Map data is received at a map-service provider associated with a blockchain from a data collector. The map data is transmitted to a data-service provider for processing. Non-crowdsourced data is received at the map-service provider from a data provider. The non-crowdsourced data is transmitted to the data-service provider for processing with the map data. A request is received for processed map data from a service user. The processed map data that is generated from the map data and the non-crowdsourced data is retrieved from the data-service provider. The processed map data is transmitted to the service user in response to the request.

Intermodal vehicles may be loaded with items and an aerial vehicle, and directed to travel to areas where demand for the items is known or anticipated. The intermodal vehicles may be coupled to locomotives, container ships, road tractors or other vehicles, and equipped with systems for loading one or more items onto the aerial vehicle, and for launching or retrieving the aerial vehicle while the intermodal vehicles are in motion. The areas where the demand is known or anticipated may be identified on any basis, including but not limited to past histories of purchases or deliveries to such areas, or events that are scheduled to occur in such areas. Additionally, intermodal vehicles may be loaded with replacement parts and/or inspection equipment, and configured to conduct repairs, servicing operations or inspections on aerial vehicles within the intermodal vehicles, while the intermodal vehicles are in motion.

Vehicles, components, and methods are disclosed for preparing hot food during delivery or at a remote location. A multi-modal food distribution system may operate in one or more various modes, including a constellation mode, a cook enroute mode, and a pop-up kitchen mode, to deliver hot, prepared food to customers. The vehicles in the system may be configurable to change between each of the different modes depending upon information received by the system. The system may in the constellation mode include additional delivery vehicles that retrieve food from a vehicle that serves as a hub. The additional delivery vehicles may deliver the food to the delivery destination. In the cook enroute mode, the vehicle may prepare and cook food enroute to a delivery destination. In a pop-up kitchen mode, the vehicle may prepare food for pick up by customers.

Systems and methods for identifying market share and sales potential are provided. Data is retrieved from databases with sales revenue potential and forecasted sales revenue of a company. Retrieved data is categorized to reflect user input indicating a type of market share analysis to be performed. The categorized data is provided at a graphical representation comprising a bar for each category. A first portion of the bar reflects a total value of the potential sales revenue and a second portion of the bar reflects the forecasted sales revenue such that relative market share may be quickly assessed.

A geo-analytical program is integrated into a customer relationship management (CRM) solution. Via the geo-analytical program, users are able to define layer configuration settings for a layer for plotting on a map-based GUI. Layer configuration settings for a respective layer comprise an indication of a data object type serving as a base object type for the respective layer. A method involves receiving from a user user-defined configuration settings for a first layer, electronically receiving, at a geospatial computer system, geospatial data for a plurality of connected devices; electronically receiving, at the geo-analytical program from the geospatial computer system, real-time geospatial data for the plurality of connected devices; and utilizing, by the geo-analytical program, the user-defined layer configuration settings for the first layer to plot the first layer on the map-based GUI based on the received real-time geospatial data for the plurality of connected devices.

Embodiments disclosed herein generally related to a system and method for rideshare vehicle routing. A computing system receives, from one or more facilities, one or more transaction requests associated with one or more accounts of an organization associated with the computing system. The computing system maps one or more customers to a respective transaction request. For each facility of the one or more facilities, the computing device identifies a geographic location thereof. The computing system categorizes each of the one or more facilities into one or more boundaries. For each boundary, the computing system determines an estimated number of rideshare vehicles to deploy, based at least on a transaction history of each customer of the one or more customers. The computing system transmits the estimated number of rideshare vehicles to be deployed to each boundary to a rideshare computing system.

The present disclosure relates to systems and methods for managing and routing ridcsharing vehicles. In some implementations, the systems and methods may count the number of passengers entering a ridcsharing vehicle, distribute vehicles in need of charge to charging stations based on predicted future demand, manage a fleet of petrol and electric ridcsharing vehicles, route autonomous and non-autonomous vehicles, automatically adjust drop-off locations based on safety constraints, and preschedule a ridcsharc with an unknown pick-up location.

A ridesharing vehicle, comprising a vehicle body; a communications interface within the vehicle body for wirelessly communicating with a remote server configured to electronically receive shared-ride requests from a plurality of users; at least one sensor associated with the vehicle body and configured to detect entry of passengers from the ridesharing vehicle; at least one processor within the vehicle body, the at least one processor being programmed to receive, via the communications interface, information about passengers to be picked up, the received information including a pick-up location and a scheduled number of passengers expected to be picked up at the pick-up location; after arriving at the pick-up location, receive from the at least one sensor a number of passengers actually picked up at the pick-up location; compare the actual number of passengers picked up at the pick-up location with the scheduled number of passengers; and initiate a remedial action when a difference exists between the scheduled number of passengers and the actual number of passengers as detected by the at least one sensor.

A system for directing an electric vehicle to a charging station, the system comprising memory for storing historical data associated with past demand for ridesharing vehicles in a geographical area and locations of a plurality of charging stations in the geographical area; a communication interface for communicating with a fleet of ridesharing vehicles including a plurality of electric vehicles; at least one processor programmed to access the memory and to receive, via the communications interface, current battery-charge data for the plurality of electric vehicles, wherein the current battery-charge data is reflective of a distance that each electric vehicle can operate before recharging; identify, from the current battery-charge data, a specific electric vehicle traveling in the geographic area and in need of a charge; receive current vehicle location data for the specific electric vehicle, wherein the current vehicle location data includes global positioning system (GPS) data generated by at least one GPS component associated with the specific electric vehicle; determine, using the historical data, predicted demand for ridesharing requests in at least one area proximate to at least one of the plurality of charging stations; based on an estimated charging completion time and the predicted demand, select a charging station for the specific electric vehicle, wherein the selected charging station is other than a charging station closest to a current location of the specific electric vehicle; and direct the specific electric vehicle to the selected charging station.

The present disclosure relates to systems and methods for managing and routing ridesharing vehicles. In some implementations, the systems and methods may count the number of passengers entering a ridesharing vehicle, distribute vehicles in need of charge to charging stations based on predicted future demand, manage a fleet of petrol and electric ridesharing vehicles, route autonomous and non-autonomous vehicles, automatically adjust drop-off locations based on safety constraints, and preschedule a rideshare with an unknown pick-up location.