# US 2023/0410643 A1 · Selecting traffic algorithms to generate traffic data

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- Number: US 2023/0410643 A1
- Document ID: US20230410643A1
- Status: Published application
- Kind: A1
- Application number: 17/843,067
- Filed: 2022-06-17
- Published: 2023-12-21
- Earliest claimed: 2022-06-17
- Assignees: Microsoft Technology Licensing, LLC
- Applicants: Microsoft Technology Licensing, LLC
- Inventors: Leon Oliver STENNETH; Catalin Bogdan CAPOTA
- Web page: https://patentgrep.com/patent/US20230410643A1

## CPC

- G08G 1/0129: {for creating historical data or processing based on historical data}
- G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)}
- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- G08G 1/0116: {from roadside infrastructure, e.g. beacons}
- G08G 1/0133: {for classifying traffic situation}
- G08G 1/0141: {for traffic information dissemination}
- 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)

## Abstract

Methods and systems for generating traffic data are provided. An example method includes receiving one or more routes from a first location to a second location, receiving vehicle data along each of the one or more routes, and segmenting the one or more routes, based on a plurality of grid spaces on a map. The method further includes determining, based on the vehicle data, a first algorithm corresponding to a first grid space from the plurality of grid spaces and a second traffic algorithm corresponding to a second grid space from the plurality of grid spaces. The method further includes generating traffic data, along the one or more routes, using the first algorithm and the second algorithm.

## Claims

### Claim 1

A method for generating traffic data, the method comprising:
receiving one or more routes from a first location to a second location;
receiving vehicle data along each of the one or more routes;
segmenting the one or more routes, based on a plurality of grid spaces on a map;
determining, based on the vehicle data, a first algorithm corresponding to a first grid space from the plurality of grid spaces and a second traffic algorithm corresponding to a second grid space from the plurality of grid spaces; and
generating traffic data, along the one or more routes, using the first algorithm and the second algorithm.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
receiving road data along each of the one or more routes;
segmenting the one or more routes, based on a plurality of road types between the first location and the second location; and
determining, based on the vehicle data, a third algorithm corresponding to a first road type from the plurality of road types and a fourth algorithm corresponding to a second road type from the plurality of road types,
wherein the traffic data, along the one or more routes, is generated using at least two from the group of the first algorithm, the second algorithm, the third algorithm, and the fourth algorithm.

### Claim 3 (depends on 1)

The method of claim 1, wherein the first algorithm is one of a historical algorithm, real-time algorithm, Bayesian inference algorithm, fitted weights algorithm, or deep learning algorithm.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
transmitting the traffic data to a remote device.

### Claim 5 (depends on 1)

The method of claim 1, wherein the traffic data comprises an estimated travel time from the first location to the second location.

### Claim 6 (depends on 1)

The method of claim 1, wherein the vehicle data corresponds to a number of vehicles travelling along each of the one or more routes.

### Claim 7 (depends on 1)

The method of claim 1, wherein dimensions of each of the plurality of grid spaces are configurable.

### Claim 8

A method for generating traffic data, the method comprising:
receiving one or more routes from a first location to a second location;
receiving vehicle data along each of the one or more routes;
receiving road data corresponding to a plurality of road types along each of the one or more routes;
segmenting the one or more routes, based on the plurality of road types;
determining, based on the vehicle data, a first algorithm corresponding to a first road type from the plurality of road types and a second algorithm corresponding to a second road type from the plurality of road types; and
generating traffic data, along the one or more routes, using the first algorithm and the second algorithm.

### Claim 9 (depends on 8)

The method of claim 8, further comprising:
segmenting the one or more routes, based on a plurality of grid spaces on a map; and
determining, based on the vehicle data, a third algorithm corresponding to a first grid space from the plurality of grid spaces and a fourth traffic algorithm corresponding to a second grid space from the plurality of grid spaces,
wherein the traffic data, along the one or more routes, is generated using at least two from the group of the first algorithm, the second algorithm, the third algorithm, and the fourth algorithm.

### Claim 10 (depends on 8)

The method of claim 8, wherein the first algorithm is one of a historical algorithm, real-time algorithm, Bayesian inference algorithm, fitted weights algorithm, or deep learning algorithm.

### Claim 11 (depends on 8)

The method of claim 8, further comprising:
transmitting the traffic data to a remote device.

### Claim 12 (depends on 8)

The method of claim 8, wherein the traffic data comprises an estimated travel time from the first location to the second location.

### Claim 13 (depends on 12)

The method of claim 12, wherein the vehicle data corresponds to a number of vehicles travelling along each of the one or more routes.

### Claim 14 (depends on 13)

The method of claim 13, wherein the plurality of road types include one or more from the group of: an interstate, highway, and local road.

### Claim 15

A method for generating traffic data, the method comprising:
receiving a request for directions from a first location to a second location;
generating one or more routes from the first location to the second location;
receiving vehicle data along each of the one or more routes;
receiving road data along each of the one or more routes;
generating an estimated travel time from the first location to the second location, for each of the one or more routes, using a respective algorithm for each of the one or more routes;
determining a confidence score for each of the estimated travel times based on the vehicle data, road data, and respective algorithm; and
generating a report comprising the estimated travel time and the corresponding confidence score, for at least one of the one or more routes.

### Claim 16 (depends on 15)

The method of claim 15, wherein the vehicle data corresponds to a number of vehicles travelling along each of the one or more routes.

### Claim 17 (depends on 15)

The method of claim 15, wherein the road data corresponds to one or more types of roads along each of the one or more routes.

### Claim 18 (depends on 17)

The method of claim 17, wherein the one or more types of roads are one or more from the group of: an interstate, highway, and local road.

### Claim 19 (depends on 15)

The method of claim 15, wherein the algorithms are each one of a historical algorithm, real-time algorithm, Bayesian inference algorithm, fitted weights algorithm, or deep learning algorithm.

### Claim 20 (depends on 15)

The method of claim 15, wherein the vehicle data is preprocessed to remove a subset of data corresponding to vehicles that have been stationary for longer than a predetermined period of time.