SYSTEM AND METHOD FOR DYNAMIC ROUTING

• US 20110246067A1
• Filed: 03/30/2010
• Published: 10/06/2011
• Est. Priority Date: 03/30/2010
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Markham, Thomas Stephen, Brusilovsky, Roman, Salvo, Joseph James, Messier, Daniel John, Mackenzie, Patricia Denise
• Application Number:
  US12/749,550
• Publication Number:
  US 20110246067A1
• Time in Patent Office: Days
• US Class Current: 701/210
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G08G 1/096816 where the complete route is transmitted to the vehicle at once
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data

Abstract

A dynamic routing system includes a data collection module, a static routing module, an orientation module, a learning module, and a route determination module. The data collection module receives real time trip data corresponding to a moving asset from a remote location, and the static routing module determines candidate routes from a source to a destination for the moving asset. The orientation module is configured to gather publically available information associated with candidate routes, and the learning module is configured to generate a learned route database based on the publically available information from the orientation module and the real time trip data from the data collection module. The route determination module determines an optimized route for the moving asset based on the learned route database. The system further includes a communication interface configured to transmit an optimized route signal.

Claims

1. A dynamic routing system comprising:

a data collection module for receiving real time trip data corresponding to a moving asset from a remote location;

a static routing module for determining candidate routes from a source to a destination for the moving asset;

an orientation module configured to gather publically available information associated with candidate routes;

a learning module configured to generate a learned route database based on the publically available information from the orientation module and on the real time trip data from the data collection module;

a route determination module to determine an optimized route for the moving asset based on the learned route database; and

a communication interface configured to transmit an optimized route signal.

2. The system of claim 1, further comprising a satellite source or a non-satellite source for transmitting position data to the moving asset

3. The system of claim 2, wherein the satellite source comprises a global positioning satellite.

4. The system of claim 2, wherein the non-satellite source comprises a WiFi access point, cellular tower, or other fixed wireless nodes.

5. The system of claim 1, wherein the moving asset comprises a trailer, a railcar, an intermodal container, flatbed, refrigerated unit, a delivery van, a sales vehicle, emergency response vehicle or a passenger vehicle.

6. The system of claim 1, wherein the static routing module comprises a map database.

7. The system of claim 6, wherein the map database comprises Google Maps mapping service, MapQuest Inc. mapping service, Yahoo! Map mapping service, or Environmental Systems Research Institute (ESRI) mapping service.

8. The system of claim 1, wherein the optimized route comprises a route with least travel time or a route with least fuel prices.

9. The system of claim 1, wherein the publically available information may comprises at least one of traffic conditions, road conditions, weather conditions, road construction information, and fuel prices on the candidate routes.

10. The system of claim 1, wherein the learning module comprises a predictor configured to output a warning on candidate routes based on obvious and non-obvious events.

11. The system of claim 10, wherein obvious events comprise at least one of time of day, day of week, season, direction of travel, and weather.

12. The system of claim 11, wherein the predictor is configured to predict delay during a time of day depending on historical data.

13. The system of claim 10, wherein obvious events further comprise slow movement of another vehicle being monitored by the dynamic routing system.

14. The system of claim 13, wherein the learning module comprises a traffic jam or lane closure predictor when the obvious event is slow movement of another vehicle being monitored by the dynamic routing system.

15. The system of claim 10, wherein the non-obvious events comprise anti-lock braking system status of the moving asset, point of sale volume from a retailer center, shipment departure from a local distribution center, local community events, department store sale or event conditions, and concert or sporting event information.

16. The system of claim 15, wherein the predictor is configured to predict icy road conditions if the anti-lock braking system status is active and temperature is low or precipitation is high.

17. The system of claim 15, wherein the predictor is configured to predict traffic delays for non-obvious events comprising at least one of a shipment departure from a local distribution center or a point of sale volume from a retailer center.

18. The system of claim 1, wherein the learning module is configured to store and analyze historical trip data and to forecast traffic conditions based on the analysis of the historical trip data.

19. A method for identifying dynamic routing for a moving asset comprising:

receiving real time trip data corresponding to the moving asset;

determining candidate routes from a source to a destination for the moving asset;

obtaining publically available information associated with candidate routes;

generating a learned route database based on candidate routes and the publically available information; and

estimating an optimized route for the moving asset based on the learned route database.