Traffic signal timing plans are derived from vehicle trajectory or probe data. The probe data is collected and archived in a datastore over a sample time on the order of weeks or longer. Probe data is corrected for clock drift, geo-fence filtered to a selected intersection, and then stop line crossings in the intersection are identified and analyzed along with related data to determine the timing plans and schedule for the intersection. In this way, access to government agency timing plans is obviated so as to save time and expense.

A traffic light control assembly includes a plurality of mounting poles each attached to a cross beam of a respective traffic signal at a roadway intersection. A plurality of light detection and ranging sensors is each mounted to a respective mounting pole to be elevated over traffic on the roadway. Each of the light detection and ranging sensors is positioned to sense the number of vehicles that are stopped at an opposing traffic signal. Each of the light detection and ranging sensors is in electrical communication with a remote data unit thereby facilitating the remote data unit to analyze data gathered by each of the light detection and ranging sensors with respect to the number of vehicles. Moreover, the remote data unit adjusts timing of the traffic signals to most efficiently direct traffic through the intersection with respect to the number of vehicles that are approaching the intersection.

A traffic light control assembly includes a plurality of mounting poles each attached to a cross beam of a respective traffic signal at a roadway intersection. A plurality of light detection and ranging sensors is each mounted to a respective mounting pole to be elevated over traffic on the roadway. Each of the light detection and ranging sensors is positioned to sense the number of vehicles that are stopped at an opposing traffic signal. Each of the light detection and ranging sensors is in electrical communication with a remote data unit thereby facilitating the remote data unit to analyze data gathered by each of the light detection and ranging sensors with respect to the number of vehicles. Moreover, the remote data unit adjusts timing of the traffic signals to most efficiently direct traffic through the intersection with respect to the number of vehicles that are approaching the intersection.

The present disclosure provides a roadside sensing system and a traffic control method. The roadside sensing system may include: a basic supply facility, a roadside computing facility and a roadside sensing facility set on a target road section. The basic supply facility is connected to the roadside computing facility and the roadside sensing facility, and the basic supply facility is configured to provide basic supply to the roadside computing facility and the roadside sensing facility; the roadside sensing facility is configured to acquire roadside sensing information; and the roadside computing facility is configured to store and process the roadside sensing information to obtain sensing result information.

The present disclosure provides a method for managing traffic congestion in a smart city. The method includes predicting, based on a trained traffic state prediction model, one or more target areas where the traffic congestion is likely to occur from the preset area during a next time period by processing the traffic data information during the current time period, the traffic state prediction model being a Graph Neural Network (GNN) model and a predicted result being output by at least one node of a traffic state prediction model; determining whether a traffic scheduling strategy is needed to be switched based on traffic data information in the one or more target areas during the next time period; and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.

A method and apparatus for level of service assessment at signalized intersections is disclosed. In an exemplary embodiment, a method for estimating an average delay per vehicle at a signalized intersection with a traffic signal, including sampling vehicle arrival rates at the signalized intersection, sampling vehicle departure rates at the signalized intersection, analyzing generated shock waves at the traffic signal, wherein the traffic signal shock wave is a change in vehicle density due to changes in the traffic signal, and estimating the average delay per vehicle based on the vehicle arrival rates, the vehicle departure rates, and the traffic shock waves at the signalized intersection.

A sensor abnormality estimation device determines whether or not a position and a speed of a sensor-mounted vehicle at an intersection fulfill a predetermined performance condition. When the position and the speed fulfill the performance condition, the sensor abnormality estimation device acquires recognition results obtained by two of a plurality of external sensors, for a target object at a designated position associated with the position of the sensor-mounted vehicle. The sensor abnormality estimation device then acquires a degree of coincidence between the recognition results, and determines that there is an abnormality in at least one of the two external sensors when the degree of coincidence is lower than a predetermined determination value.

A sensor abnormality estimation device determines whether or not a position and a speed of a sensor-mounted vehicle at an intersection fulfill a predetermined performance condition. When the position and the speed fulfill the performance condition, the sensor abnormality estimation device acquires recognition results obtained by two of a plurality of external sensors, for a target object at a designated position associated with the position of the sensor-mounted vehicle. The sensor abnormality estimation device then acquires a degree of coincidence between the recognition results, and determines that there is an abnormality in at least one of the two external sensors when the degree of coincidence is lower than a predetermined determination value.

A method and an electronic device for detecting a vehicle queue length are disclosed. The method includes: when determining that a traffic light turns green, obtaining vehicle information of each vehicle on a lane section to be detected in a first preset time period before a time point when the traffic light turns green; determining at least one vehicle that has a static position and a static time point on the lane section to be detected; determining a first queuing vehicle and a last queuing vehicle on the lane section to be detected based on the at least one vehicle that has the static position and the static time point on the lane section to be detected; and determining a vehicle queue length on the lane section to be detected based on a position of the first queuing vehicle and a position of the last queuing vehicle.

A method and an electronic device for detecting a vehicle queue length are disclosed. The method includes: when determining that a traffic light turns green, obtaining vehicle information of each vehicle on a lane section to be detected in a first preset time period before a time point when the traffic light turns green; determining at least one vehicle that has a static position and a static time point on the lane section to be detected; determining a first queuing vehicle and a last queuing vehicle on the lane section to be detected based on the at least one vehicle that has the static position and the static time point on the lane section to be detected; and determining a vehicle queue length on the lane section to be detected based on a position of the first queuing vehicle and a position of the last queuing vehicle.