System and method of predicting traffic speed based on speed of neighboring link

• US 20080033630A1
• Filed: 07/26/2006
• Published: 02/07/2008
• Est. Priority Date: 07/26/2006
• Status: Abandoned Application

• Current Assignee: FINEDIGITAL Inc.
• Original Assignee: FINEDIGITAL Inc.
• Inventors: Yoon, Won-Sik, Lim, Sung-Hwa, Lee, Eun-Mi, Kim, Byung-Soo, Kim, Jae-Hoon
• Application Number:
  US11/493,088
• Publication Number:
  US 20080033630A1
• Time in Patent Office: Days
• US Class Current: 701/117
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions

Abstract

The present invention relates to a system and a method of predicting traffic speed based on the speeds of neighboring links. A system for predicting traffic speed on a link basis, on a map based on a past traffic patterns, includes a prediction model establishment unit that establishes a neighboring link speed-based prediction model including the correlation between the speed of each link and the speeds of neighboring links based on real-time traffic information accumulated in a real-time traffic information database for a predetermined time, a prediction model database that stores the established neighboring link speed-based prediction model data, and a traffic speed prediction unit that inputs real-time neighboring link speed information for a specific object link to the neighboring link speed-based prediction model and predicts an output value according to the real-time neighboring link speed information as the traffic speed of the object link.

Claims

1. A system for predicting traffic speed on a link basis, on a map based on a past traffic patterns, the system comprising:

a prediction model establishment unit that establishes a neighboring link speed-based prediction model including the correlation between a speed of each link and speeds of neighboring links based on real-time traffic information accumulated in a real-time traffic information database for a predetermined time;

a prediction model database that stores the established neighboring link speed-based prediction model data; and

a traffic speed prediction unit that inputs real-time neighboring link speed information for a specific object link to the neighboring link speed-based prediction model and predicts an output value according to the input of the real-time neighboring link speed information as a traffic speed of the object link.

2. The system of claim 1,

wherein the prediction model establishment unit establishes the neighboring link speed-based prediction model by learning using a neuron network including an input layer having a plurality of input neurons for receiving a plurality of neighboring link speeds for a specific object link as inputs, and an output layer having one output neuron for outputting the object link speed as an output.

3. The system of claim 1,

wherein the prediction model establishment unit establishes the neighboring link speed-based prediction model by learning using a neuron network including an input layer having a plurality of input neurons for receiving a plurality of neighboring link speeds, a date, and a time for a specific object link as inputs, and an output layer having one output neuron for outputting the object link speed as an output.

4. The system of claim 2,

wherein the neuron network further includes one or more hidden layers interposed between the input layer and the output layer in order to reduce an error rate between an output value of the neighboring link speed-based prediction model and an actual value of the object link speed.

5. The system of claim 1, further comprising:

a real-time traffic information monitoring unit that monitors the real-time traffic information accumulated in the real-time traffic information database, stores a statistical value of a traffic speed in a compensation traffic information database and, if it is impossible to collect traffic information of a specific object link from the real-time traffic information database, transmits information about a corresponding object link and information about neighboring link speeds to the traffic speed prediction unit in order to request the traffic speed prediction unit to predict a traffic speed,

wherein the traffic speed prediction unit inputs the neighboring link speed information, which is received from the real-time traffic information monitoring unit, to the neighboring link speed-based prediction model and stores an output value according to the input of the neighboring link speed information in the compensation traffic information database as the traffic speed of the object link.

6. The system of claim 1,

wherein the neighboring links include one or more unit links using a departure node of an object link as an arrival node and one or more unit links using an arrival node of the object link as a departure node, for the object link from a specific departure node to a specific arrival node.

12. The system of claim 3,

wherein the neuron network further includes one or more hidden layers interposed between the input layer and the output layer in order to reduce an error rate between an output value of the neighboring link speed-based prediction model and an actual value of the object link speed.

7. A method of predicting traffic speed on a link basis, on a map based on a past traffic patterns, the method comprising:

calculating a traffic speed on a link basis based on real-time traffic information accumulated for a predetermined time;

deducing the correlation between a speed of each link and speeds of neighboring links; and

predicting a speed of a specific object link using information about real-time speeds of neighboring links for the object link and the correlation deduced in the second step.

8. The method of claim 7,

wherein the deducing of the correlation establishes a neighboring link speed-based prediction model by learning using a neuron network including an input layer having a plurality of input neurons for receiving a plurality of neighboring link speeds for a specific object link as inputs, and an output layer having one output neuron for outputting the object link speed as an output.

9. The method of claim 7,

wherein the deducing of the correlation establishes a neighboring link speed-based prediction model by learning using a neuron network including an input layer having a plurality of input neurons for receiving a plurality of neighboring link speeds, a date, and a time for a specific object link as inputs, and an output layer having one output neuron for outputting the object link speed as an output.

10. The method of claim 8,

wherein the neuron network further includes one or more hidden layers interposed between the input layer and the output layer in order to reduce an error rate between an output value of the neighboring link speed-based prediction model and an actual value of the object link speed.

11. The method of claim 7,

wherein the neighboring links include one or more unit links using a departure node of an object link as an arrival node and one or more unit links using an arrival node of the object link as a departure node, for the object link from a specific departure node to a specific arrival node.

13. The method of claim 9,

wherein the neuron network further includes one or more hidden layers interposed between the input layer and the output layer in order to reduce an error rate between an output value of the neighboring link speed-based prediction model and an actual value of the object link speed.