APPARATUS AND METHOD FOR PREDICTING ARRIVAL TIMES IN A TRANSPORTATION NETWORK

• US 20140088865A1
• Filed: 09/27/2012
• Published: 03/27/2014
• Est. Priority Date: 09/27/2012
• Status: Abandoned Application

• Current Assignee: Siemens Industry Incorporated (Siemens AG)
• Original Assignee: Siemens Industry Incorporated (Siemens AG)
• Inventors: Thies, Eckart, Vang, Dang Neng
• Application Number:
  US13/628,210
• Publication Number:
  US 20140088865A1
• Time in Patent Office: Days
• US Class Current: 701/465
• CPC Class Codes: G01C 21/34 Route searching; Route guidance
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A scheduling system is provided for improving the routing performance of a plurality of objects moving through a transportation network. In one preferred application, a train scheduling system is improved by using an artificial neural network (ANN) to determine the trains'"'"' arrival times at various points in the network. Real-time data collection is minimized by training the ANN using stored data regarding historical, previously run train routes. A train'"'"'s particular physical characteristics are input to the ANN to predict the trains'"'"' arrival times at the particular destination within the network. Scheduling and routing functions may then be applied to the ANN output data to determine modified, train arrival times and to make routing changes to optimize the scheduling. Completed route information is continuously fed back to the ANN and the scheduling system to retrain the ANN, assimilate the latest route data and further optimize the system'"'"'s scheduling performance.

Claims

1. A computer-implemented system for predicting arrival times at nodes within a transport network for each of a plurality of objects moving between said nodes, said objects moving along travel routes between said nodes, said computer-implemented system having at least one computer including a processor and associated memory from which computer instructions are executed by said processor, said system comprising:

a tracking module for receiving real-time, location data indicating a physical location of each object and associating an object identifier with each object;

an object database, accessible by said tracking module, for storing historical tracking information including previous travel routes of objects within said network, said object database further storing said real-time location data and information regarding physical attributes of each of said objects;

an artificial neural network module associated with a travel route between an origination node and a destination node, said artificial neural network module being trained by said historical tracking information of objects including previous travel routes of objects travelling between said origination and destination nodes, said artificial neural network module accessing said object database and predicting an arrival time at said destination node of one of said objects travelling on said travel route based on said physical attributes of said object;

a scheduling module coupled to a schedule database, said schedule database storing said predicted arrival time and detecting deviations from an expected arrival time;

a routing system for executing routing instructions based on said detected deviations; and

a feed-back module for retraining said artificial neural network module based on a completed travel route of said object.

2. The system of claim 1 wherein said objects are trains, said object identifiers are train numbers, and said travel routes are segments of train track provided for said trains between said origination and destination nodes.

3. The system of claim 2 wherein said physical attributes include at least one of:

a locomotive type, a locomotive horsepower, a number of loaded train cars, and a train weight.

4. The system of claim 2 wherein said historical tracking information includes data regarding previously traversed train track segments for particular trains having particular physical attributes.

5. The system of claim 2 wherein said schedule database includes a plurality of train schedules, one for each of said trains, and said expected arrival times are arrival times of said trains at said destination nodes.

6. The system of claim 2 wherein said destination node is a train station or a railroad siding.

7. The system of claim 2 wherein said routing information includes at least one of track switch information and track signaling information.

8. The system of claim 2 wherein said artificial neural network weights said historical tracking information with said physical attributes of said trains in predicting said arrival times.

9. A method of predicting arrival times at nodes within a transport network for each of a plurality of objects moving between said nodes, said objects moving along travel routes between said nodes, said method performed using a computer-implemented system having at least one computer including a processor and associated memory from which computer instructions are executed by said processor, said method comprising:

receiving real-time, input data regarding physical locations of each of the plurality of objects;

associating an object identifier with each object;

accessing an object database to obtain historical tracking information regarding previous travel routes of objects between origination nodes and destination nodes and physical attributes of said objects;

predicting with an artificial neural network module an arrival time of one of said objects at a destination node based on said physical attributes, said artificial neural network being associated with a travel route between an origination node and said destination node;

detecting deviations between expected arrival times of said objects based and said predicted arrival times with a schedule of arrival times for said objects, said schedule of arrival times and said expected arrival times being stored in a schedule database;

executing routing instructions based on said detected deviations; and

modifying said schedule of arrival times based on said detected deviations.

10. The method of claim 9 further comprising:

training said artificial neural network using historical tracking information regarding previous travel routes of objects between said origination nodes and destination nodes.

11. The method of claim 10 wherein said step of training includes the step of weighting said physical attributes.

12. The method of claim 10 further comprising:

feeding back completed travel routes of said objects between said origination and destination nodes to said artificial neural network; and

retraining said artificial neural network.

13. The method of claim 9 further comprising:

scheduling a new route for said object based on said detected deviations.

14. The method of claim 9 wherein said objects are trains, said object identifiers are train numbers, and said travel routes are segments of train track provided for said trains between said origination and destination nodes.

15. A computer-implemented system for predicting an arrival time of an object at a destination node within a transport network of a moving objects, said object moving along a travel route between an origination node and said destination node, said computer-implemented system having at least one computer including a processor and associated memory from which computer instructions are executed by said processor, said system comprising:

an artificial neural network module associated with said travel route between said origination node and said destination node, said artificial neural network module being trained by historical tracking information including previous travel routes of objects travelling between said origination and destination nodes, said artificial neural network predicting an arrival time at said destination node of said object based on physical attributes of said object.

16. The system of claim 15 wherein said many objects are trains, said object identifiers are train numbers, and said travel routes are segments of train tracks provided for said trains between said origination and destination nodes.

17. The system of claim 16 wherein said physical attributes include at least one of a locomotive type, a locomotive horsepower, a number of loaded train cars, and a train weight.

18. The system of claim 15 wherein said historical tracking information includes data regarding previously traversed train track segments for particular trains having particular physical attributes.

19. The system of claim 15 wherein said destination node is a train station or a railroad siding.