A computer system for providing Signal Phase and Timing, SPaT, messages for influencing operation of a vehicle (401, 501), wherein the SPaT messages include timing parameters for the minimum-end-time, likely-time, and maximum-end-time for the next signal switch of at least one signal group of a traffic light system affecting the vehicle, the computer system comprising: an interface component configured to receive signal state data from a controller controlling one or more signal groups (S1 to S4) of a traffic light system, wherein the signal state data of a particular signal group (S*) comprises a current signal state (SD*) of the respective signal group (S*), and information about the time interval (Te*) which has elapsed since the last state switch of the respective signal group (S*), and further configured to provide a SPaT message to a receiving device associated with the vehicle (401, 501); a data pre-processor configured to generate a feature vector for the one or more signal groups (S1 to S4) comprising for each of the one or more signal groups at least: a first value indicating the elapsed time (T*p) since the latest switch of the respective signal group (S*) to a pass-state, and a second value indicating the elapsed time (T*s) since the latest switch of the respective signal group (S*) to a stop-state; at least one quantile regression neural network configured to receive a particular current feature vector (T*p, T*s) as test input, and to predict for at least one of the one or more signal groups (S1 to S4), as one or more outputs, a low conditional quantile representing the minimum-end-time (ETmin), a medium conditional quantile (Qm) representing the likely-time (Tl) and a high conditional quantile (Qh) representing the maximum-end-time (ETmax) for the at least one of the one or more signal groups (S1 to S4), wherein the at least one quantile regression neural network has been trained with stochastic gradient descent, on past feature vectors of at least a subset of the one or more signal groups of the traffic light system and respective low, medium and high conditional quantile outputs, using a loss function being the sum of individual loss functions over the at least one of the one or more signal groups (S1 to S4), and the corresponding one or more conditional quantiles, wherein the individual loss functions are hinge loss functions; and a message composer configured to compose the SPaT message including the predicted minimum-end-time (ETmin), likely-time (Tl), and maximum-end-time (ETmax) of the at least one of the one or more signal groups (S1 to S4). an interface component configured to receive signal state data from a controller controlling one or more signal groups (S1 to S4) of a traffic light system, wherein the signal state data of a particular signal group (S*) comprises a current signal state (SD*) of the respective signal group (S*), and information about the time interval (Te*) which has elapsed since the last state switch of the respective signal group (S*), and further configured to provide a SPaT message to a receiving device associated with the vehicle (401, 501); a data pre-processor configured to generate a feature vector for the one or more signal groups (S1 to S4) comprising for each of the one or more signal groups at least: a first value indicating the elapsed time (T*p) since the latest switch of the respective signal group (S*) to a pass-state, and a second value indicating the elapsed time (T*s) since the latest switch of the respective signal group (S*) to a stop-state; at least one quantile regression neural network configured to receive a particular current feature vector (T*p, T*s) as test input, and to predict for at least one of the one or more signal groups (S1 to S4), as one or more outputs, a low conditional quantile representing the minimum-end-time (ETmin), a medium conditional quantile (Qm) representing the likely-time (Tl) and a high conditional quantile (Qh) representing the maximum-end-time (ETmax) for the at least one of the one or more signal groups (S1 to S4), wherein the at least one quantile regression neural network has been trained with stochastic gradient descent, on past feature vectors of at least a subset of the one or more signal groups of the traffic light system and respective low, medium and high conditional quantile outputs, using a loss function being the sum of individual loss functions over the at least one of the one or more signal groups (S1 to S4), and the corresponding one or more conditional quantiles, wherein the individual loss functions are hinge loss functions; and a message composer configured to compose the SPaT message including the predicted minimum-end-time (ETmin), likely-time (Tl), and maximum-end-time (ETmax) of the at least one of the one or more signal groups (S1 to S4).