Patents by Inventor Ugur Demiryurek

Ugur Demiryurek has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20190370922
    Abstract: Methods, systems, and apparatus for matching a driver associated with a driver timetable and a rider associated with a ride request including a detected rider location and a rider destination. The system includes a synchronizing server configured to transmit the ride request to a driver device. The driver device is configured to determine a driver value associated with incorporating the ride request into the driver timetable. The driver device is also configured to communicate, to the synchronizing server, the driver value. The synchronizing server is also configured to receive one or more other values from one or more other driver devices. The synchronizing server is also configured to determine a prime value from the one or more values, the prime value being the driver value. The synchronizing server is also configured to communicate, to the driver device, an indication to incorporate the ride request into the driver timetable.
    Type: Application
    Filed: October 27, 2017
    Publication date: December 5, 2019
    Inventors: Mohammad Asghari, Cyrus Shahabi, Ugur Demiryurek, Dingxiong Deng
  • Publication number: 20190180612
    Abstract: A method for traffic prediction of a road network includes receiving past traffic information corresponding to multiple locations on the road network. The method further includes determining, by a processor and based on the past traffic information, temporal characteristics of the past traffic information corresponding to changes of characteristics over time and spatial characteristics of in the past traffic information corresponding to interactions between locations on the road network. The method further includes predicting predicted traffic information corresponding to a later time based on the determined temporal and spatial characteristics of the past traffic information. The method further includes receiving detected additional traffic information corresponding to the later time.
    Type: Application
    Filed: August 14, 2017
    Publication date: June 13, 2019
    Inventors: Ugur Demiryurek, Dingxiong Deng, Cyrus Shahabi, Linhong Zhu, Rose Yu, Yan Liu
  • Patent number: 9996798
    Abstract: Systems and techniques for enhancing accuracy of traffic prediction include a system of one or more computers operable to receive a request relating to traffic prediction, compare a first prediction error for a first (moving average) traffic prediction model with a second prediction error for a second (historical average) traffic prediction model, calculated using a historical data set selected from previously recorded traffic data in accordance with a day and time associated with the request, select use of the first model or the second model based on the comparison of prediction errors, and provide an output for use in traffic prediction, wherein the output comes from applying the first traffic prediction model when the first prediction error is less than the second prediction error, and the output comes from applying the second traffic prediction model when the first prediction error is not less than the second prediction error.
    Type: Grant
    Filed: March 9, 2016
    Date of Patent: June 12, 2018
    Assignee: University of Southern California
    Inventors: Bei Pan, Ugur Demiryurek, Cyrus Shahabi
  • Patent number: 9501509
    Abstract: The present disclosure relates to a short-lived throwaway index structure for generating an index from scratch in a short period of time rather than updating an index with every location change of moving objects. Rapid index construction results from the generation of Voronoi diagrams in parallel using multiple cloud servers simultaneously.
    Type: Grant
    Filed: September 10, 2014
    Date of Patent: November 22, 2016
    Assignee: UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Afsin Akdogan, Cyrus Shahabi, Ugur Demiryurek
  • Publication number: 20160189044
    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%.
    Type: Application
    Filed: March 9, 2016
    Publication date: June 30, 2016
    Inventors: Bei Pan, Ugur Demiryurek, Cyrus Shahabi
  • Patent number: 9286793
    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%.
    Type: Grant
    Filed: October 22, 2013
    Date of Patent: March 15, 2016
    Assignee: University of Southern California
    Inventors: Bei Pan, Ugur Demiryurek, Cyrus Shahabi
  • Publication number: 20150248450
    Abstract: The present disclosure relates to a short-lived throwaway index structure for generating an index from scratch in a short period of time rather than updating an index with every location change of moving objects. Rapid index construction results from the generation of Voronoi diagrams in parallel using multiple cloud servers simultaneously.
    Type: Application
    Filed: September 10, 2014
    Publication date: September 3, 2015
    Applicant: UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Afsin Akdogan, Cyrus Shahabi, Ugur Demiryurek
  • Patent number: 9062985
    Abstract: The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
    Type: Grant
    Filed: October 21, 2013
    Date of Patent: June 23, 2015
    Assignee: University of Southern California
    Inventors: Ugur Demiryurek, Cyrus Shahabi, Farnoush Banaei-Kashani
  • Publication number: 20140114556
    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%.
    Type: Application
    Filed: October 22, 2013
    Publication date: April 24, 2014
    Applicant: UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Bei Pan, Ugur Demiryurek, Cyrus Shahabi
  • Patent number: 8660789
    Abstract: With real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. To speed up the path computation, exact and approximate techniques for computation of the fastest path in time-dependent spatial networks are presented. An exact fastest path computation technique based on a time-dependent A* search can significantly improve the computation time and storage complexity of existing approaches. Moreover, for applications with which approximate fastest path is acceptable, the approximate fastest path computation technique can improve the computation time by an order of magnitude while maintaining high accuracy (e.g., with only 7% increase in travel-time of the computed path on average). With experiments using real data-sets (including a variety of large spatial networks with real traffic data) the efficacy of the disclosed techniques for online fastest path computation is demonstrated.
    Type: Grant
    Filed: April 24, 2012
    Date of Patent: February 25, 2014
    Assignee: University of Southern California
    Inventors: Ugur Demiryurek, Cyrus Shahabi
  • Publication number: 20140046593
    Abstract: The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
    Type: Application
    Filed: October 21, 2013
    Publication date: February 13, 2014
    Applicant: UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Ugur Demiryurek, Cyrus Shahabi, Farnoush Banaei-Kashani
  • Patent number: 8566030
    Abstract: The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
    Type: Grant
    Filed: October 20, 2011
    Date of Patent: October 22, 2013
    Assignee: University of Southern California
    Inventors: Ugur Demiryurek, Cyrus Shahabi, Farnoush Banaei-Kashani
  • Publication number: 20120283948
    Abstract: With real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. To speed up the path computation, exact and approximate techniques for computation of the fastest path in time-dependent spatial networks are presented. An exact fastest path computation technique based on a time-dependent A* search can significantly improve the computation time and storage complexity of existing approaches. Moreover, for applications with which approximate fastest path is acceptable, the approximate fastest path computation technique can improve the computation time by an order of magnitude while maintaining high accuracy (e.g., with only 7% increase in travel-time of the computed path on average). With experiments using real data-sets (including a variety of large spatial networks with real traffic data) the efficacy of the disclosed techniques for online fastest path computation is demonstrated.
    Type: Application
    Filed: April 24, 2012
    Publication date: November 8, 2012
    Applicant: UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Ugur Demiryurek, Cyrus Shahabi