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.

It is an object of the present invention to provide a predictive traffic law enforcement profiler apparatus and method which incorporates a means to determine current location, time, velocity and also incorporates a means to utilize a database derived from historic traffic law enforcement records, crowd sourced records and historical traffic data and also incorporates a predictive processing means to provide historic traffic law enforcement records and estimates of enforced speed limits and enforcement profiles, patrol locations and schedules of traffic law enforcement to a driver.

A method for trajectory planning for a commercial vehicle at a traffic junction, in particular including a traffic circle, by a control unit is provided. Trafficability of a lane of the commercial vehicle within a lane width of the traffic junction is checked prior to traveling into the traffic junction, traveling through a center of the traffic junction via an alternative trajectory is checked when normal negotiating of the traffic junction is not possible, and detection and/or prediction of surrounding traffic and trajectory planning of the alternative trajectory are carried out, taking the detected and/or predicted surrounding traffic into account. Moreover, a control unit, a computer program, and a machine-readable memory medium are provided.

Provided is a vehicle management system including a plurality of vehicles that execute different functions, and an event information server that transmits event occurrence information. The vehicles execute the same function corresponding to the event occurrence information, when a priority of a function corresponding to the event occurrence information received from the event information server is higher than a priority of a function currently being executed.

Apparatuses, systems, methods, and computer-readable media are provided for the preemptive logical configuration of vehicle control systems. A vehicle control computer may determine a location of a vehicle. The vehicle control computer may query a historical data source server for historical incident data corresponding to a first vicinity around the first location of the vehicle. Based on the historical incident data, the vehicle control computer may identify one or more driving danger areas in the first vicinity around the first location of the vehicle, wherein each of the one or more driving danger areas are associated with one or more driving hazards. The vehicle control computer may generate a configuration for vehicle operation in the first vicinity around the first location of the vehicle based on the one or more driving danger areas and may update driving logic of the vehicle with the configuration.

Methods, systems, and devices for dataset simplification of N-dimensional signals captured for asset tracking are provided. An example method involves obtaining raw data from a data source onboard an asset and determining whether obtainment of the raw data results in satisfaction of a data logging trigger. The method further involves, when the data logging trigger is satisfied, performing a dataset simplification algorithm on a target set of data within the raw data to generate a simplified set of data, wherein the target set of data contains a time-variant N-dimensional signal, N>=1, and the dataset simplification algorithm is generalized for all N>=1. The method further involves transmitting the simplified set of data to a server.

The present disclosure provides a method for controlling a traffic scheduling strategy in a smart city. The method includes obtaining traffic data information in a preset area during a current time period, the traffic data information at least including a speed of at least one vehicle on at least one road, predicting one or more target areas where traffic congestion is likely to occur from the preset area during a next time period based on the traffic data information in the preset area during the current time period, determining whether the 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 system for location risk determination and ranking based on vehicle events and/or an accident database includes an interface and a processor. The interface is configured to receive accident data and/or event data. The processor is configured to determine a set of incident groups, wherein an incident group of the set of incident groups is associated with a set of accidents of the accident data and/or a set of events of the event data that are grouped by location proximity. The processor is further configured to determine a set of traffic estimations, wherein a traffic estimation of the set of traffic estimations is associated with the incident group of the set of incident groups; and determine a ranking of the set of incident groups based at least in part on the set of accidents, the set of events, and the set of traffic estimations.

Embodiments of this application provide a target road segment speed prediction method, apparatus, a server, and a non-transitory computer-readable medium. In the method, a first moving speed on a target road segment at a first time point is obtained. A plurality of second moving speeds on the target road segment at each of a plurality of time points before the first time point is obtained. A mean historical speed on the target road segment at the first time point during previous time cycles is obtained. A speed prediction feature of the target road segment at a second time point is determined. A moving speed on the target road segment at the second time point is predicted according to the speed prediction feature of the target road segment at the second time point and a pre-trained road segment speed prediction model.

A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.