Abstract: Described techniques receive signal state data from a controller controlling one or more signal groups of a traffic light system, with a current signal state of respective signal groups, and information about the time interval elapsed since the last state switch of respective signal groups. A feature vector is generated with a first value indicating the elapsed time since the latest switch of the respective signal group to a pass-state, and a second value indicating the elapsed time since the latest switch of the respective signal group to a stop-state. Based on the current feature vector, a quantile regression neural network predicts a low conditional quantile representing the minimum-end-time, a medium conditional quantile representing the likely-time and a high conditional quantile representing the maximum-end-time for respective signal groups. A message which includes the predicted minimum-end-time, likely-time, and maximum-end-time is provided to a receiving device associated with the vehicle.

Abstract: Signal Phase and Timing (SPaT) messages are provided to control operation of a vehicle. A computer system receives switching state data (SD1) from one or more traffic lights and provides a SPaT message to the vehicle. The SD1 of a traffic light includes a pass-state (SD1p) and a stop-state (SD1s) data at respective sampling time points. A signal analyzer in the computer system analyzes the SD1 by: identifying the current signal state (SD1s, SD1p) of the one or more traffic lights; deriving, from a statistical model, probabilities for future state transitions for one or more future prediction intervals; and determining a minimum end time for a state transition from a current state to the different state. A message composer composes the SPaT message including the determined minimum end time.

Abstract: Signal Phase and Timing (SPaT) messages are provided to control operation of a vehicle. A computer system receives switching state data (SD1) from one or more traffic lights and provides a SPaT message to the vehicle. The SD1 of a traffic light includes a pass-state (SD1p) and a stop-state (SD1s) data at respective sampling time points. A signal analyzer in the computer system analyzes the SD1 by: identifying the current signal state (SD1s, SD1p) of the one or more traffic lights; deriving, from a statistical model, probabilities for future state transitions for one or more future prediction intervals; and determining a minimum end time for a state transition from a current state to the different state. A message composer composes the SPaT message including the determined minimum end time.

Abstract: A method of optimizing the traffic control at a traffic signal-controlled intersection in a road traffic network. The vehicle traffic in entrances to the intersection are controlled by signal groups of a traffic signal system according to associated signal times. For vehicles approaching the signal group, traffic parameters are determined from traffic data using a traffic model according to the signal times, and in order to determine optimal signal times, the traffic parameters are weighted and added up and the target function formed in such a way is optimized by varying the signal times. By individually determining the traffic parameters for each vehicle and individually weighting the traffic parameters according to the strategic relevance thereof for the implementation of a specified traffic strategy, an improved implementation of the specified traffic strategy is made possible.

Abstract: A method of optimizing the traffic control at a traffic signal-controlled intersection in a road traffic network. The vehicle traffic in entrances to the intersection are controlled by signal groups of a traffic signal system according to associated signal times. For vehicles approaching the signal group, traffic parameters are determined from traffic data using a traffic model according to the signal times, and in order to determine optimal signal times, the traffic parameters are weighted and added up and the target function formed in such a way is optimized by varying the signal times. By individually determining the traffic parameters for each vehicle and individually weighting the traffic parameters according to the strategic relevance thereof for the implementation of a specified traffic strategy, an improved implementation of the specified traffic strategy is made possible.

Abstract: Methods for determining turning rates in a road network are provided. Traffic volumes are recorded at measurement cross-sections at predefinable measuring intervals. For at least one forward-related subnetwork of the road network in which measurement cross-sections are taken into account at an exit and at entries of the subnetwork, a model equation is formulated in which the exit traffic volume is set as the weighted sum of the entry traffic volumes and the weighting factors correspond to the forward-related turning rates which specify in each case the portion of an entry traffic volume flowing out through the exit taken into account, and wherein the forward-related turning rates are calculated on the basis of the model equation using a mathematical estimation method.

Abstract: A method of determining a tailback characteristic factor ? at operating stations for processing individually moving units having alternating hold-back and release phases and having a detector upstream of the respective operating station includes measuring the filling time between the hold-back start or a time instant tied to the hold-back start and continuous occupancy of the detector and subsequent comparison with a reference filling time. A first value is assigned to the tailback characteristic factor ? if the reference filling time is exceeded and a second value is assigned if the reference filling time is not exceeded.