A traffic analysis system analyzes location data from a plurality of vehicles to determine journeys made by the vehicles. Vehicles may make one or more rest stops during a journey. The traffic analysis system compares rest periods to journey criteria to determine whether a rest period delineates the end of a journey, or whether a rest period is still within the journey. In this way, a plurality of trips can be chained together into a journey to provide more accurate analysis of traffic patterns.

A system and method for collecting, processing, storing, or transmitting traffic data. A localized data collection module may retrieve, receive, or intercept traffic data through or from hardware installed in a traffic control cabinet adjacent an intersection or other roadway feature of interest. Data which may have previously been confined to a closed loop traffic control system may be remotely accessible for traffic operations control or monitoring via a network connected server and/or cloud architecture.

In an example, a method may assign a first drone of a drone network a first task, the first task may instruct the first drone to transport a first package to a first destination in a geographic area. The method may receive roadway traffic data for a plurality of roadway vehicles in the geographic area; determine, based on the roadway traffic data and during transit of the first package to the first destination by the first drone, to transfer the first package to a second drone in the drone network; and transfer the first package to the second drone in the drone network.

The present invention discloses a method for expressing traffic flow characteristics based on a quantum harmonic oscillator model, including: (1) constructing an energy eigenequation of a quantum harmonic oscillator (QHO) for vehicle movement, and converting the energy eigenequation to an Hermite polynomial; (2) solving traffic flow characteristic parameters using K-order Hermite polynomial approximation; and (3) expressing the traffic flow characteristic parameters on a sphere. On the premise of the autonomous decision of a driving strategy by a driver and centering on the objective limitation that the individual accurate state information in the long-distance expressway traffic flow is not observable, the dynamic evolution of the speed and the state of the vehicle is described using a quantum state, the driving state of the vehicle is expressed as the superposition state of three driving states, and the probability of the three states is represented using QHO model parameters.

System, methods, and other embodiments described herein relate to improving monitoring of traffic flows. In one embodiment, a method includes aggregating perception data associated with a road network from information sources to a server over a network. The method also includes generating a graph structure from the perception data in association with a neural network model. The graph structure is an incomplete representation of the road network in view of missing data. The method also includes completing the graph structure using the neural network model that forms a graph model of the traffic flows to de-noise the graph structure according to road constraints between two points in the road network. The method also includes communicating the graph model of the traffic flows to a vehicle to navigate traffic in the road network.

The present invention relates to systems and methods that allocate, arrange, and distribute certain types of functions and intelligence, for connected automated vehicle highway (CAVH) systems, to facilitate vehicle operations and controls, to improve the general safety of the whole transportation system, and to ensure the efficiency, intelligence, reliability, and resilience of CAVH systems. The present invention also provides methods to define CAVH system intelligence and its levels, which are based on two dimensions: the vehicle intelligence and infrastructure intelligence.

A cooperative vehicle-infrastructure processing method and apparatus, an electronic device, and a computer-readable storage medium are disclosed in embodiments of this application. The method includes: obtaining longitude and latitude position information and absolute elevation information of a traffic object on a road through roadside infrastructure, the traffic object including at least one of a road event, a traffic sign, and a traffic participant; converting the absolute elevation information of the traffic object into a roadside map-based relative elevation representation according to surrounding road network information in a roadside map unit deployed in the roadside infrastructure; and generating a roadside notification message according to the longitude and latitude position information and the relative elevation representation of the traffic object, and broadcasting the roadside notification message, a broadcast range of the roadside notification message covering at least a plurality of roads, so that vehicles on the plurality of roads make vehicle safety early warning decisions based on the roadside notification message. Technical solutions of the embodiments of this application can achieve a more accurate vehicle safety early warning.

A traffic flow forecasting method based on Deep graph Gaussian processes includes: S1, with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an Aggregation Gaussian process to extract dynamic spatial features; S2, obtaining a Temporal convolutional Gaussian process from weights at different times and a convolution function that obeys the Gaussian processes, and obtaining temporal features in traffic data by combining the Aggregation Gaussian process; S3, constructing a Deep graph Gaussian process method integrating a Gaussian process and a depth structure from the Aggregation Gaussian process, the Temporal convolutional Gaussian process and a Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the Deep graph Gaussian process method to obtain a forecasted result.

A regional dynamic perimeter control method and system for preventing boundary links queuing overflow. The method includes: estimating the number of queuing vehicles of boundary links using a Kalman filtering extension method using traffic flow information, and calculating a maximum of receivable vehicles; dividing the boundary links utilizing an estimated number of each boundary link's queuing vehicles and the maximum number of receivable vehicles obtaining a boundary link set with sufficient storage and a boundary link set with insufficient storage; obtaining a critical accumulation of a region according to a preset Macroscopic Fundamental Diagram (MFD) model of the region, and predicting the estimated region accumulation in a sampling period; and controlling a regional boundary intersection traffic flow operation using a deviation between the predicted and critical accumulation and boundary link sets. Deterioration of regional traffic flow is avoided, and the probability of overflow of boundary links is reduced.

A method for traffic diversion management in a smart city is provided. The method applied to the management platform includes obtaining a first traffic feature of a target road within a first time period from an object platform through a sensor network platform, wherein the first traffic feature is a feature reflecting a flow situation of the target road, determining a target tidal lane opening scheme of the target road within the first time period based on the first traffic feature, wherein the target tidal lane opening scheme is a scheme for managing an opening time of a tidal lane, a flow direction of the tidal lane, and a number of tidal lanes for the target road, sending the target tidal lane opening scheme to the target object through a sensor network platform, and sending the target tidal lane opening scheme to the user platform through a service platform.