# US 2021/0343142 A1 · Method for determining traffic metrics of a road network

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- Number: US 2021/0343142 A1
- Document ID: US20210343142A1
- Status: Published application
- Kind: A1
- Application number: 17/368,171
- Filed: 2021-07-06
- Published: 2021-11-04
- Earliest claimed: 2019-04-04
- Assignees: Geotab Inc.
- Applicants: Geotab Inc.
- Inventors: Daniel J. Lewis; Robert Bradley
- Web page: https://patentgrep.com/patent/US20210343142A1

## CPC

- G06N 20/00: Machine learning
- G01C 21/3822: {Road feature data, e.g. slope data}
- G01S 19/01: Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G06F 18/2321: using statistics or function optimisation, e.g. modelling of probability density functions
- G06V 20/54: of traffic, e.g. cars on the road, trains or boats
- G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle
- G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)}
- G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)}
- G07C 5/085: {using electronic data carriers}
- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- G08G 1/0125: {Traffic data processing}
- G08G 1/0129: {for creating historical data or processing based on historical data}
- G08G 1/0133: {for classifying traffic situation}
- G08G 1/0141: {for traffic information dissemination}
- G08G 1/20: {Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles}
- H04W 4/021: Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
- H04W 4/022: {with dynamic range variability}
- H04W 4/029: Location-based management or tracking services
- H04W 4/44: for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- 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)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- 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})
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Disclosed are systems and methods relating to providing intersection metrics based on telematic data.

## Claims

### Claim 1

A method for providing traffic metrics for a first intersection of a road network comprising:
providing first road network data indicating a first plurality of road network subzones defining an area occupied by the road network, the road network including a plurality of intersections;
processing first road network data for labelling each road network subzone as one of a core subzone and non-core subzone for forming a first plurality of core subzones and a first plurality of non-core subzones, respectively, and storing an indication thereof in the first road network data;
mapping each of the first plurality of core subzones to an intersection of the plurality of intersections of the road network and storing an indication thereof in the first road network data, the first plurality of core subzones including a plurality;
forming a plurality of subsets of core subzones of the first plurality of core subzones, each thereof defining a geographic area occupied by an intersection core of an intersection of the plurality of intersections of the road network;
processing first road network data for mapping each non-core subzone of the first plurality of non-core subzones to at least an intersection of the plurality of intersections of the road network;
forming second road network data including the first road network data and an indication of the at least an intersection of the plurality of intersections of the road network each non-core subzone of the first plurality of non-core subzones is mapped
forming trip metadata dependent on second road network data and third vehicle data corresponding to the first plurality of road network subzones; and
processing trip metadata associated with the first intersection for forming traffic metrics data indicative of intersection metrics for the first intersection.

### Claim 2 (depends on 1)

The method of claim 1 wherein processing first road network data for mapping each non-core subzone of the first plurality of non-core subzones to at least an intersection of the plurality of intersections of the road network includes,
for each road network subzone of the first plurality of road network subzones, processing road network data for forming representative point data indicating representative points representing the location of the road network subzone,
for each intersection of the plurality of intersections, processing representative point data of a corresponding intersection core and representative point data of the first plurality of non-core subzones for clustering corresponding representative points into groups, and
for each representative point of a non-core subzone of the first plurality of non-core subzones grouped in a same group as representative points of an intersection core, mapping the non-core subzone to the corresponding intersection of the intersection core.

### Claim 3 (depends on 2)

The method of claim 2 wherein, processing representative point data of a corresponding intersection core and representative point data of the first plurality of non-core subzones for clustering corresponding representative points into groups includes, for at least a road network subzone, processing road network data for determining a centre point of the road network subzone.

### Claim 4 (depends on 2)

The method of claim 2 wherein processing representative point data of a corresponding intersection core and representative point data of the first plurality of non-core subzones for clustering corresponding representative points into groups includes clustering the corresponding representative points into groups using a spatial clustering algorithm.

### Claim 5 (depends on 4)

The method of claim 4 wherein the spatial clustering algorithm includes density-based spatial clustering of applications with noise spatial clustering algorithm.

### Claim 6 (depends on 1)

The method of claim 1 wherein forming trip metadata dependent on second road network data and third vehicle data corresponding to the first plurality of road network subzones includes,
for a plurality of vehicles corresponding to the third vehicle data, selecting at least a first subset of temporally consecutive third vehicle data instances indicating the vehicle transitions from a first undrivable state to a second drivable state to a third undrivable state for forming journey data;
processing journey data and road network data for mapping each instance of journey data to a road network subzone of the first plurality of road network subzones based on the journey data instance corresponding to a road network subzone of the first plurality of road network subzones and storing an indication of the location of the road network subzone, the label of the road network subzone, and intersection mapping of the road network subzone in the journey data;
selecting subsets of journey data instances for forming trip data indicative of vehicle trips and mapping trip data to an intersection of the plurality of intersections of the road network; and
processing each trip data instance of the trip data forming trip metadata.

### Claim 7 (depends on 6)

The method of claim 6 wherein for a plurality of vehicles corresponding to the third vehicle data, selecting at least a first subset of temporally consecutive third vehicle data instances indicating the vehicle transitions from a first undrivable state to a second drivable state to a third undrivable state for forming journey data, includes, for at least a vehicle,
selecting at least a sequence of third vehicle data instances including a third vehicle data instance indicating ignition status of the vehicle is OFF, immediately followed by a third vehicle data instance indicating the ignition status of the vehicle is ON and the vehicle has a speed greater than 0 km/h, immediately followed by one or more third vehicle data instances indicating the vehicle is ON, immediately followed by a third vehicle data instance indicating ignition status of the vehicle is OFF.

### Claim 8 (depends on 6)

The method of claim 6 wherein selecting subsets of journey data instances for forming trip data indicative of vehicle trips includes, selecting at least a first sequence of journey data instances from journey data for forming trip data, the at least a first sequence of journey data instances including at least one journey data instance corresponding to a core subzone that is mapped to a first intersection, and immediately followed by a journey data instance mapped to a second intersection, wherein the second intersection and the first intersection are not the same intersection and mapping each trip data instance to the first intersection and storing an indication of the mapping therein.

### Claim 9 (depends on 6)

The method of claim 6 wherein selecting subsets of journey data instances for forming trip data indicative of vehicle trips includes, selecting at least a first sequence of journey data instances from journey data including at least one journey data instance corresponding to a core subzone that is mapped to a first intersection, and immediately followed by a journey data instance mapped to a second intersection, wherein the second intersection and the first intersection are not the same intersection and selecting a second sequence of journey data instances including at least a journey data instance corresponding to a non-core subzone mapped to the first intersection immediately preceding the at least a first sequence of journey data instances and forming trip data based on the first sequence of journey data instances and the second sequence of journey data instances, mapping each trip data instance to the first intersection and storing an indication of the mapping therein.

### Claim 10 (depends on 6)

The method of claim 6 wherein forming trip metadata includes forming trip metadata selected from a group of: Hardwareld data, VIN data, Make data, Model data, VehicleYear data, WeightClass data, VehicleType data, Vocation data, TripID data, IntersectionId data, TimezoneName data, EventStartTimeUTC data, EventEndTimeUTC data, EventStartTimeLocal data, EventEndTimeLocal data, StartingLocation data, EntryCardinal data, ExitCardinal data, StreetNameEntry data, StreetNameExit data, SignalUsed data, TurnSignals data, TravelTime data, TravelDistance data, TravelSpeed data, RunningTime data, RunningSpeed data, MaxSpeed data, MinSpeed data, StopTimeTotal data, NumberOfStops data, TimeFromFirstStop data, DistanceFromFirstStop data, CoreDistance data, MaxAcceleration data, and MinAcceleration data.

### Claim 11 (depends on 10)

The method of claim 10 wherein forming EntryCardinal data comprises determining a bearing between position data corresponding to the first instance of corresponding trip data located in the core and the immediately preceding the instance of the corresponding journey data and creating EntryCardinal data indicative of the bearing.

### Claim 12 (depends on 10)

The method of claim 10 wherein forming ExitCardinal data comprises determining a bearing between position data corresponding to the last instance of corresponding trip data located in the core and the immediately following instance of the corresponding journey data and creating ExitCardinal data indicative of the bearing.

### Claim 13 (depends on 1)

The method according to claim 1 wherein processing trip metadata associated with the first intersection for providing traffic metrics data indicative of intersection metrics for the first intersection further includes selecting a subset of trip metadata associated with the first intersection for forming filtered trip metadata and processing filtered trip metadata for providing traffic metrics for the first intersection.

### Claim 14 (depends on 13)

The method of claim 13 wherein selecting a subset of trip metadata for forming filtered trip metadata includes selecting a subset of trip metadata dependent on filter data.

### Claim 15 (depends on 14)

The method of claim 14 wherein filter data is selected from a group of: Hardwareld data, VIN data, Make data, Model data, VehicleYear data, WeightClass data, VehicleType data, Vocation data, IntersectionId data, TimezoneName data, EventStartTimeUTC data, EventEndTimeUTC data, EventStartTimeLocal data, EventEndTimeLocal data, StartingLocation data, EntryCardinal data, ExitCardinal data, StreetNameEntry data, StreetNameExit data, SignalUsed data, TurnSignals data, TravelTime data, TravelDistance data, TravelSpeed data, RunningTime data, RunningSpeed data, MaxSpeed data, MinSpeed data, StopTimeTotal data, NumberOfStops data, TimeFromFirstStop data, DistanceFromFirstStop data, CoreDistance data, MaxAcceleration data, and MinAcceleration data.

### Claim 16 (depends on 12)

The method of claim 12 wherein filter data indicates one or more days of the week.

### Claim 17 (depends on 14)

The method of claim 14 wherein filter data includes filter data provided by a user.

### Claim 18 (depends on 1)

The method of claim 1 wherein forming intersection metrics data includes forming intersection metrics data selected from a group of:
Percentage Stopping, AvgTravel Speed, AvgRunningSpeed, AvgTotalTimeStopped, AvgTotalTimeStoppedNoZero, AvgTravelTime, AvgTimeFromFirstStop, AvgNumberOfStops, AvgNumberOfStopsNoZero, AvgDistanceFromIntersectionToFirstStop, PercentOfVolumeByVehicleClass, and NumberOfTrips.

### Claim 19 (depends on 10)

The method of claim 10 wherein forming trip metadata StreetNameEntry data, for at least an intersection of the plurality of intersections, includes receiving data indicative of roads and location thereof within the area occupied by core subzones of the corresponding intersection core;
mapping each core subzone of the intersection core to the nearest road; and
generating StreetNameEntry data indicating associated with the first core subzone of the trip.

### Claim 20 (depends on 10)

The method of claim 10 wherein forming trip metadata StreetNameEntry data, for at least an intersection of the plurality of intersections, includes
receiving data indicative of roads and location thereof within the area occupied by core subzones of the corresponding intersection core;
mapping each core subzone of the intersection core to the nearest road; and
generating StreetNameEntry data indicating the name of the road associated with the last core subzone of the trip.