SYSTEM FOR PROVIDING TRAFFIC DATA AND DRIVING EFFICIENCY DATA

• US 20160321918A1
• Filed: 07/11/2016
• Published: 11/03/2016
• Est. Priority Date: 05/18/2011
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Pelmorex Canada Inc.
• Inventors: Guziec, Andr, Faure, Romain, Poulain, Damien
• Granted Patent: US 9,547,984 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/09675 where a selection from the received information takes place in the vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/096783 where the origin of the information is a roadside individual element
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/0969 having a display in the form of a map
• CPC Class Codes: H04W 4/60 Subscription-based services using application servers or record carriers, e.g. SIM application toolkits
• CPC Class Codes: H04W 88/02 Terminal devices

Abstract

Current and predicted traffic information is provided from incident data, traffic flow data, and media related to traffic received from multiple sources. The crowd sourced data may be provided passively by applications on remote mobile devices or actively by users operating the remote mobile devices. An application on a mobile device may receive the multiple data types, aggregate and validate the data, and provides traffic information for a user. The traffic information may relate to the current position and route of the user or a future route. The present technology may also provide driving efficiency information such as fuel consumption data, carbon footprint data, and a driving rating for a user associated with a vehicle.

Claims

1. A method for predicting traffic, the method comprising:

receiving traffic data originating from multiple sources at a mobile device, wherein the received traffic data includes;

crowd-sourced data collected passivly through a set of other mobile devices, and

crowd-sourced data collected actively from a remote user through another set of other mobile devices;

receiving user input at an interface of the mobile device, wherein the received user input specifies a preferred route, a time of day, and a preferred type of traffic data;

executing instructions stored in memory, wherein execution of the instructions by a processor;

filters the received traffic data based on the preferred type of traffic data specified by the received user input,

analyzes the filtered traffic data received at a plurality of different times of day to determine recurring traffic speeds associated with the preferred route, wherein a plurality of recurring traffic speeds are determined for the preferred route, and wherein each of the plurality of recurring traffic speeds is associated with a respective time of day,

makes a prediction regarding traffic at the specified tiem of day based on the determined recurring traffic speeds associated with the preferred route and time of day specified by the received user input, and

displays a map that includes the predicted traffic information regarding the preferred route at the time of day specified by the received user input, wherein the map is displayed on a screen of the mobile device.

2. The method of claim 1, further comprising displaying a suggestion based on the predicted traffic information, wherein the suggestion informs the user to leave earlier, delay departure or leave on time.

3. The method of claim 1, further comprising validating the filtered traffic data by aggregating the filtered traffic data and removing anomalies in the filtered traffic data.

4. The method of claim 3, further comprising updating the predicted traffic information using the validated traffic data.

5. The method of claim 1, further comprising setting a frequency of updating the predicted traffic information of the preferred route, wherein the frequency is based on a battery performance for the mobile device.

6. The method of claim 1, wherein the received traffic data includes incident data, traffic flow data, and media data.

7. The method of claim 1, further comprising predicting an amount of fuel consumed for a trip based on the predicted traffic information of the preferred route.

8. The method of claim 7, further comprising calculating a driver rating for the preferred route based on the predicted traffic information, wherein the driver rating compares the predicted amount of fuel consumed against a pre-determined amount of fuel consumed associated with the preferred route.

9. A device for predicting traffic data, the device comprising:

a communication interface that receives traffic data originating from multiple sources at a mobile device, wherein the received traffic data includes;

crowd-sourced data collected passivly through a set of other mobile devices, and

crowd-sourced data collected actively from a remote user through another set of other mobile devices;

a user interface that receives user input, wherein the received user input specifies a preferred route, a time of day, and a preferred type of traffic data;

a personalization module executable by a processor, wherein the personalization module is executable to;

filter the received traffic data based on the preferred type of traffic data specified by the received user input,

analyze the filtered traffic data received at a plurality of different times of day to determine recurring traffic speeds associated with the preferred route, wherein a plurality of recurring traffic speeds are determined for the preferred route, and wherein each of the plurality of recurring traffic speeds is associated with a respective time of day, and

make a prediction regarding traffic at the specified tiem of day based on the determined recurring traffic speeds associated with the preferred route and time of day specified by the received user input; and

a screen that displays a map that includes the predicted traffic information regarding the preferred route at the time of day specified by the received user input.

10. The device of claim 9, wherein the screen further displays a suggestion based on the predicted traffic information, wherein the suggestion informs the user to leave earlier, delay departure or leave on time.

11. The device of claim 9, further comprising a data validation module executable by the processor to validate the filtered traffic data by aggregating the filtered traffic data and removing anomalies in the filtered traffic data.

12. The device of claim 11, further comprising a traffic prediction module that updates the predicted traffic information using the validated traffic data.

13. The device of claim 9, wherein the personalization module is further executable by the processor to set a frequency of updating the predicted traffic information of the preferred route, wherein the frequency is based on a battery performance for the mobile device.

14. The device of claim 9, wherein the received traffic data includes incident data, traffic flow data, and media data.

15. The device of claim 9, further comprising an eco-analysis module executable by the processor to predict an amount of fuel consumed for a trip based on the predicted traffic information of the preferred route.

16. The device of claim 15, wherein the eco-analysis module is further executable by the processor to calculate a driver rating for the preferred route based on the predicted traffic information, wherein the driver rating compares the predicted amount of fuel consumed against a pre-determined amount of fuel consumed associated with the preferred route.

17. A non-transitory computer-readable storage medium, having embodied thereon a program executable by a processor to perfrom a method for predicting traffic, the method comprising:

receiving traffic data originating from multiple sources at a mobile device, wherein the received traffic data includes;

crowd-sourced data collected passivly through a set of other mobile devices, and

crowd-sourced data collected actively from a remote user through another set of other mobile devices;

receiving user input at an interface of the mobile device, wherein the received user input specifies a preferred route, a time of day, and a preferred type of traffic data;

filtering the received traffic data based on the preferred type of traffic data specified by the received user input;

analyzing the filtered traffic data received at a plurality of different times of day to determine recurring traffic speeds associated with the preferred route, wherein a plurality of recurring traffic speeds are determined for the preferred route, and wherein each of the plurality of recurring traffic speeds is associated with a respective time of day;

making a prediction regarding traffic at the specified tiem of day based on the determined recurring traffic speeds associated with the preferred route and time of day specified by the received user input; and

displaying a map that includes the predicted traffic information regarding the preferred route at the time of day specified by the received user input, wherein the map is displayed on a screen of the mobile device.