Method and apparatus for detecting pedestrian mode from probe data

Patent metadata

Field	Value
Publication number	US-9911326-B2
Application number	US-201514674839-A
Country	US
Kind code	B2
Filing date	Mar 31, 2015
Priority date	Mar 31, 2015
Publication date	Mar 6, 2018
Grant date	Mar 6, 2018

Abstract

An approach is provided for processing and/or facilitating a processing of probe trace data to determine one or more mode indicators, wherein the one or more mode indicators include, at least in part, one or more attributes of the probe trace data. The approach involves causing, at least in part, a modeling of one or more statistical patterns of at least one pedestrian mode of transport, at least one non-pedestrian mode of transport, or a combination thereof based, at least in part, on determining one or more probabilities that one or more mode indicators are associated with the at least one pedestrian mode of transport, the at least one non-pedestrian mode of transport, or a combination thereof. The approach also involves causing, at least in part, a classification of other probe trace data as being associated with the at least one pedestrian mode of transport or the at least one non-pedestrian mode of transport based, at least in part, on the one or more mode indicators that are associated with the other probe trace data and the one or more statistical patterns.

First claim

What is claimed is:

1. A method for developing an enhanced certainty in detecting a mode associated with probe data, the method comprising:
receiving, utilizing at least one interface, probe trace data associated with one or more physical probe sensors;
processing, utilizing at least one processor controlling a memory, probe trace data by one or more physical probe sensors which follow a travel path of a mode of transport to determine one or more mode indicators,
wherein the one or more mode indicators include, at least in part, one or more attributes of the probe trace data;

modeling one or more statistical patterns of at least one pedestrian mode of transport, at least one non-pedestrian mode of transport, or a combination thereof based, at least in part, on determining one or more probabilities that the determined one or more mode indicators are associated with the at least one pedestrian mode of transport, the at least one non-pedestrian mode of transport, or a combination thereof;
categorizing the probe trace data as clear pedestrian probe trace data, clear non-pedestrian probe trace data or ambiguous probe trace data based, at least in part, on the modeled one or more statistical patterns;
classifying at least some part of the categorized probe trace data as being associated with the at least one pedestrian mode of transport or the at least one non-pedestrian mode of transport based, at least in part, on the one or more mode indicators that are associated with the other probe trace data and the modeled one or more statistical patterns,
wherein the categorized probe trace data includes clear pedestrian probe trace data, clear non-pedestrian probe trace data, and ambiguous probe trace data,
wherein the modeling of the one or more statistical patterns is configured to increase certainty of the classified mode of transportation; and

transmitting, utilizing the at least one interface, at least some part of the association of the classified categorized probe trace data with the at least one pedestrian mode of transport or the at least one non-pedestrian mode of transport for developing the solution associated with transportation and/or geography.

2. A method of claim 1 , wherein the one or more mode indicators include, at least in part, speed information, speed change rate information, heading information, heading change rate information, stop rate information, information regarding closeness to one or more road links, or a combination thereof.

3. A method of claim 1 , wherein the modeling of the one or more statistical patterns for the at least one pedestrian mode of transport is trained based, at least in part, on the clear pedestrian probe trace data.

4. A method of claim 1 , wherein the modeling of the one or more statistical patterns for the at least one non-pedestrian mode of transport is trained based, at least in part, on the clear non-pedestrian probe data.

5. A method of claim 1 , wherein the other probe data includes, at least in part, the ambiguous probe trace data.

6. A method of claim 1 , further comprising:
categorizing the probe trace data as the clear pedestrian probe trace data, the clear non-pedestrian probe trace data, or the ambiguous probe trace data based, at least in part, on a speed of travel, a location in a spatial domain, a smartphone reporting activity, one or more path characteristics, or a combination thereof.

7. A method of claim 1 , further comprising:
categorizing the probe trace data as the clear pedestrian probe trace data based, at least in part, on a determination that the probe trace data is associated with at least one of:
originating from one or more pedestrian zones;
traveling a wrong direction on a one-way street; and
originating from a street that is closed to non-pedestrian traffic.

8. A method of claim 1 , further comprising:
categorizing the probe trace data as the clear non-pedestrian probe trace data based, at least in part, on a determination that the probe trace data is associated with at least one of:
traveling at a non-pedestrian speed;
originating from one or more fleets; and
originating from a street with no pedestrian paths.

9. A method of claim 1 , wherein the probe trace data includes a sequence of a plurality of location data points, and wherein the plurality of location data points indicate, at least in part, that a probe is at a location with a speed and a heading at a time.

10. An apparatus for developing an enhanced certainty in detecting a mode associated with probe data, the 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 controlling the memory, cause the apparatus to perform at least the following:
receiving, utilizing at least one interface, probe trace data associated with one or more physical probe sensors;
processing probe trace data by one or more physical probe sensors which follow a travel path of a mode of transport to determine one or more mode indicators,
wherein the one or more mode indicators include, at least in part, one or more attributes of the probe trace data;

modeling one or more statistical patterns of at least one pedestrian mode of transport, at least one non-pedestrian mode of transport, or a combination thereof based, at least in part, on determining one or more probabilities that the determined one or more mode indicators are associated with the at least one pedestrian mode of transport, the at least one non-pedestrian mode of transport, or a combination thereof;
categorizing the probe trace data as clear pedestrian probe trace data, clear non-pedestrian probe trace data or ambiguous probe trace data based, at least in part, on the modeled one or more statistical patterns;
classifying at least some part of the categorized probe trace data as being associated with the at least one pedestrian mode of transport or the at least one non-pedestrian mode of transport based, at least in part, on the one or more mode indicators that are associated with the other probe trace data and the modeled one or more statistical patterns,
wherein the categorized probe trace data includes clear pedestrian probe trace data, clear non-pedestrian probe trace data, and ambiguous probe trace data,
wherein the modeling of the one or more statistical patterns is configured to increase certainty of the classified mode of transportation; and

transmitting, utilizing the at least one interface, at least some part of the association of the classified categorized probe trace data with the at least one pedestrian mode of transport or the at least one non-pedestrian mode of transport for developing the solution associated with transportation and/or geography.

11. An apparatus of claim 10 , wherein the one or more mode indicators include, at least in part, speed information, speed change rate information, heading information, heading change rate information, stop rate information, information regarding closeness to one or more road links, or a combination thereof.

12. An apparatus of claim 10 , wherein the modeling of the one or more statistical patterns for the at least one pedestrian mode of transport is trained based, at least in part, on the clear pedestrian probe trace data.

13. An apparatus of claim 10 , wherein the modeling of the one or more statistical patterns for the at least one non-pedestrian mode of transport is trained based, at least in part, on the clear non-pedestrian probe data.

14. An apparatus of claim 10 , wherein the other probe data includes,

Assignees

• Here Global Bv

Inventors

• Xu Bo
• Macfarlane Jane

Classifications

• G08G1/056

  with provision for distinguishing direction of travel · CPC title

• H04W4/44

  for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H] · CPC title

• G08G1/052

  with provision for determining speed or overspeed {(speed measuring in general G01P)} · CPC title

• H04W4/029

  Location-based management or tracking services · CPC title

• G08G1/0133 Primary

  for classifying traffic situation · CPC title