A controller monitors the number of traveling vehicles scheduled to enter a sectioned area in addition to the number of existing vehicles, determines whether the traveling vehicles, scheduled to enter the sectioned area when the total of the number of existing vehicles and the number of vehicles scheduled to enter the sectioned area is acquired, can enter the sectioned area based on the total number of vehicles, determines whether a route including the sectioned area in a traveling route can be newly set, and controls the traveling of the traveling vehicle based on the determination on the entry and the determination on the setting.

A first learning unit (101) learns a difference model (111) for predicting a difference between current monitoring data that is monitoring data obtained by monitoring a monitoring target at each time point and at each of a plurality of monitoring points and is monitoring data at a current time point, and past monitoring data that is monitoring data at each of a plurality of past time points; a second learning unit (102) learns a prediction model (past) (112) for predicting variation of the monitoring target using the past monitoring data; a first generation unit (103) generates corrected past data using the difference model (111), by correcting a difference between the past monitoring data and the current monitoring data; and a third learning unit (104) learns a prediction model (current) (113) for predicting variation of the monitoring target using the current monitoring data, the difference model (111), the prediction model (past) (112), and the corrected past data, whereby variation of the monitoring target can be appropriately predicted even when the monitoring target involves irregular variation.

The present method permits to get real-time traffic data by the mean of pictures took by a series of georeferenced and synchronized high speed cameras installed on the portions of the road. These pictures and these data will be transferable by a secure means such as 5G or any other fast and secure technology on a server and accessible by cloud computing. Picture processing is carried out by photogrammetric, triangulation and picture recognition approaches in order to extract the position of each vehicle, pedestrian, cyclist or any object and identify its x, y, z coordinates in a local or global referencing system. This method permits to count the flow of traffic (vehicles, pedestrians, etc.) passing through these roads portions, to reproduce the real movements of vehicles, pedestrians and any object moving on a road, make simulations with a computer and intervene remotely in real-time to manage traffic.

The present invention is an information management apparatus, capable of communicating with a plurality of vehicles, the apparatus comprising a receiving unit for receiving information indicating an occurrence of a failure in one vehicle of the plurality of vehicles, from the one vehicle, and a transmission unit for transmitting information based on the failure to the other vehicles of the plurality of vehicles, thereby, a recurrence of a similar failure in a predetermined region can be appropriately prevented.

Embodiments of systems and methods for automated real-time routing within a fleet of geographically distributed drivers are disclosed. Embodiments may operate to dispatch orders and determine routing in real-time in a geographic area through application of rule-based filtering of drivers and selective application of optimal or non-optimal routing solutions utilizing the real-time locations of drivers, real-time conditions within the geographic area and the locations for the set of orders being routed by the system.

A system and method for predicting travel time of a vehicle on routes of unbounded length within arterial roads. It collects historical information from probe vehicles positions using GPS technology in a periodic fashion and the sequence of links traversed between successive position measurements. Further, it collects information of neighborhood structure for each link within the arterial roads network. Any of the existing conditional probability distribution functions could be used to capture the spatio-temporal dependencies between each link of the arterial network and its neighbors. It learns the parameters of this data driven probabilistic model from historical information of probe vehicle trajectories traversed within the arterial roads network using an associated expectation maximization method. Finally it predicts travel time of vehicles on routes of unbounded length in a novel fashion within the network of arterial roads using the learnt parameters and current real time information of trajectories of vehicle.

A computer-implemented method for configuring a system model and a computer-implemented method for configuring a sensor model. There is also described a computer-implemented method for determining future switching behavior of a system unit, with the following steps: a) receiving the configured system model; b) receiving the configured sensor model, c) the configured sensor model being a probability distribution regarding how the sensor unit will behave in the specific time period; d) establishing at least one random sample of behavior of a sensor unit by sampling from the probability distribution; and e) determining the future switching behavior of the system unit and/or at least one associated statistical value on the basis of the established random sample by means of the trained system model. There is also described a corresponding computer program product.

Driving behavior for a particular driver may be gathered and analyzed over a time period, such as a year or a duration of the driver's employment at a particular employer. The driving behavior is received by a server from a positioning device as multiple streams of position data at different time stretches throughout the time period, each stream of position data associated with a route of a plurality of routes driven by at least one vehicle. The server generates speed data from the streams of position data and compares the speed data to retrieved speed limit data for those routes. The server generates a report with at least a speeding percentage value corresponding to how often the driver was speeding while driving during the time period. The report is then sent to the driver's computing device.

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 method and device for evaluating a function for predicting a trajectory of an object in an environment of a vehicle. The method includes acquiring first environment data values representing the environment of the vehicle at a first point in time, and the first environment data values including a start position of the object and object parameters; determining a trajectory of the object as a function of the start position and the object parameters, the trajectory including a possible position of the object at a second point in time later than the first point in time; acquiring second environment data values representing the environment of the vehicle at the second point in time and including an actual position of the object; determining an agreement between the possible position and the actual position at the second point in time; and evaluating the function as a function of the agreement.