Traffic prediction using real-world transportation data

• US 9,286,793 B2
• Filed: 10/22/2013
• Issued: 03/15/2016
• Est. Priority Date: 10/23/2012
• Status: Active Grant

• Current Assignee: University of Southern California
• Original Assignee: University of Southern California
• Inventors: Pan, Bei, Demiryurek, Ugur, Shahabi, Cyrus
• Primary Examiner(s): Khatib, Rami
• Assistant Examiner(s): MERLINO, DAVID P
• Application Number:
  US14/060,360
• Publication Number:
  US 20140114556A1
• Time in Patent Office: 875 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination

Abstract

Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data'"'"'s intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.

Claims

1. A method of predicting traffic on a road network in view of an event having an identified time and an identified location in the road network, the method comprising:

retrieving attributes from past events on the road network;

selecting, by a processor, a subset of the attributes that are correlated with traffic parameters comprising delayed traffic speeds, affected backlogs of vehicles, and amounts of time needed to clear backlogs of vehicles;

discovering, by a processor, corresponding values for the traffic parameters under all combinations of the selected attributes;

matching, by a processor, current attributes for the event in the road network to previous event attributes using the corresponding values for the traffic parameters to identify a subset of the past events;

using, by a processor, the identified time, the identified location, and the subset of the past events to predict (i) a delayed traffic speed for the event, (ii) an affected backlog of vehicles on one or more roads approaching the event in the road network, and (iii) an amount of time needed for the affected backlog of vehicles to be cleared in the road network; and

providing prediction data to a navigation device for the road network, the prediction data comprising (i) the delayed traffic speed for the event, (ii) the affected backlog of vehicles, and (iii) the amount of time needed for the affected backlog of vehicles to be cleared in the road network.

2. The method of claim 1, wherein the selected attributes comprise (i) start time, (ii) location, (iii) direction, (iv) event type, and (v) affected lanes.

3. The method of claim 1, wherein the past events comprise accidents, vehicle breakdowns, scheduled or unscheduled road closures or construction, emergencies, and social events, including concert and sporting events.

4. The method of claim 1, comprising predicting traffic on the road network using previously recorded traffic data comprising data derived from mobile sensor data.

5. The method of claim 4, comprising generating the derived data by performing operations comprising:

calculating speeds for multiple mobile sensors from mobile sensor data with respect to connected road segments in a road network; and

generating a speed for a road segment of the connected road segments by calculating an aggregation of all speeds calculated for mobile sensors passing the road segment at a given time.

6. The method of claim 5, wherein the mobile sensor data is obtained from public transit vehicles.

7. The method of claim 1, wherein the navigation device comprises a mobile phone.