Here Global B.v.

Ron Begleiter

Ofri Rom

Silviu Zilberman

Harel Primack

Daniel Schmidt

Alexander Maryanovsky

Inna Branevich

Marina Ousov Fridin

Yelena Shnaider

Marcelo Savignano

An approach is provided for determining travel direction and/or location data based on sequential semantic events. The approach, for example, involves processing sensor data collected from at least one sensor of a mobile device to determine a set of observed time-sequenced semantic events. The semantic events are associated with traveling within a transportation system, and at least one semantic event of the set of observed time-sequenced semantic events is based on a turn angle value determined from the sensor data. The approach also involves initiating a comparison of the set of observed time-sequenced semantic events against a set of known time-sequenced semantic events and/or a known piece of information associated with the transportation system. The approach further involves determining a direction of travel and/or a location of the mobile device within the transportation system based on the comparison. The approach further involves providing the direction of travel and/or the location as an output.

Method, apparatus, and system for providing travel direction and navigation in a multi-one-dimensional transportation system

An approach is provided for in-parking navigation based on mobile device sensor data, the resulted semantic events, and an estimated parking occupancy level. The approach, for example, involves receiving parking information from in-parking navigation system(s) of vehicle(s) and/or mobile device(s) associated with the vehicle(s) traveling in a parking facility with no or a positioning satellite coverage below a threshold. The parking information indicates parking spot location(s) occupied by the vehicle(s). The in-parking navigation system(s) determines the at least one parking spot location based on tracking multi-modal trajectories of the mobile device(s). The multi-modal trajectories comprise vehicle trajectory segment(s) traveling within the parking facility and pedestrian trajectory segment(s) traveling to/from pedestrian entry or exit point(s) of the parking facility. The approach also involves determining an occupancy level of the parking facility based on the parking information. The approach further involves providing the occupancy level of the parking facility as an output.

Method, apparatus, and system for providing in-parking navigation

A system, a method and a computer program product are provided for determining building type of one or more buildings in a geographic region, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain a plurality of mobility features associated with the one or more buildings. The processor may be configured to determine, using a trained machine learning model, one or more transport modes for the one or more buildings, based on the plurality of mobility features. The processor may be further configured to determine, using the trained machine learning model, the building type of the one or more buildings based on the determined one or more transport modes.

Automatic building detection and classification using elevator/escalator stairs modeling&#x2014;building classification

An approach is provided for determining vehicle maneuver events. The approach, for example, involves determining sensor data from a sensor of a device associated with a vehicle. The sensor data includes a maneuver parameter represented using a device frame of reference. The approach also involves transforming the maneuver parameter from the device frame of reference to an Earth frame of reference. The approach further involves automatically detecting a maneuver event of the vehicle based on the maneuver parameter as transformed to the Earth frame of reference. The approach further involves providing the detected maneuver event to a signaling unit associated with the vehicle.

Method, apparatus, and system for providing automatic alerts for transportation maneuver events

A system, a method and a computer program product are provided to determine user profile of one or more users in one or more buildings, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain mobility features associated with the one or more buildings in a geographic region. The processor may be configured to determine using a trained machine learning model, one or more transport modes for the one or more buildings. The processor may be further configured to obtain indoor mobility data of the one or more users based on the one or more transport modes for the one or more buildings. The processor may be further configured determine the user profile of the one or more users in the one or more buildings.

Automatic building detection and classification using elevator/escalator/stairs modeling-user profiling

An approach is provided for discovery of semantic nodes for map-based dynamic location sampling. The approach, for instance, involves identifying a semantic node occurring on a road segment. The semantic node is a point of interest on or near the road segment that is different from beginning node and end node of the road segment. The approach also involves calculating an estimated time of arrival at the semantic node based on historical traversal time data for the road segment. The approach further involves determining a sampling rate for a location sensor of vehicle traveling the road segment based on the estimated time of arrival. The approach further involves configuring the location sensor to collect location data using the sampling rate.

Method and apparatus for discovery of semantic nodes for map-based dynamic location sampling

A system, a method and a computer program product are provided to determine population distribution of users associated with one or more buildings in a geographic region, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain mobility features associated with the one or more buildings in the geographic region. The processor may be configured to determine using a trained machine learning model, one or more transport modes for the one or more buildings, based on the mobility features. The processor may be further configured to determine, using the trained machine learning model, the population distribution of the users associated with the one or more buildings in the geographic region at a fixed epoch based on the determined one or more transport modes.

Automatic building detection and classification using elevator/escalator stairs modeling&#x2014;smart cities

A system, a method and a computer program product are provided for determining one or more transport modes for one or more buildings in a geographic region, using a machine learning model. The system may include at least one memory configured to store computer executable instructions and at least one processor configured to execute the computer executable instructions to obtain sensor data associated with the one or more buildings for time duration. The processor may be configured to determine mobility features associated with the one or more buildings, based on the sensor data. The processor may be further configured to determine, using a trained machine learning model, the one or more transport modes for the one or more buildings, based on the mobility features.

Automatic building detection and classification using elevator/escalator stairs modeling

An approach is provided for recommending services based on map-based dynamic location sampling. The approach, for instance, involves determining a predicted location of a vehicle on a road segment based on an estimated time of arrival at an end node of the road segment. The approach also involves calculating a difference between the predicted location and an actual location sensed by a location of the vehicle. The approach further involves recommending a service for installation on or near the road segment based on the difference.

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Tel Aviv, Israel

Ron Begleiter