A method, apparatus and computer program product for activating a flood event warning are described herein. In the context of a method, a location may be identified as a flood prone location. Data relating to the flood prone location may be received from one or more remote devices. The method may determine a flood confidence for the flood prone location based upon the data. The method may identify an active flood event for the flood prone location based on the flood confidence and cause a flood event warning to be activated in an instance in which the active flood event is identified.

A system and a computer-implemented method employ an appointment optimization and route planning system (AORPS) for optimizing home-visit appointments and related travel for delivering patient care. The AORPS receives registration and patient data from patients and client input including information about healthcare providers, onsite care coordinators, health plans, appointment types, and success rates from a client. The AORPS collates the patient data and generates an input matrix from the client input and the collated patient data. The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on appointment and patient history, feedback, and healthcare data. The AORPS generates an appointment schedule with travel routes dynamically based on optimization factors derived from the client input, the collated patient data, the input matrix, the healthcare data, and the predictive model, incorporating real-time changes in patient data, the client input, the optimization factors, and appointments.

A method, system and apparatus for accessing an internal database that includes at least one payroll database, identifying an individual, a first location, a second location, a future date, and a time of day. Responsive to identifying the individual, the first location, the second location, the date, and the time of day, determining a predicted travel time for the individual between the first location and the second location at the time of day on the future date.

Methods and systems for traffic jam management include generating a representation of a road network that comprises a road graph, with vertices of the road graph representing road segments and edges of the road graph representing pairs of adjacent road segments at each intersection. The road graph is partitioned into sub-graphs to balance a number of messages received from vehicles within each sub-graph and to minimize vehicle transitions between sub-graphs. It is determined that a traffic jam is present on one or more road segments. Navigational information is provided to one or more vehicles responsive to the traffic jam.

In some examples, a system may receive location information. The system may determine at least one landmark based on the location information. In addition, the system may determine one or more current conditions for the at least one landmark. Further, the system may receive sensor configuration information. Based on the sensor configuration information and the one or more current conditions, the system may determine the detectability of the at least one landmark.

A system including a processor and memory may provide for automatically activating or deactivating a feature of a fleet vehicle. For example, one or more fleet vehicles may include one or more of a global-positioning system, a speed governor, electronically-controlled brakes, an electronically-controlled accelerator, a speed limiter, or an on-board computer with a processor and memory. One or more features may be activated by a local or remote computing device or system. For example, a system may determine one or more recommended routes between two or more locations. The system may track a fleet vehicle's progress along a route, and activate a feature of the fleet vehicle based on the fleet vehicle following or not following the recommended route. For example, the system may cause activation of a speed limiter on the fleet vehicle, disable the fleet vehicle, and/or activate or deactivate autonomous features of the fleet vehicle.

Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

According to an embodiment, a method for providing a smart parking space guide service to provide a real-time moving route and occupancy state is executed by a parking space guide service provider server and comprises starting to trace a vehicle's moving route upon receiving the vehicle's entry event from an entry recognition device for recognizing entry of the vehicle into a parking lot, updating, in real-time, the vehicle's moving route upon real-time entry of the vehicle's moving route from at least one movement recognition device, displaying occupancy by the vehicle's parking on a pre-stored parking spot map upon sensing the vehicle's parking state from any one of at least one vehicle recognition sensor installed in at least one parking spot, and updating, in real-time, and displaying the vehicle's moving route and occupancy state on the pre-stored parking spot map.

Systems and methods for vehicle-based weather detection are disclosed herein. The systems and methods can include selecting one or more vehicles from a plurality of vehicles based on one or more network membership parameters. One or more data acquisition networks can be formed using the one or more selected vehicles. Sensor data can be received from the one or more data acquisition networks. One or more weather conditions can be predicted using the sensor data. One or more environmental elements can be updated based on the predicted weather conditions.

There is provided a technique for improving the accuracy of estimation of a position of a nearby vehicle. A nearby vehicle position estimation system is formed that includes a relative position information obtaining part that obtains relative direction of a nearby vehicle with respect to a host vehicle, the nearby vehicle being present around the host vehicle; a map information obtaining part that obtains map information around a current position of the host vehicle; and a nearby vehicle position estimating part that obtains, based on the map information, a nearby road that overlaps a straight line extending in the relative direction from the host vehicle, and estimates that the nearby vehicle is present on the nearby road.