# US 2023/0054547 A1 · Vehicle mobility patterns based on user location data

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- Number: US 2023/0054547 A1
- Document ID: US20230054547A1
- Status: Published application
- Kind: A1
- Application number: 17/404,697
- Filed: 2021-08-17
- Published: 2023-02-23
- Earliest claimed: 2021-08-17
- Assignees: Allstate Insurance Company
- Applicants: Allstate Insurance Company
- Inventors: Matei Stroila; Narayanan Alwar; Timothy W. Gibson; Sunil Chintakindi
- Web page: https://patentgrep.com/patent/US20230054547A1

## CPC

- G06Q 10/0635: Risk analysis of enterprise or organisation activities
- H04W 4/029: Location-based management or tracking services
- G06F 16/29: Geographical information databases
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0283: Price estimation or determination
- G06Q 40/08: Insurance
- G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083)
- H04W 4/022: {with dynamic range variability}
- H04W 64/006: {with additional information processing, e.g. for direction or speed determination}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Methods, computer-readable media, software, and system may generally build and quantify mobility patterns based on user location data, both at an individual level and an aggregate level. The system may determine the origin and destination data for each trip taken by a user. The system may then define areas of mobility using a mobility graph built from the data. The graph may include nodes and edges. In some examples, the nodes are constructed from the origins and destinations of the trajectories using spatial clustering techniques. Further, the edges between nodes may be constructed based on the trips between them, such as two nodes are connected by an edge if there is at least one trip between them. The edges may be given different weights based on trip frequencies. The system may then determine a region of mobility using the generated mobility graph and data clustering techniques.

## Claims

### Claim 1

A system comprising:
a processor;
a display with a user interface; and
a memory unit storing computer-executable instructions, which when executed by the processor, cause the system to:
receive and determine location data that includes a plurality of trips by a user between a plurality of origins and a plurality of destinations;
aggregate the location data;
cluster the aggregated location data using one or more cluster levels;
form, by a machine learning algorithm operating on the processor, one or more location clusters into a plurality of nodes from the aggregated location data and based on the one or more cluster levels;
generate a mobility graph that includes the plurality of nodes and a plurality of edges, wherein the plurality of nodes is defined from the one or more location clusters, and the plurality of edges is defined by each of the plurality of trips between one of the plurality of origins and one of the plurality of destinations;
generate a region of mobility based on the mobility graph, wherein the region of mobility is a spatial region based on a specific location included with the plurality of nodes and the plurality of edges that meet a location criteria; and
display, via the user interface, the region of mobility for the user.

### Claim 2 (depends on 1)

The system of claim 1, wherein the location data is received from one or more sensors from a mobile device for the user.

### Claim 3 (depends on 1)

The system of claim 1, wherein the memory unit storing computer-executable instructions, which when executed by the processor, further cause the system to:
detect and filter one or more noise location data from the aggregated location data based on environmental noise and inaccuracies from the location data.

### Claim 4 (depends on 1)

The system of claim 1, wherein the one or more cluster levels includes one or more of the following: a destination-based cluster level, a spatial aggregation layer cluster level, a temporal cluster level, a point of interest based cluster level, or a regional cluster level.

### Claim 5 (depends on 1)

The system of claim 1, wherein each of the plurality of nodes includes a node size that defines a location frequency that the user visits each specific location, wherein the node size is larger when the location frequency is more frequent and the node size is smaller when the location frequency is less frequent.

### Claim 6 (depends on 1)

The system of claim 1, wherein the plurality of edges includes an edge weight based on a trip frequency, wherein the edge weight is larger when the trip frequency is more frequent and the edge weight is smaller when the trip frequency is less frequent.

### Claim 7 (depends on 1)

The system of claim 1, wherein the location criteria includes one or more of: a frequency of visit, a time of visit, a length of stay at each specific location, or a type of location visited.

### Claim 8 (depends on 1)

The system of claim 1, wherein the region of mobility is a home mobility region defined as the region of mobility around a home location of the user.

### Claim 9 (depends on 1)

The system of claim 1, wherein the region of mobility is a work mobility region defined as the region of mobility around a work location of the user.

### Claim 10 (depends on 1)

The system of claim 1, wherein the region of mobility is a point of interest mobility region defined as the region of mobility around a specific point of interest for the user.

### Claim 11 (depends on 1)

The system of claim 1, wherein the memory unit storing computer-executable instructions, which when executed by the processor, further cause the system to:
determine a risk index for the user based on the region of mobility of the user, wherein the risk index is used for insurance pricing for the user.

### Claim 12

A computer-implemented method comprising:
at a mobility region system:
receive and determine, by a processor and a memory unit storing computer-executable instructions operating on the mobility region system, location data that includes a plurality of trips by a user between a plurality of origins and a plurality of destinations;
aggregate, by the processor, the location data;
detect and filter, by the processor, noise location data from the aggregated location data, wherein the noise location data comprises environmental noise data and inaccuracies from the location data;
cluster, by the processor, the aggregated location data using one or more cluster levels;
form, by a machine learning algorithm operating on the processor, one or more location clusters into a plurality of nodes from the aggregated location data and based on the one or more cluster levels;
generate, by the processor, a mobility graph that includes the plurality of nodes and a plurality of edges, wherein the plurality of nodes is defined from the one or more location clusters, and the plurality of edges is defined by each of the plurality of trips between one of the plurality of origins and one of the plurality of destinations;
generate, by the processor, a region of mobility based on the mobility graph, wherein the region of mobility is a spatial region based on a specific location included with the plurality of nodes and the plurality of edges that meet a location criteria; and
display, by the processor via a user interface connected to the processor, the region of mobility for the user.

### Claim 13 (depends on 12)

The method of claim 12, wherein the location data is received from one or more sensors from a mobile device for the user.

### Claim 14 (depends on 12)

The method of claim 12, wherein each of the plurality of nodes includes a node size that defines a location frequency that the user visits each specific location, wherein the node size is larger when the location frequency is more frequent and the node size is smaller when the location frequency is less frequent, and further wherein each of the plurality of edges includes an edge weight based on a trip frequency, wherein the edge weight is larger when the trip frequency is more frequent and the edge weight is smaller when the trip frequency is less frequent.

### Claim 15 (depends on 12)

The method of claim 12, wherein the location criteria comprises one or more of: a frequency of visit, a time of visit, a length of stay at each specific location, or a type of location visited.

### Claim 16 (depends on 12)

The method of claim 12, wherein the region of mobility is one of the following: a home mobility region defined as the region of mobility around a home location of the user; a work mobility region defined as the region of mobility around a work location of the user; or a point of interest mobility region defined as the region of mobility around a specific point of interest for the user.

### Claim 17 (depends on 12)

The method of claim 12, further including the step of:
determine, by the processor, a risk index for the user based on the region of mobility of the user, wherein the risk index is used for insurance pricing for the user.

### Claim 18

One or more non-transitory computer-readable media storing instructions that, when executed by a computing device, cause the computing device to:
receive and determine, by a processor operating on the computing device, location data that includes a plurality of trips by a user between a plurality of origins and a plurality of destinations;
aggregate, by the processor, the location data;
detect and filter, by the processor, noise location data from the aggregated location data, wherein the noise location data comprises environmental noise data and inaccuracies from the location data;
cluster, by the processor, the aggregated location data using one or more cluster levels;
form, by a machine learning algorithm operating on the processor, one or more location clusters into a plurality of nodes from the aggregated location data and based on the one or more cluster levels;
generate, by the processor, a mobility graph that includes the plurality of nodes and a plurality of edges, wherein the plurality of nodes is defined from the one or more location clusters, and the plurality of edges is defined by each of the plurality of trips between one of the plurality of origins and one of the plurality of destinations;
generate, by the processor, a region of mobility based on the mobility graph;
and
display, by the processor via a user interface connected to the processor, the region of mobility for the user.

### Claim 19 (depends on 18)

The one or more non-transitory media storing instructions of claim 18, wherein the region of mobility is one of the following: a home mobility region defined as the region of mobility around a home location of the user; a work mobility region defined as the region of mobility around a work location of the user; or a point of interest mobility region defined as the region of mobility around a specific point of interest for the user.

### Claim 20 (depends on 18)

The one or more non-transitory media storing instructions of claim 18, wherein the region of mobility is based on location criteria that includes one or more of: a frequency of visit, a time of visit, a length of stay at a specific location, or a type of location visited.