# US 10,373,494 B1 · Method and apparatus for estimating a parking event based on device signal observations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,373,494 B1 - Document ID: US10373494B1 - Status: Granted - Kind: B1 - Application number: 15/975,415 - Filed: 2018-05-09 - Granted: 2019-08-06 - Earliest claimed: 2018-05-09 - Assignees: HERE Global B.V. - Applicants: Here Global B.V. - Inventors: Marek Krysiuk - Web page: https://patentgrep.com/patent/US10373494B1 ## CPC - G08G 1/144: {on portable or mobile units, e.g. personal digital assistant [PDA]} - G01C 21/36: Input/output arrangements for on-board computers - G01C 21/3685: {the POI's being parking facilities} - G06Q 10/00: Administration; Management - G08G 1/147: {where the parking area is within an open public zone, e.g. city centre} - G08G 1/148: {Management of a network of parking areas} - H04W 4/023: {using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds} - H04W 4/029: Location-based management or tracking services - H04W 8/22: Processing or transfer of terminal data, e.g. status or physical capabilities - H04W 24/10: Scheduling measurement reports {; Arrangements for measurement reports} - H04W 64/006: {with additional information processing, e.g. for direction or speed determination} - H04W 4/40: for vehicles, e.g. vehicle-to-pedestrians [V2P] - H04W 64/003: {locating network equipment} - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract An approach is provided for estimating parking or other mobility events. The approach, for instance, involves receiving a signal strength profile of a user device with respect to a location-fixed communication equipment (e.g., a base station). The signal strength profile comprises signal strength data for the user device measured over a time series by the location-fixed communication equipment. The approach also involves processing the signal strength profile to determine a signal strength change over the time series. The approach further involves identifying a parking event (e.g., a park-in event or a park-out event) or other mobility event of a vehicle occurring in a geographic area covered by the location-fixed communication equipment based on the signal strength change. ## Claims ### Claim 1 A computer-implemented method comprising: receiving a signal strength profile of a user device with respect to a location-fixed communication equipment, wherein the signal strength profile comprises signal strength data for the user device measured over a time series by the location-fixed communication equipment; processing the signal strength profile to determine a signal strength change over the time series; and identifying a park-in event or a park-out event of a vehicle occurring in a geographic area covered by the location-fixed communication equipment based on the signal strength change. ### Claim 2 (depends on 1) The method of claim 1, wherein the park-in event is identified based on determining that the signal strength change is a signal strength increase, and wherein the park-out event is identified based on determining that the signal strength change is a signal strength decrease. ### Claim 3 (depends on 1) The method of claim 1, wherein the location-fixed communication equipment is a base station, a wireless access point, or a combination thereof. ### Claim 4 (depends on 1) The method of claim 1, further comprising: initiating additional processing of the signal strength profile backwards in time based on determining that signal strength change, wherein the identifying of the park-in event or the park-out event is further based on the additional processing of the signal strength profile backwards in time. ### Claim 5 (depends on 4) The method of claim 4, wherein the additional processing of the signal strength profile comprises determining whether a calculated speed of the user device is within a speed range associated with driving the vehicle, and wherein the calculated speed is determined based on a handover of the user device between the location-fixed communication equipment and one or more neighboring location-fixed communication equipment. ### Claim 6 (depends on 1) The method of claim 1, further comprising: processing the signal strength profile to determine a dwell time of the user device in the geographic area covered by the location-fixed equipment, wherein the identifying of the park-in event or the park-out event is further based on the dwell time. ### Claim 7 (depends on 1) The method of claim 1, further comprising: processing probe data collected from one or more fleet vehicles in the geographic area covered by the location-fixed communication equipment to determine that the vehicle is a fleet vehicle; and filtering the park-in or the park-out event identified for the fleet vehicle. ### Claim 8 (depends on 1) The method of claim 1, further comprising: processing public transport scheduling data to determine that the vehicle is a public transport vehicle; and filtering the park-in or the park-out even identified for the public transport vehicle. ### Claim 9 (depends on 1) The method of claim 1, further comprising: aggregating the signal strength change with one or more other signal strength changes associated with one or more other devices based on determining that the one or more other signal strength changes occur within a threshold time period, a threshold distance, or a combination thereof of the signal strength change. ### Claim 10 (depends on 1) The method of claim 1, wherein the processing of the signal strength profile comprises: creating a feature set of the signal strength profile, the signal strength change, the user device, the location-fixed communication equipment, or a combination thereof; and processing the feature set using a trained machine learning model to identify the park-in event or the park-out event. ### Claim 11 (depends on 1) The method of claim 1, wherein the signal strength profile indicates the user device being inside or outside of the vehicle for the identification of the park-in event or the park-out event. ### Claim 12 An apparatus comprising: at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, receive a signal strength profile of a user device with respect to a location-fixed communication equipment, wherein the signal strength profile comprises signal strength data for the user device measured over a time series by the location-fixed communication equipment; process the signal strength profile to determine a signal event over the time series; and identifying a mobility event with respect to a mode of transportation occurring in a geographic area covered by the location-fixed communication equipment based on the signal event. ### Claim 13 (depends on 12) The apparatus of claim 12, wherein the apparatus is further caused to: calculate a speed associated with user device based on a handover of the user device to the location-fixed communication from one or more neighboring communication equipment, wherein the mobility event, the mode of transportation, or a combination thereof is further based on the calculated speed. ### Claim 14 (depends on 12) The apparatus of claim 12, wherein the mobility event is a transition from a non-vehicle based mode of transportation to a vehicle-based mode of transportation. ### Claim 15 (depends on 14) The apparatus of claim 14, wherein the apparatus is further caused to: identify a park-in event or a park-out event associated with a vehicle in the geographic area covered by the location-fixed communication equipment based on the transition. ### Claim 16 (depends on 12) The apparatus of claim 12, wherein the signal event is a signal strength fluctuation that exceeds a threshold value. ### Claim 17 A non-transitory computer-readable storage medium for generating parking occupancy data using a machine learning model, carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform the following steps: receiving signal strength data for a plurality of user devices located with a geographic area covered by a location-fixed communication equipment providing the signal strength data; processing the signal strength data to determine one or more signal fluctuations; and identifying one or more park-in events, one or more park-out events, or a combination thereof occurring in the geographic area based on the one or more signal fluctuations. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the apparatus is caused to further perform: filtering one or more false positive events from the one or more park-in events, the one or more park-out events, or combination thereof based a characteristic determined from the signal strength data, an external data source, or a combination thereof. ### Claim 19 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the characteristic includes a dwell time within the geographic area covered by the location fixed communication equipment, and wherein the filtering of the one or more false positive events is based on determining that the dwell time is not within a dwell time range associated with a vehicle-based mode of transportation, determining that the dwell time is within a range associated with entry into a building, or a combination thereof. ### Claim 20 (depends on 18) The non-transitory computer-readable storage medium of claim 18, wherein the external data source includes fleet vehicle probe data, public transport scheduling data, or combination thereof; and wherein the filtering of the one or more false positive events is based on matching the one or more park-in events, the one or more park-out events, or a combination thereof against the fleet vehicle probe data, the public transport scheduling data, or a combination thereof.