A method for constructing a prediction model of an auto trips quantity and a prediction method and system are disclosed. The prediction model construction method designs a deep neural network Multitask GCN-LSTM based on GCN and LSTM for predicting the auto trips quantity. The deep neural network comprises three modules, wherein the three modules are respectively used for extracting a spatial correlation, a temporal correlation and a feature fusion. The prediction method and system predict the auto trips quantity based on a model constructed. By considering a road segment local relationship and a road segment global relationship and taking an auto arrival quantity as a related task in constructing the model, the prediction model construction method uses a multi-task learning method to avoid overfitting of the deep neural network and reduce a prediction error of the auto trips quantity effectively.

Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

An on-board unit (OBU) of a vehicle receives one or more data messages indicative of intersection geometry for an upcoming intersection along a roadway being traversed by the vehicle and traffic control status of a traffic control of the intersection. An outbound direction for the vehicle through the intersection is identified. A first traffic message is sent to preempt the traffic control to allow the vehicle to perform a maneuver to traverse the intersection in the outbound direction. The maneuver is indicated as complete and a second traffic message is sent to discontinue the preempt of the traffic control.

Various embodiments are described herein for systems and methods of traffic management in a road network including pathways and at least one intersection. In at least one embodiment, the method comprises receiving data signals from corresponding one or more connected vehicles and generating an intersection model for each approach of each intersection at a first time, where the intersection model comprises estimated arrival times for incoming vehicles at each approach. The method further comprises generating at the first time, for each intersection, candidate traffic timing data signals based at least on the intersection model corresponding to all approaches at the intersection, and generating, at the first time, for each intersection, an optimized traffic timing data signal, which is configured to control the operation of one or more traffic signals at the intersection, and is generated based on the candidate traffic timing data signals and a predetermined optimization variable.

A method may include a processing system having at least one processor obtaining a first plurality of domain name system traffic records, generating an input aggregate vector from the first plurality of domain name system traffic records, where the input aggregate vector comprises a plurality of features derived from the first plurality of domain name system traffic records, and applying an encoder-decoder neural network to the input aggregate vector to generate a reconstructed vector, where the encoder-decoder neural network is trained with a plurality of aggregate vectors generated from a second plurality of domain name system traffic records. In one example, the processing system may then calculate a distance between the input aggregate vector and the reconstructed vector, and apply at least one remedial action associated with the first plurality of domain name system traffic records when the distance is greater than a threshold distance.

A system for monitoring a traffic situation includes at least one radar sensor unit for recording environmental data in a monitoring region of the radar sensor unit. A central processor evaluates the environmental data from the radar sensor unit and determines the traffic situation within the monitoring region based on the environmental data. A reference signal unit for outputting a reference signal to the radar sensor unit is configured to modulate a radar signal emitted by the radar sensor unit and to reflect a modulated radar signal back to the radar sensor unit as a reference signal. The processor identifies the modulated radar signal as a reference signal from the reference signal unit and recognizes a malfunction of the radar sensor unit if a reference signal is not received by the radar sensor unit.

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.

Various embodiments include a driver assistance system for determining the position of a stopping point of a vehicle at an infrastructure device comprising: a control unit; a communication device for receiving data from a server or from the infrastructure device; and a sensor arrangement for capturing vehicle data or environmental data. The control unit determines the location of the stopping point at the infrastructure device based at least in part on the data and the vehicle data or environmental data.

Devices, systems, and methods related to incentives for complying with suggested driving operations can include determining suggested driving operations, determining a desirability of the suggested driving operations, communicating the desirability of the suggested driving operations, and optionally monitoring compliance with the suggested driving operations.

An apparatus and a method for providing traffic information are provided. The apparatus includes a traffic data database (DB) that stores traffic data and a processor connected to the traffic data DB. The processor generates a prediction model to predict a traffic flow and determines an appropriate data input range for a target time point in the future using the prediction model. Additionally, the processor extracts past traffic data from the traffic data DB based on the determined appropriate data input range, predicts a traffic flow at the target time point based on the extracted past traffic data and provides the predicted traffic flow as traffic information.