Traffic forecasting employing modeling and analysis of probabilistic interdependencies and contextual data

• US 20060106530A1
• Filed: 06/30/2005
• Published: 05/18/2006
• Est. Priority Date: 11/16/2004
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric J., Apacible, Johnson T., Sarin, Raman K.
• Granted Patent: US 7,698,055 B2
• 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

Systems and methods are described for constructing predictive models, based on statistical machine learning, that can make forecasts about traffic flows and congestions, based on an abstraction of a traffic system into a set of random variables, including variables that represent the amount of time until there will be congestion at key troublespots and the time until congestions will resolve. Observational data includes traffic flows and dynamics, and other contextual data such as the time of day and day of week, holidays, school status, the timing and nature of major gatherings such as sporting events, weather reports, traffic incident reports, and construction and closure reports. The forecasting methods are used in alerting, the display graphical information about predictions about congestion on desktop on mobile devices, and in offline and real-time automated route recommendations and planning.

Claims

1. A system that facilitates communicating, visualizing, or alerting about traffic patterns, comprising:

a predictive model component that generates predictions relating to traffic parameters; and

an interface component that graphically outputs traffic parameters based at least in part upon the generated predictions.

2. The system of claim 1, the predictive model component generates predictions as a function of received contextual data.

3. The system of claim 2, the contextual data received by way of one or more of a camera, a sensor in a roadway, a GPS system, monitoring hand-shaking between cellular phones and cellular towers, the Internet, a television broadcast, and a radio broadcast, and the contextual data comprises at least one of time of day, day of week, holiday status, season, school status, weather properties, data relating to sporting events, data relating to parades, data relating to movies, data relating to political events, ongoing free text from a traffic monitoring department, and coded reports from a traffic monitoring department.

4. The system of claim 2, the predictive model component employs one or more of current status of traffic;

marginal or mean traffic flow expectations based on one or more of time of day, day of week, and current traffic information; and

probabilistic forecasts about future traffic flows in different regions to guide creation of route plans in an attempt to minimize total driving time.

5. The system of claim 4, GPS data is utilized to locate a user and to provide information on traffic at a current location and a future location.

6. The system of claim 1, the interface component existent upon one or more of a laptop, a personal digital assistant, a cellular phone, a smart phone, an automobile, and a desktop computer.

7. The system of claim 1, the predictions relating to traffic parameters are associated with one or more of time until congestions appear and time until congestions will clear at each of a set of at least one of identified troublespots and bottleneck regions of a road system.

8. The system of claim 1, the predictive model component is built by way of a statistical method, the statistical method is one of a Bayesian network, a dynamic Bayesian network, a continuous time Bayesian network, a Hidden Markov Model, a Markov process, particle filtering, a Gibbs-sampling based approach, a neural network, a support vector machine, one or more differential equations, a logic-based reasoning system, and a fuzzy logic-centric method.

9. The system of claim 1, further comprising a model analyzer component that compares outputs predicted by the predictive model component with actual events, builds a case library of predicted situations and their accuracy, and uses the library to tune and/or annotate base level inferences so as to relay a reliability of the base level inference.

10. The system of claim 1, a case library of accuracies of predictions is used to build via statistical machine learning procedures a separate predictive model that can predict in real-time reliability of the predictive model component based at least in part on available contextual information.

11. The system of claim 1, further comprising a bottleneck identification tool that identifies regions within a traffic system associated with at least one of cyclic congestions, frequent congestions, and largest duration congestions within a traffic system via exploring statistics of congestion.

12. The system of claim 1, the interface component provides a user with a mechanism for at least one of customizing congestion levels associated with a prediction relating to traffic parameters;

selecting regions and labeling the selected regions as important regions;

selecting sets of regions that comprise routes of interest to the user by selection of regions;

linking selected regions to times of day that the regions are to be monitored; and

selecting a type of alert to associated with the regions and times of day.

13. The system of claim 1, contextual data received and analyzed by the predictive model component utilized to improve the predictive model component.

14. A computer-readable medium comprising the system of claim 1.

15. A method for predicting traffic patterns, comprising:

generating a representation of a plurality of roadways;

providing a predictive model that predicts events with respect to traffic patterns upon the plurality of roadways; and

graphically displaying the predicted events.

16. The method of claim 15, further comprising:

receiving contextual data relating to the plurality of roadways; and

generating the predictions as a function of the received contextual data.

17. The method of claim 16, further comprising

graphically displaying an icon that indicates at least one of a time until a region is clear and a time until an open region will jam;

and

graphically displaying a graphical feature to indicate occurrence of a surprising event.

18. The method of claim 15, further comprising reasoning about and among random variables that represent time until congestion and time until clear for sets of at least one of troublesome traffic hotspots and bottleneck regions in a traffic system.

19. The method of claim 15, further comprising:

comparing output of the predictive model with actual events; and

automatically tuning the predictive model as a function of the comparison.

20. A traffic pattern prediction system, comprising:

means for predicting traffic parameters;

means for determining which of the predicted traffic parameters are of interest to a user; and

means for alerting the user of the traffic parameters that are of interest to the user.