live learning, eliminating the need for expensive (re)calibrations and improving its performance with more and better data, which will become available in the near future. A long short-term memory recurrent neural network is developed to predict the arriving traffic flows. On a case study this network proves to be on average 4.7% more accurate than the current state-of-the-art model, which is significant for a controller's performance. Simulations of the same case study intersection, which is currently equipped with a state-of-the-art actuated controller with green wave coordination, show that in ad-hoc mode DIRECTOR performs similar to the current controller. DIRECTOR reduces the average delay per vehicle by 1% (from 10.4s to 10.3s) at the cost of an increase of 15% in the average number of stops per vehicle (from 0.40 to 0.46) compared to the current controller. Simulations with ideal predictions show that, in ad-hoc mode, DIRECTOR has the potential to improve the average delay by 8.7% (from 10.4s to 9.5s) while keeping the number of stops equal (at 0.40). Simulations with GLOSA show a 30% reduction in the average number of stops at the cost of a 13% increase of the travel time compared to the ad-hoc mode. Combining this with ideal predictions shows that DIRECTOR in fixed-ahead mode has the potential to keep the average delay equal compared to the current controller, which will greatly improve traffic flow. Compared to a more typical Dutch actuated controller, DIRECTOR achieves a delay reduction of 39% in ad-hoc mode and 23% in fixed-ahead mode. Overall, DIRECTOR is a new data-driven traffic light controller that is relatively easy to set up, reduces costs, can enable advanced driver assistance systems, is futureproof and has the potential to greatly improve traffic flow.