Traffic monitoring system

• US 20170140645A1
• Filed: 11/07/2016
• Published: 05/18/2017
• Est. Priority Date: 11/06/2015
• Status: Abandoned Application

• Current Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Original Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Inventors: Balid, Walid, Refai, Hazem
• Application Number:
  US15/330,823
• Publication Number:
  US 20170140645A1
• Time in Patent Office: Days
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/015 with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
• CPC Class Codes: G08G 1/017 identifying vehicles G08G1/015, G08G1/054 take precedence
• CPC Class Codes: G08G 1/042 using inductive or magnetic detectors
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• 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 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• CPC Class Codes: H04W 88/08 Access point devices

Abstract

An automated computerized system comprises a computer system executing traffic monitoring software. The traffic monitoring software reads data corresponding to a magnetic field of a first vehicle collected by a first node. The traffic monitoring software may determine a unique magnetic signature for the first vehicle from the data collected by the first node, and correlate the first vehicle using the magnetic signature to a predefined vehicle class. The predefined vehicle class may group vehicles by structural similarity.

Claims

1. An automated computerized system, comprising:

a computer system executing traffic monitoring software reading;

data corresponding to magnetic field of a first vehicle collected by a first node;

wherein the traffic monitoring software executed by the computer system determines a unique magnetic signature for the first vehicle from the data collected by the first node and correlates the first vehicle using the magnetic signature to a predefined vehicle class, the predefined vehicle class grouping vehicles by structural similarity.

2. The automated computerized system of claim 1, wherein the first node is positioned adjacent to a road lane.

3. The automated computerized system of claim 1, wherein the first node is positioned in a center of a road lane.

4. The automated computerized system of claim 1, wherein the traffic monitoring software further reads:

data corresponding to arrival time and departure time of the first vehicle collected by the first node;

data corresponding to arrival time of the first vehicle collected by a second node, the second node longitudinally positioned from the first node and separated by a pre-determined distance;

wherein the traffic monitoring software executed by the computer system determines speed of the first vehicle based upon the unique magnetic signature of the first vehicle, at least one of the arrival time and the departure time collected by the first node, and the arrival time of the first vehicle collected by the second node.

5. The automated computerized system of claim 4, wherein the traffic monitoring software executed by the computer system determine vehicle magnetic length of the first vehicle using an instantaneous speed and occupancy time data of the first vehicle.

6. The automated computerized system of claim 5, wherein the traffic monitoring software executed by the computer system correlates the first vehicle into the predefined vehicle class using the vehicle magnetic length.

7. The automated computerized system of claim 4, wherein the traffic monitoring software executed by the computer system further reads data corresponding to arrival time and departure time of a plurality of vehicles collected by the first node and data corresponding to arrival time and departure time of the plurality of vehicles collected by the second node and determines average speed of the plurality of vehicles over a predefined time period.

8. The automated computerized system of claim 1, wherein the traffic monitoring software further reads:

data corresponding to magnetic field of the first vehicle collected by a second node;

wherein the traffic monitoring software executed by the computer system determines a unique magnetic signature for the first vehicle from the data collected by the second node and uses a vehicle re-identification process to match the magnetic signature from data collected by the first node to the magnetic signature from data collected by the second node.

9. One or more non-transitory computer readable medium storing a set of computer executable instructions for running on one or more computer systems that when executed cause the one or more computer systems to:

receive data from a first node, the first node having a plurality of sensors configured to detect signals and transmit the data to the computer system;

determine a unique magnetic signature of a first vehicle using the data received from the first node;

correlate the magnetic signature of the first vehicle to a predefined vehicle class, the predefined vehicle class grouping two or more vehicle structures.

10. The set of computer executable instruction of claim 9, further comprising receiving data from a second node, the second node positioned at a pre-determined distance from the first node;

and determining speed of the first vehicle using data collected by the first and second nodes.

11. The set of computer executable instructions of claim 10, further comprising determining vehicle magnetic length of the first vehicle using the speed of the first vehicle and occupancy time data of the first vehicle.

12. The set of computer executable instructions of claim 9, further comprising receiving data from a plurality of nodes;

identifying the unique magnetic signature of the first vehicle from data collected at each node; and

determining at least one of first location or origin of the first vehicle based on identification of the unique magnetic signature at each node.

13. The set of computer executable instructions of claim 12, further comprising determining at least one of a second location or destination of the first vehicle based on identification of the unique magnetic signature at each node.

14. The set of computer executable instructions of claim 12, further comprising determining route of the first vehicle based on identification of the unique magnetic signature at each node.

15. The set of computer executable instructions of claim 9, further comprising receiving data from a plurality of nodes;

identifying unique magnetic signatures for each of a plurality of vehicles from data collected at each node;

determining routes for each of the plurality of vehicles from the unique magnetic signatures for each of the plurality of vehicles; and

analyzing the routes for each of the plurality of vehicles to identify one or more traffic patterns.

16. An automated method of classifying a vehicle, comprising:

receiving data related to a first vehicle from a first node positioned on a roadway, the node collecting a plurality of signals from at least one sensor and transmitting the signal to a processor;

determining a unique magnetic signature of the first vehicle using the data collected from the at least one sensor;

correlating the first vehicle to a vehicle class using the unique magnetic signature of the first vehicle, the vehicle class grouping vehicles by structural similarity.

17. The automated method of claim 16, wherein at least a portion of the processor is within an intelligent access point (iAP).

18. The automated method of claim 16, wherein at least a portion of the processor is in an internet cloud computing center.

19. The automated method of claim 16, further comprising receiving data related to the first vehicle from a second node positioned on the roadway, and determining speed of the first vehicle based on the data collected from the first node and the second node.

20. The automated method of claim 19, further comprising determining vehicle magnetic length of the first vehicle using the speed, and at least one of the occupancy time data, determined using data transmitted from at least one of the first node and the second node and travel time determined using data transmitted from the first node and the second node.