A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine position of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict position of the object with respect to the predefined zone and future status of the associated traffic light, if position prediction is within a predetermined threshold, modify the current status, wherein t.sub.green is extended, if position prediction is outside of the predetermined threshold, modify the current status, wherein t.sub.green is reduced, and repeating above steps until the object has cleared the intersection.

A system to reduce probability of a crash in an intersection is disclosed which includes one or more way-sensor systems, the one or more sensor systems adapted to provide position and velocity vector of an object approaching the intersection on an associated path, a processing unit including configured to: determine position of the object with respect to a predefined zone of the intersection on the associated path, determine the current and future status of the associated traffic lights on the associated path of the object, predict position of the object with respect to the predefined zone and future status of the associated traffic light, if position prediction is within a predetermined threshold, modify the current status, wherein t.sub.green is extended, if position prediction is outside of the predetermined threshold, modify the current status, wherein t.sub.green is reduced, and repeating above steps until the object has cleared the intersection.

Traffic control systems, signal control systems, and methods of using such systems are disclosed. A real-time adaptive traffic control system includes multiple signal control systems, each associated with a signalized intersection. Each signal control system includes a receiver, a processor, and a transmitter. The receiver receives inflow traffic control information from other signal control systems and information associated with one or more vehicles or devices. The processor generates a signal timing plan based on the information and the inflow traffic control information. The transmitter transmits outflow traffic control information to other signal control systems. A fee or toll may be incurred for receiving the route information associated with a vehicle or device. The result of providing such route information may be better traffic flow for the transmitting vehicle or device and other vehicles or devices resulting from improved information being available to the traffic control system.

Traffic control systems, signal control systems, and methods of using such systems are disclosed. A real-time adaptive traffic control system includes multiple signal control systems, each associated with a signalized intersection. Each signal control system includes a receiver, a processor, and a transmitter. The receiver receives inflow traffic control information from other signal control systems and information associated with one or more vehicles or devices. The processor generates a signal timing plan based on the information and the inflow traffic control information. The transmitter transmits outflow traffic control information to other signal control systems. A fee or toll may be incurred for receiving the route information associated with a vehicle or device. The result of providing such route information may be better traffic flow for the transmitting vehicle or device and other vehicles or devices resulting from improved information being available to the traffic control system.

There is provided a system and method for traffic signal control of a traffic network with a learned model. The method including: receiving sensor readings from the traffic network, the sensor readings including positions and speeds of vehicles approaching each intersection; using a learned dynamics model that takes the sensor readings as input, predicting a plurality of possibilities for position and velocity of the vehicles approaching each intersection in a future timestep; determining a action for the one or more intersections by performing a tree search on the plurality of possibilities and selecting the possibility with a highest action value; and outputting the action to the traffic network for implementation as a traffic control action at the one or more intersections.

There is provided a system and method for traffic signal control of a traffic network with a learned model. The method including: receiving sensor readings from the traffic network, the sensor readings including positions and speeds of vehicles approaching each intersection; using a learned dynamics model that takes the sensor readings as input, predicting a plurality of possibilities for position and velocity of the vehicles approaching each intersection in a future timestep; determining a action for the one or more intersections by performing a tree search on the plurality of possibilities and selecting the possibility with a highest action value; and outputting the action to the traffic network for implementation as a traffic control action at the one or more intersections.

A time-segmented signal timing method is related to the field of intelligent transportation. The specific implementation is: obtaining traffic volume data of a target intersection in each period of time of N consecutive days, N being an integer greater than 1; performing curve fitting on the traffic volume data to determine a traffic volume curve corresponding to the target intersection of each day; determining calculating respective target points in the traffic volume curve with second derivatives satisfying a preset condition; and performing time-segmented signal timing on a traffic light at the target intersection based on time points corresponding to the respective target points.

A time-segmented signal timing method is related to the field of intelligent transportation. The specific implementation is: obtaining traffic volume data of a target intersection in each period of time of N consecutive days, N being an integer greater than 1; performing curve fitting on the traffic volume data to determine a traffic volume curve corresponding to the target intersection of each day; determining calculating respective target points in the traffic volume curve with second derivatives satisfying a preset condition; and performing time-segmented signal timing on a traffic light at the target intersection based on time points corresponding to the respective target points.

Methods and systems may provide for technology to identify that a transmission includes one or more of vehicle sensor data sensed by a vehicle or at least one feature identified from the vehicle sensor data. The vehicle is associated with vehicle identification data unique to the vehicle. The technology also identifies that the vehicle identification data is associated with the transmission, identifies that one or more parameters that are associated with the vehicle identification data, and adjusts at least one parameter of the identified one or more parameters.

Methods and systems may provide for technology to identify that a transmission includes one or more of vehicle sensor data sensed by a vehicle or at least one feature identified from the vehicle sensor data. The vehicle is associated with vehicle identification data unique to the vehicle. The technology also identifies that the vehicle identification data is associated with the transmission, identifies that one or more parameters that are associated with the vehicle identification data, and adjusts at least one parameter of the identified one or more parameters.