IP Library › Granted Patent US 12704386
Granted Patent B2
US 12704386 · App. 18/392,893 · Granted Aug 11, 2026

Method, apparatus, and system of providing zone-to-zone trip reliability analysis using probe data

Inventors: Jingwei Xu (Buffalo Grove, IL); Weimin Huang (Chicago, IL); Yuxin Guan (Chicago, IL); Bruce Bernhardt (Wauconda, IL)
Assignee: Here Global B.V.
G01C21/3841G01C21/3476
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Quick Facts
Patent No.
US 12704386
App. No.
18/392,893
Granted
Aug 11, 2026
Kind
B2
Abstract

An approach is provided for zone-to-zone trip reliability. The approach involves, for example, determining probe data collected from a plurality of sensors of a plurality of probe vehicles traveling in a geographic area of interest. The approach also involves processing the probe data to determine a plurality of vehicle trips. The approach further involves clustering the plurality of vehicle trips into one or more zone-to-zone trip categories based on the origin zones and destination zones of the plurality of vehicle trips. The approach further involves, for each zone-to-zone trip category of the one or more zone-to-zone trip categories, computing a zone-to-zone trip category reliability metric based on the plurality of vehicle trips clustered into each zone-to-zone trip category. The approach further involves providing the zone-to-zone trip category reliability metric for each zone-to-zone trip category as an output.

Claims (49)

1 . A method comprising:

determining probe data collected from a plurality of sensors of a plurality of probe vehicles traveling in a geographic area of interest;

processing the probe data to determine a plurality of vehicle trips;

clustering the plurality of vehicle trips into one or more zone-to-zone trip categories based on origin zones and destination zones of the plurality of vehicle trips;

for each zone-to-zone trip category of the one or more zone-to-zone trip categories, computing, using a parallelized compute infrastructure, a zone-to-zone trip category reliability metric based on the plurality of vehicle trips clustered into each zone-to-zone trip category, wherein the parallelized compute infrastructure partitions the plurality of vehicle trips into distinct data partitions corresponding to the distinct zones and applies compute resources to concurrently process the distinct data partitions of the plurality of vehicle trips, and wherein the reliability metric comprises a statistical measure of a variability of zone-to-zone travel times for the plurality of vehicle trips clustered into each zone-to-zone trip category; and

providing the zone-to-zone trip category reliability metric for each zone-to-zone trip category as an output.

2 . The method of claim 1 , further comprising:

processing trip data associated with the plurality of vehicle trips in each zone-to-zone trip category to determine one or more statistical parameters,

wherein the zone-to-zone trip category reliability metric is computed based on the one or more statistical parameters.

3 . The method of claim 2 , wherein the one or more statistical parameters include a mean, median, one or more percentiles, or a combination thereof.

4 . The method of claim 2 , wherein the trip data includes a zone-to-zone travel time.

5 . The method of claim 2 , wherein the trip data includes a trips speed distribution, a trips volume for each origin zone and destination zone pair, or a combination thereof.

6 . The method of claim 1 , further comprising:

map matching the plurality of vehicle trips to map data of a geographic database, wherein the origin zones and the destination zones are determined based on the map matching.

7 . The method of claim 1 , further comprising:

determining one or more contextual attributes of the plurality of vehicle trips,

wherein the zone-to-zone trip category reliability metric is determined with respect to the one or more contextual attributes.

8 . The method of claim 7 , wherein the one or more contextual attributes include a time epoch, an environmental condition, a vehicle attribute, or a combination thereof.

9 . The method of claim 7 , wherein the output is provided based on the one or more contextual attributes.

10 . The method of claim 1 , wherein the origin zones, the destination zones, or a combination thereof are predefined.

11 . The method of claim 1 , wherein the origin zones, the destination zones, or a combination thereof are dynamically adjusted based on the probe data, the plurality of trips, or a combination thereof.

12 . The method of claim 1 , further comprising:

automatically controlling an operation of an autonomous vehicle based on the zone-to-zone category reliability metric.

13 . The method of claim 1 , further comprising:

providing the output as an input to a routing engine to compute an estimated time of arrival based on the zone-to-zone trip category reliability metric.

14 . The method of claim 1 , wherein the probe data is collected in real-time, and wherein the zone-to-zone trip reliability metric is dynamically updated in real-time.

15 . 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,

determine probe data collected from a plurality of sensors of a plurality of probe vehicles traveling in a geographic area of interest;

process the probe data to determine a plurality of vehicle trips;

cluster the plurality of vehicle trips into one or more zone-to-zone trip categories based on origin zones and destination zones of the plurality of vehicle trips;

for each zone-to-zone trip category of the one or more zone-to-zone trip categories, compute, using a parallelized compute infrastructure, a zone-to-zone trip category reliability metric based on the plurality of vehicle trips clustered into each zone-to-zone trip category, wherein the parallelized compute infrastructure partitions the plurality of vehicle trips into distinct data partitions corresponding to the distinct zones and applies compute resources to concurrently process the distinct data partitions of the plurality of vehicle trips, and wherein the reliability metric comprises a statistical measure of a variability of zone-to-zone travel times for the plurality of vehicle trips clustered into each zone-to-zone trip category; and

provide the zone-to-zone trip category reliability metric for each zone-to-zone trip category as an output.

16 . The apparatus of claim 15 , wherein the apparatus is further caused to:

process trip data associated with the plurality of vehicle trips in each zone-to-zone trip category to determine one or more statistical parameters,

wherein the zone-to-zone trip category reliability metric is computed based on the one or more statistical parameters.

17 . The apparatus of claim 16 , wherein the one or more statistical parameters include a mean, median, one or more percentiles, or a combination thereof.

18 . A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

determining probe data collected from a plurality of sensors of a plurality of probe vehicles traveling in a geographic area of interest;

processing the probe data to determine a plurality of vehicle trips;

clustering the plurality of vehicle trips into one or more zone-to-zone trip categories based on origin zones and destination zones of the plurality of vehicle trips;

for each zone-to-zone trip category of the one or more zone-to-zone trip categories, computing, using a parallelized compute infrastructure, a zone-to-zone trip category reliability metric based on the plurality of vehicle trips clustered into each zone-to-zone trip category, wherein the parallelized compute infrastructure partitions the plurality of vehicle trips into distinct data partitions corresponding to the distinct zones and applies compute resources to concurrently process the distinct data partitions of the plurality of vehicle trips, and wherein the reliability metric comprises a statistical measure of a variability of zone-to-zone travel times for the plurality of vehicle trips clustered into each zone-to-zone trip category; and

providing the zone-to-zone trip category reliability metric for each zone-to-zone trip category as an output.

19 . The non-transitory computer-readable storage medium of claim 18 , wherein the apparatus is caused to further perform:

processing trip data associated with the plurality of vehicle trips in each zone-to-zone trip category to determine one or more statistical parameters,

wherein the zone-to-zone trip category reliability metric is computed based on the one or more statistical parameters.

20 . The non-transitory computer-readable storage medium of claim 19 , wherein the one or more statistical parameters include a mean, median, one or more percentiles, or a combination thereof.