Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a machine-learned classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a machine-learned classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a machine-learned classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a neural network classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: In an embodiment, a data processing method providing an improvement in filtering telemetry data received from mobile devices comprises receiving, from a mobile device, telemetry data comprising a plurality of probes, each probe identifying a location of the mobile device at a time of the probe, wherein the plurality of probes form first digital trace data identifying a path of the mobile device; determining, for a particular probe of the plurality of probes, a first attribute of the particular probe; determining whether the first attribute of the particular probe satisfies a first threshold; in response to satisfying the first threshold, deleting the particular probe from the plurality of probes and dividing the first digital trace data into second digital trace data and third digital trace data; performing an action using the second digital trace data and the third digital trace data.

Abstract: A method for providing an improvement in speed estimation for route planning involves identifying a plurality of observed speeds for a segment of road during a first time period, the plurality of observed speeds corresponding to the plurality of traces of the segment of road. The method further involves identifying a plurality of discrete values in the plurality of observed speeds, and determining a count for each of the plurality of discrete values. The method further involves aggregating the counts for the plurality of traces including the segment to create aggregated counts for the segment, and determining the speed estimate from the aggregated counts for the plurality of traces including the segment.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics is calculated from the sets of values corresponding to the metrics, and the set of derived values is input to a neural network classification model to determine whether the received digital data represents movement of a first type. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics are calculated from the sets of values corresponding to the metrics, and the set of derived values are compared to a binary classification model to determine whether the received digital data represents movement of a first type. The binary classification model was trained using a first set of traces representing the first type of movement and a second set of traces. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes a set of values for each of a plurality of metrics. A set of derived values for the plurality of metrics are calculated from the sets of values corresponding to the metrics, and the set of derived values are compared to a machine learning model trained using a plurality of travel modes. A plurality of confidence scores for the derived values are calculated. The plurality of confidence scores correspond to the plurality of travel modes. In response to a confidence score for a first travel mode of the plurality of confidence scores for the plurality of travel modes meeting a threshold value for the first travel mode, the received digital data corresponding to the trace is labelled with the first travel mode.

Abstract: In an embodiment, a data processing method providing an improvement in filtering telemetry data received from mobile devices comprises receiving, from a mobile device, telemetry data comprising a plurality of probes, each probe identifying a location of the mobile device at a time of the probe, wherein the plurality of probes form first digital trace data identifying a path of the mobile device; determining, for a particular probe of the plurality of probes, a first attribute of the particular probe; determining whether the first attribute of the particular probe satisfies a first threshold; in response to satisfying the first threshold, deleting the particular probe from the plurality of probes and dividing the first digital trace data into second digital trace data and third digital trace data; performing an action using the second digital trace data and the third digital trace data

Abstract: A method for providing an improvement in speed estimation for route planning involves identifying a plurality of observed speeds for a segment of road during a first time period, the plurality of observed speeds corresponding to the plurality of traces of the segment of road. The method further involves identifying a plurality of discrete values in the plurality of observed speeds, and determining a count for each of the plurality of discrete values.

Abstract: In an embodiment, a data processing method providing an improvement in filtering telemetry data received from mobile devices comprises receiving, from a mobile device, telemetry data comprising a plurality of probes, each probe identifying a location of the mobile device at a time of the probe, wherein the plurality of probes form first digital trace data identifying a path of the mobile device; determining, for a particular probe of the plurality of probes, a first attribute of the particular probe; determining whether the first attribute of the particular probe satisfies a first threshold; in response to satisfying the first threshold, deleting the particular probe from the plurality of probes and dividing the first digital trace data into second digital trace data and third digital trace data; performing an action using the second digital trace data and the third digital trace data.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes a set of values for each of a plurality of metrics. A set of derived values for the plurality of metrics are calculated from the sets of values corresponding to the metrics, and the set of derived values are compared to a machine learning model trained using a plurality of travel modes. A plurality of confidence scores for the derived values are calculated. The plurality of confidence scores correspond to the plurality of travel modes. In response to a confidence score for a first travel mode of the plurality of confidence scores for the plurality of travel modes meeting a threshold value for the first travel mode, the received digital data corresponding to the trace is labelled with the first travel mode.

Abstract: Digital data corresponding to a trace by a device is received. The digital data includes location data and time data obtained over a period of time and representing movement of the device. A set of derived values for the plurality of metrics are calculated from the sets of values corresponding to the metrics, and the set of derived values are compared to a binary classification model to determine whether the received digital data represents movement of a first type. The binary classification model was trained using a first set of traces representing the first type of movement and a second set of traces. In response to determining that the digital data represents movement of the first type, the received digital data corresponding to the trace is labelled with the first type of movement.

Abstract: In an embodiment, a data processing method providing an improvement in filtering telemetry data received from mobile devices comprises receiving, from a mobile device, telemetry data comprising a plurality of probes, each probe identifying a location of the mobile device at a time of the probe, wherein the plurality of probes form first digital trace data identifying a path of the mobile device; determining, for a particular probe of the plurality of probes, a first attribute of the particular probe; determining whether the first attribute of the particular probe satisfies a first threshold; in response to satisfying the first threshold, deleting the particular probe from the plurality of probes and dividing the first digital trace data into second digital trace data and third digital trace data; performing an action using the second digital trace data and the third digital trace data