A system for prioritizing traffic having a traffic signal controller (TSC), a processor, a database, and one or more detectors configured to detect the presence of a vehicle having a vehicle identifier. The system is configured so that the processor is in communication with the TSC and at least one detector, and the detectors are configured to detect passing vehicles having a vehicle identifier. In a case the vehicle identifier has a record within the database, the processor an approximate travel time of the vehicle identifier between a location of the detector where the vehicle identifier is detected and a traffic signal connected to the TSC, and a command is set to be sent from the processor to the TSC to change the traffic signal connected to the TSC in a direction of travel of the vehicle identifier if one or more conditions are met. The command sent from the processor to the TSC has a delay of between zero seconds and the approximate travel time calculated.

A system for prioritizing traffic having a traffic signal controller (TSC), a processor, a database, and one or more detectors configured to detect the presence of a vehicle having a vehicle identifier. The system is configured so that the processor is in communication with the TSC and at least one detector, and the detectors are configured to detect passing vehicles having a vehicle identifier. In a case the vehicle identifier has a record within the database, the processor an approximate travel time of the vehicle identifier between a location of the detector where the vehicle identifier is detected and a traffic signal connected to the TSC, and a command is set to be sent from the processor to the TSC to change the traffic signal connected to the TSC in a direction of travel of the vehicle identifier if one or more conditions are met. The command sent from the processor to the TSC has a delay of between zero seconds and the approximate travel time calculated.

A system and method of multi-agent reinforcement learning for integrated and networked adaptive traffic controllers (MARLIN-ATC). Agents linked to traffic signals generate control actions for an optimal control policy based on traffic conditions at the intersection and one or more other intersections. The agent provides a control action considering the control policy for the intersection and one or more neighboring intersections. Due to the cascading effect of the system, each agent implicitly considers the whole traffic environment, which results in an overall optimized control policy.

A system and method of multi-agent reinforcement learning for integrated and networked adaptive traffic controllers (MARLIN-ATC). Agents linked to traffic signals generate control actions for an optimal control policy based on traffic conditions at the intersection and one or more other intersections. The agent provides a control action considering the control policy for the intersection and one or more neighboring intersections. Due to the cascading effect of the system, each agent implicitly considers the whole traffic environment, which results in an overall optimized control policy.

A system includes: a laser radar irradiating a detection region and detecting a reflected signal of the laser by a moving body in the detection region at a predetermined cycle, a vehicle detecting unit detecting a vehicle existing in the detection region based on the reflected signal detected by the laser radar, a vehicle tracking unit setting a plurality of divided regions in the detection region and detecting a moving direction of the vehicle based on a presence or absence of the vehicle in each of the divided regions detected by the vehicle detecting unit at each predetermined cycle, and a traffic flow calculating unit calculating a traffic flow data including a number of vehicles in each of the divided regions detected by the vehicle detecting unit and the moving direction of each vehicle detected by the vehicle tracking unit.

A system includes: a laser radar irradiating a detection region and detecting a reflected signal of the laser by a moving body in the detection region at a predetermined cycle, a vehicle detecting unit detecting a vehicle existing in the detection region based on the reflected signal detected by the laser radar, a vehicle tracking unit setting a plurality of divided regions in the detection region and detecting a moving direction of the vehicle based on a presence or absence of the vehicle in each of the divided regions detected by the vehicle detecting unit at each predetermined cycle, and a traffic flow calculating unit calculating a traffic flow data including a number of vehicles in each of the divided regions detected by the vehicle detecting unit and the moving direction of each vehicle detected by the vehicle tracking unit.

The invention relates to a traffic signal control field, discloses method and system of dynamic adjust signal time according to traffic flows in order to decrease stops/starts and green-light idle time: method includes: 1) get parameters of roadnet, signals; 2) get traffic flows; 3) I-neurons predict traffic flows about over thresholds; 4) P-neuron determine pre-judges according to over thresholds of intersection; 5) overall trade-off accept/reject, priority, schedule, and management of pre-judges, make and send I-instructions; system includes: 1) predict method package; 2) traffic data center or vehicle queue detecting equipments; 3) or vehicle in/out detectors; 4) traffic signals controllers. The predicting math model based signals net universal “A-A” serial method, support String mode control, enable roadnet traffic always run low energy consumption signals, avoid redundant stops/starts one time per period per vehicle per road-segment about 60 seconds and idle gasoline consumption, 30 vehicles about 30 minutes idle gasoline consumption per road-segment, and with solitary wave technique for dissolving jam-core, suddenly-happened big queue, provide a serial continuity solution means for signal control to dissolve congestion core, early congestion, delay arrival of a large cluster of congestion, improve efficiency of traffic signal response.

The invention relates to a traffic signal control field, discloses method and system of dynamic adjust signal time according to traffic flows in order to decrease stops/starts and green-light idle time: method includes: 1) get parameters of roadnet, signals; 2) get traffic flows; 3) I-neurons predict traffic flows about over thresholds; 4) P-neuron determine pre-judges according to over thresholds of intersection; 5) overall trade-off accept/reject, priority, schedule, and management of pre-judges, make and send I-instructions; system includes: 1) predict method package; 2) traffic data center or vehicle queue detecting equipments; 3) or vehicle in/out detectors; 4) traffic signals controllers. The predicting math model based signals net universal “A-A” serial method, support String mode control, enable roadnet traffic always run low energy consumption signals, avoid redundant stops/starts one time per period per vehicle per road-segment about 60 seconds and idle gasoline consumption, 30 vehicles about 30 minutes idle gasoline consumption per road-segment, and with solitary wave technique for dissolving jam-core, suddenly-happened big queue, provide a serial continuity solution means for signal control to dissolve congestion core, early congestion, delay arrival of a large cluster of congestion, improve efficiency of traffic signal response.

The disclosure presents methods and apparatus for adaptive control of one or more traffic signals. A method may include determining an offset value based on a function of a traffic flow performance metric. The method may further include determining a green time split value based on a distributed algorithm. The method may further include adaptive control of the one or more traffic signals based on the green time split value and the offset value.

The present disclosure provide a method and an apparatus for detecting light state data, which includes: acquiring light state data of traffic lights, and acquiring control information of the traffic lights, where the light state data includes period time information and/or phase sequence information, the period time information characterizes time information of lighting each light holder in the traffic lights in a period, the phase sequence information characterizes a green light order of each phase corresponding to the traffic lights; and the control information characterizes a control rule of the traffic lights; and performing consistency matching between the light state data and the control information to obtain a first matching result, and determining a detection result of the light state data according to the first matching result.