Traffic impact prediction for multiple event planning

• US 9,171,462 B2
• Filed: 10/10/2013
• Issued: 10/27/2015
• Est. Priority Date: 09/09/2013
• Status: Expired due to Fees

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: GlobalFoundries, Inc.
• Inventors: Hampapur, Arun, He, Qing, Liu, Xuan, Xing, Songhua
• Primary Examiner(s): CAMBY, RICHARD M
• Application Number:
  US14/050,390
• Publication Number:
  US 20150073689A1
• Time in Patent Office: 747 Days
• Field of Search: 701/117, 701/118, 701/119, 701/120, 701/300, 701/408, 701/420, 701/423, 701/451, 340/906, 340/907, 340/910, 340/911, 340/915, 340/917, 340/991, 340/998
• US Class Current: 1/1
• 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 system for traffic impact prediction, the system comprising:

a memory having computer readable computer instructions, the memory comprising a tangible non-transitory computer readable medium; and

a processor for executing the computer readable computer instructions to perform a 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

outputting the estimated link level total traffic demand to set signal cycles of traffic signals in the transportation network.

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

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

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

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

10. The system 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.