Traffic flow rates

• US 10,319,232 B2
• Filed: 06/19/2017
• Issued: 06/11/2019
• Est. Priority Date: 03/03/2014
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Scofield, Christopher L.
• Primary Examiner(s): Feild, Joseph H
• Assistant Examiner(s): Point, Rufus C
• Application Number:
  US15/626,449
• Publication Number:
  US 20170287327A1
• Time in Patent Office: 722 Days
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/4845 Toxicology, e.g. by detection of alcohol, drug or toxic products A61B5/4848 takes precedence
• CPC Class Codes: B60R 16/0236 for economical driving
• CPC Class Codes: B60W 2040/0809 Driver authorisation; Driver identity check
• CPC Class Codes: B60W 2040/0872 Driver physiology
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2710/1044 Output speed change rate
• CPC Class Codes: B60W 2710/18 Braking system
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 30/143 Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B64C 39/024 : of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64U 2101/30 : for imaging, photography or videography
• CPC Class Codes: G01C 21/3415 : Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G01C 21/3469 : Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3608 : using speech input, e.g. using speech recognition
• CPC Class Codes: G01C 21/3617 : using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/3655 : Timing of guidance instructions
• CPC Class Codes: G01C 21/3667 : Display of a road map G01C21/3614 takes precedence; guidance using 3D or perspective road maps G01C21/3635
• CPC Class Codes: G01C 21/3682 : output of POI information on a road map G01C21/3614, G01C21/3685 take precedence
• CPC Class Codes: G05D 1/0011 : associated with a remote control arrangement
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06Q 20/102 : Bill distribution or payments
• CPC Class Codes: G06Q 2240/00 : Transportation facility access, e.g. fares, tolls or parking
• CPC Class Codes: G06Q 30/0283 : Price estimation or determination
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06Q 50/40 : Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07B 15/00 : Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points handling coins or valuable papers, e.g. banknotes, G07D; apparatus for vending or hiring articles or services activated by coins, credit cards, paper currency or the like G07F7/00, G07F17/00
• CPC Class Codes: G07B 15/063 : using wireless information transmission between the vehicle and a fixed station identification of vehicles G08G
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• 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 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 : for classifying traffic situation
• CPC Class Codes: G08G 1/0141 : for traffic information dissemination
• CPC Class Codes: G08G 1/0145 : for active traffic flow control
• CPC Class Codes: G08G 1/065 : by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
• CPC Class Codes: G08G 1/07 : Controlling traffic signals
• CPC Class Codes: G08G 1/093 : Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output
• CPC Class Codes: G08G 1/0962 : having an indicator mounted inside the vehicle, e.g. giving voice messages
• CPC Class Codes: G08G 1/0965 : responding to signals from another vehicle, e.g. emergency vehicle
• CPC Class Codes: G08G 1/0967 : Systems involving transmission of highway information, e.g. weather, speed limits transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 : where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/096811 : where the route is computed offboard
• CPC Class Codes: G08G 1/096822 : where the segments of the route are transmitted to the vehicle at different locations and times
• CPC Class Codes: G08G 1/096838 : where the user preferences are taken into account or the user selects one route out of a plurality
• CPC Class Codes: G08G 1/097 : Supervising of traffic control systems, e.g. by giving an alarm if two crossing streets have green light simultaneously
• CPC Class Codes: H04B 1/3822 : specially adapted for use in vehicles H04B1/3827 takes precedence
• CPC Class Codes: H04B 7/18504 : Aircraft used as relay or high altitude atmospheric platform
• CPC Class Codes: H04L 67/02 : based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 9/3247 : involving digital signatures
• CPC Class Codes: H04M 15/60 : based on actual use of network resources
• CPC Class Codes: H04W 12/08 : Access security
• CPC Class Codes: H04W 4/024 : Guidance services
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/42 : for mass transport vehicles, e.g. buses, trains or aircraft
• CPC Class Codes: H04W 4/48 : for in-vehicle communication
• CPC Class Codes: H04W 4/50 : Service provisioning or reconfiguring

Abstract

One or more techniques and/or systems are provided for determining a scaled flow rate of traffic for a road segment. For example, probe flow rate information is determined based upon locational information from one or more probe vehicles on a road segment (e.g., a flow rate of probe vehicles corresponding to a sum of probe vehicles identified from time stamped global positioning system coordinates provided by the probe vehicles). Satellite imagery of the road segment is analyzed to identify a count of vehicles on the road segment. Scale factor and offset information is estimated based upon the probe flow rate information and the count of vehicles. The scale factor and offset information is used to scale the probe flow rate information to determine a scaled flow rate that may be a relatively accurate flow rate of traffic, which may correspond to an inferred traffic volume along the road segment.

Claims

1. A system for determining a flow rate of traffic for a road segment, comprising:

a traffic modeling component, implemented via a processor, configured to;

determine probe flow rate information based upon locational information from one or more probe vehicles on a road segment;

obtain satellite imagery of the road segment;

analyze the satellite imagery to identify a count of vehicles on the road segment for a unit of time; and

scale the probe flow rate information based upon the count of vehicles for the unit of time to determine a flow rate.

2. The system of claim 1, the traffic modeling component configured to:

identify a linear feature for identifying the road segment within the satellite imagery.

3. The system of claim 1, the traffic modeling component configured to:

overlay a road network onto the satellite imagery for identifying the road segment within the satellite imagery.

4. The system of claim 1, the traffic modeling component configured to:

identify a parallelogram for identifying a vehicle within the satellite imagery.

5. The system of claim 1, the traffic modeling component configured to:

determine a ground flow rate based upon the count of vehicles; and

determine the flow rate based upon the probe flow rate information and the ground flow rate.

6. The system of claim 1, the traffic modeling component configured to:

determine the flow rate for a second unit of time, different than the unit of time, in real-time based upon the probe flow rate information corresponding to real-time locational information of the one or more probe vehicles.

7. The system of claim 1, the traffic modeling component configured to:

obtain real-time locational information from a set of probe vehicles traveling along the road segment; and

determine a real-time flow rate for the road segment based upon the real-time locational information.

8. The system of claim 1, the locational information corresponding to a timestamp and global positioning system (GPS) coordinates of at least one of a vehicle head unit, a vehicle computing device, or a mobile device located within a vehicle.

9. The system of claim 1, the traffic modeling component configured to:

estimate scale factor and offset information based upon the probe flow rate information and the count of vehicles for the unit of time; and

average the scale factor and offset information based upon at least one of a road type, a day of week, a number of lanes per road, a season, road construction, or a weather condition.

10. The system of claim 1, the traffic modeling component configured to:

utilize vehicle speed information and the probe flow rate information to determine a traffic density.

11. The system of claim 1, the flow rate corresponding to inferred traffic volume along the road segment.

12. The system of claim 1, the traffic modeling component configured to:

utilize an image analysis algorithm to identify the count of vehicles from the satellite imagery.

13. The system of claim 12, the traffic modeling component configured to:

utilize the locational information to identify one or more identified probe vehicles on the road segment; and

adjust the image analysis algorithm based upon the one or more identified probe vehicles.

14. The system of claim 13, the traffic modeling component configured to:

adjust at least one of an edge detection function or a threshold used by the image analysis algorithm based upon the one or more identified probe vehicles.

15. A computer-implemented method for determining a scaled flow rate of traffic for a road segment, comprising:

determining probe flow rate information based upon locational information from one or more probe vehicles on a road segment;

obtaining imagery of the road segment;

analyzing the imagery to identify a count of vehicles on the road segment for a unit of time;

estimating scale factor and offset information based upon the probe flow rate information and the count of vehicles for the unit of time; and

scaling, via a processor, the probe flow rate information based upon the scale factor and offset information to determine a scaled flow rate.

16. The computer-implemented method of claim 15, comprising:

obtaining real-time locational information from a set of probe vehicles traveling along the road segment; and

applying the scale factor and offset information to the real-time locational information to determine a real-time flow rate for the road segment.

17. The computer-implemented method of claim 15, comprising:

utilizing an image analysis algorithm to identify the count of vehicles from the imagery;

utilizing the locational information to identify one or more identified probe vehicles on the road segment; and

adjusting the image analysis algorithm based upon the one or more identified probe vehicles.

18. The computer-implemented method of claim 15, the imagery comprising an image.

19. The computer-implemented method of claim 15, the imagery comprising a video.

20. A non-transitory computer readable medium comprising instructions which when executed perform a method for determining a scaled flow rate of traffic for a road segment, comprising:

determining flow rate information associated with a road segment;

obtaining satellite imagery of a road segment;

analyzing the satellite imagery to identify a count of vehicles on the road segment for a unit of time;

estimating scale factor and offset information based upon the flow rate information and the count of vehicles for the unit of time; and

scaling, via a processor, the flow rate information based upon the scale factor and offset information to determine a scaled flow rate.