Traffic Impact Prediction for Multiple Event Planning

• US 20150073688A1
• Filed: 09/09/2013
• Published: 03/12/2015
• Est. Priority Date: 09/09/2013
• Status: Active Grant

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Hampapur, Arun, He, Qing, Liu, Xuan, Xing, Songhua
• Granted Patent: US 9,047,767 B2
• Time in Patent Office: Days
• US Class Current: 701/118
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0145 for active traffic flow control

Abstract

Embodiments relate to traffic impact prediction in a transportation network. Link level background traffic demand in a transportation network may be estimated based on information about available routes, and based on expected background traffic volumes between origins and destinations. A background traffic flow model that optimizes a background flow of the expected background traffic volumes among the available routes to minimize a sum of background congestion costs, background path entropy, and errors between an observed background traffic flow and the optimized background flow may be applied. Alternative routes may be identified based on the available routes and event based control plans. Expected additional event based traffic volumes may be received. A link level total traffic demand in the transportation network may be estimated based on the expected additional event based traffic volumes, the identified alternative routes, and the estimated background traffic demand.

Claims

1. A method of traffic impact prediction, the method comprising:

estimating a link level background traffic demand in a transportation network, the estimating the link level background traffic demand including;

receiving information about available routes in the transportation network;

receiving expected background traffic volumes between origins and destinations in the transportation network; and

applying a background traffic flow model that optimizes a background flow of the expected background traffic volumes among the available routes to minimize a sum of background congestion costs, background path entropy, and errors between an observed background traffic flow and the optimized background flow;

identifying alternative routes for at least a subset of the estimated link level background traffic demand, the identifying based on the available routes in the transportation network and event based control plans;

receiving expected additional event based traffic volumes between the origins and the destinations in the transportation network;

estimating a link level total traffic demand in the transportation network based on the expected additional event based traffic volumes, the identified alternative routes, and the estimated link level background traffic demand; and

setting signal cycles of traffic signals in the transportation network based on the estimated link level total traffic demand.

2. The method of claim 1, wherein the observed background traffic flow is received from a subject matter expert (SME).

3. The method of claim 1, wherein the event based control plans include at least one of a variable message sign (VMS), a road closure, a turn restriction, and a detour.

4. The method of claim 1, wherein the identifying alternative routes includes event influenced estimated background traffic being assigned the identified alternative routes based on responsive rerouting.

5. The method of claim 4, wherein input to the responsive rerouting includes input from a SME.

6. The method of claim 1, wherein estimating the link level total traffic demand includes applying a total traffic demand model that optimizes a total flow of the expected background traffic volumes and the expected additional event based traffic volumes among the available routes and the identified alternative routes to minimize a sum of total congestion costs and total path entropy.

7. The method of claim 6, wherein the link level total traffic demand model further minimizes a deviation from the optimized background flow.

8. The method of claim 6 wherein the link level total traffic demand model further minimizes a deviation between observed turning ratios and estimated turning ratios.

9. The method of claim 8, wherein the observed turning ratios are received from a SME.

10. The method of claim 6, wherein the total traffic demand model takes into account spatial-temporal data that is estimated based on a total number of expected event attendees, an event start time, an event end time, and a location and capacity of at least one event parking lot.

11-15. -15. (canceled)

16. A computer program product for traffic impact prediction, the computer program product comprising:

a tangible non-transitory computer readable storage medium having program code embodied therewith, the program code executable by a computer to implement;

estimating a link level background traffic demand in a transportation network, the estimating the link level background traffic demand including;

receiving information about available routes in the transportation network;

receiving expected background traffic volumes between origins and destinations in the transportation network; and

applying a background traffic flow model that optimizes a background flow of the expected background traffic volumes among the available routes to minimize a sum of background congestion costs, background path entropy, and errors between an observed background traffic flow and the optimized background flow;

identifying alternative routes for at least a subset of the estimated link level background traffic demand, the identifying based on the available routes in the transportation network and event based control plans;

receiving expected additional event based traffic volumes between the origins and the destinations in the transportation network;

estimating a link level total traffic demand in the transportation network based on the expected additional event based traffic volumes, the identified alternative routes, and the estimated link level background traffic demand; and

setting signal cycles of traffic signals in the transportation network based on the estimated link level total traffic demand.

17. The computer program product of claim 16, wherein the observed background traffic flow is received from a subject matter expert (SME).

18. The computer program product of claim 16, wherein the identifying alternative routes includes event influenced estimated background traffic being assigned the identified alternative routes based on responsive rerouting that includes input from a SME.

19. The computer program product of claim 16, wherein estimating the link level total traffic demand includes applying a total traffic demand model that optimizes a total flow of the expected background traffic volumes and the expected additional event based traffic volumes among the available routes and the identified alternative routes to minimize a sum of total congestion costs, total path entropy, and a deviation from the optimized background flow.

20. The computer program product of claim 19, wherein the total traffic demand model takes into account spatial-temporal data that is estimated based on a total number of expected event attendees, an event start time, an event end time, and a location and capacity of at least one event parking lot.