Commute distance optimization

Patent metadata

Field	Value
Publication number	US-11544584-B2
Application number	US-201815935140-A
Country	US
Kind code	B2
Filing date	Mar 26, 2018
Priority date	Mar 26, 2018
Publication date	Jan 3, 2023
Grant date	Jan 3, 2023

Abstract

A method, system and apparatus for accessing an internal database that includes at least one payroll database, identifying an individual, a first location, a second location, a future date, and a time of day. Responsive to identifying the individual, the first location, the second location, the date, and the time of day, determining a predicted travel time for the individual between the first location and the second location at the time of day on the future date.

First claim

What is claimed is:

1. A computer-implemented method for traffic prediction, the computer implemented method comprising:
accessing, by a machine intelligence application including machine learning and predictive algorithms running on a processor unit, an internal database that includes at least one payroll database;
identifying, by the machine intelligence application running on the processor unit, an individual, a first location, a second location, a future date, and a time of day;
responsive to identifying the individual, the first location, the second location, the future date, and the time of day, determining, by the machine intelligence application running on the processor unit, a predicted travel time for the individual between the first location and the second location at the time of day on the future date;
responsive to identifying the first location, computing, by the machine intelligence application running on the processor, a number of routes for movement by the individual from the first location to the second location based on predicted factors from predictive algorithms of the machine intelligence application;
combining, by the machine intelligence application running on the processor unit, the predicted routes for the individual with additional predicted routes calculated for other individuals to create combined predicted route data that is improved with regard to the routes by employee travel along the routes from machine learning of the machine intelligence application, wherein the combined predicted route data comprises combined predicted travel times for individuals;
accessing, by the machine intelligence application running on the processor unit, the combined predicted route data;
accessing, by the machine intelligence application running on the processor unit, current internal and external data;
analyzing, by the machine intelligence application running on the processor unit, the combined predicted route data, the current internal and external data;
responsive to analyzing the combined predicted route data, determining, by the machine intelligence application running on the processor unit, changes to traffic routing to the second location; and
responsive to determining changes to traffic routing to the second location, sending, by the machine intelligence application running on the processor unit, instructions to a number of traffic devices thereby controlling traffic to the second location.

2. The computer-implemented method of claim 1 , further comprising:
receiving from a device, by the machine intelligence application running on the processor unit, a number of actual routes taken by the individual between the first location and the second location over a period of time;
retrieving from a source, by the machine intelligence application running on the processor unit, a number of factors associated with each of the number of routes and the number of actual routes;
analyzing by the machine intelligence application running on the processor unit, the number of routes, the number of actual routes, and the number of factors to determine a number of predicted routes for the individual; and
delivering, by the machine intelligence application running on the processor unit, the number of predicted routes to a first database.

3. The computer-implemented method of claim 2 , wherein the number of factors include a number of internal factors in an internal database and a number of external factors in an external database.

4. The computer-implemented method of claim 3 , wherein the number of internal factors comprise:
an age of the individual, a physical address of the individual, a salary of the individual, an automobile of the individual, a work schedule of the individual, IP addresses of the individual, mobile device numbers of the individual, email addresses of the individual, an industry of the individual, and a geographic location of the individual.

5. The computer-implemented method of claim 3 , wherein the number of external factors comprise:
weather conditions, road construction, detours, traffic congestion, and accidents, public data feeds regarding traffic flow, public records regarding proposed construction, and public records regarding anticipated population growth.

6. The computer-implemented method of claim 2 , further comprising:
storing by the machine intelligence application running on the processor unit, the combined predicted data in a blockchain.

7. The computer-implemented method of claim 2 , wherein the device is a mobile phone.

8. The computer implemented method of claim 2 , wherein the device is a monitoring device affixed to an automobile of the individual.

9. The computer-implemented method of claim 1 , wherein controlling traffic comprises at least one of closing a lane and opening a lane.

10. The computer-implemented method of claim 1 , further comprising: securing, by the machine intelligence application running on the processor unit, access to a combined predicted database with one of a two-step access system and an encryption system.

11. A traffic prediction system comprising:
a processor unit connected to a number of internal sources, a number of external sources, and a combined predicted data database;
a device carried by an individual or affixed to a mode of transportation of the individual;
computer program instructions stored in a computer readable storage media and configured to cause a machine intelligence application including machine learning and predictive algorithms running on a processor unit to access an internal database that includes at least one payroll database, identify an individual, a first location, a second location, a future date, and a time of day, and responsive to the processor unit identifying the individual, the first location, the second location, the future date, and the time of day, to determine a predicted travel time for the individual between the first location and the second location at the time of day on the future date, determine changes to traffic routing to the second location and, responsive to determine changes to traffic routing to the second location, send instructions to a number of traffic devices thereby controlling traffic to the second location;
computer program instructions stored in the computer readable storage media and configured to cause the machine intelligence application running on the processor unit, responsive to identifying the first location, computing, by the processor, a number of routes for movement by the individual from the first location to the second location based on predicted factors from predictive algorithms of the machine intelligence application;
computer program instructions stored in the computer readable storage media and configured to cause the machine intelligence application running on the processor unit to receive, from a device, a number of actual routes taken by the individual between the first location and the second location over a period of time;
computer program instructions stored in the computer readable storage media and configured to cause the machine intelligence application running on the processor unit to retrieve a number of factors associated with each of the number of routes and the number of actual routes;
computer program instructions stored in the computer readable storage media and configured to cause the machine intelligence application running on the processor unit, to analyze the number of routes, the number of actual routes, and the number of factors to determine a number of predicted routes for the individual;
computer program instructions stored in the computer readable storage media and configured to the machine intelligence applica

Assignees

• Adp Llc
• Adp Inc

Inventors

• Newman Kurt
• Ghosh Debashis
• Wohlers Robert

Classifications

• G01C21/26

  specially adapted for navigation in a road network · CPC title

• G08G1/012 Primary

  from other sources than vehicle or roadside beacons, e.g. mobile networks · CPC title

• G01C21/3691

  Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions (arrangements for giving variable traffic instructions G08G1/09) · CPC title

• G08G1/0125

  Traffic data processing · CPC title

• G06N5/04 Primary

  Inference or reasoning models · CPC title