Abstract: An exemplary method includes determining whether real-time vehicle location information deviates from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method further includes generating scheduling information for a user based on user data and the public transportation data, determining that the real-time vehicle location information deviates from at least one of the historical real-time vehicle location information or the scheduled vehicle location information by more than a threshold deviation, and updating the scheduling information for the user based on the user data, the public transportation data, and the determined deviation.

Abstract: Computer-implemented methods for scoring and ranking transit stops and transit lines are provided. In one aspect, a method includes determining data and factors associated with a first transit stop or line and calculating a score for the first transit stop or line. The data and factors used in the scoring calculation may be a combination of the number of vehicle departures from the transit stop, the number of transit lines connected to the transit stop, a point of interest proximity factor, a population center factor, the number of trips on the transit line, the number of transit stops on the transit line, the number of interconnected transit lines and the travel distance of the transit line. The transit stop and transit lines scores may be iterated over each other. Systems and machine-readable media for scoring and ranking transit stops and transit lines are also provided.

Abstract: Methods, systems, and apparatus, including executable instructions encoded on one or more machine-readable storage media, for correlating transportation data are described. A transportation schedule that includes planned records for first trips and associations between the first trips and corresponding first trip IDs may be accessed. A transportation status update that includes actual records for second trips and associations between the second trips and corresponding second trip IDs may be received. Information relating one or more of the first trip IDs to one or more of the second trip IDs may be obtained. The information may be used to identify a trip that is common to both the transportation schedule and the transportation status update. An update for the trip using the transportation status update may be provided.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: An exemplary method includes determining whether real-time vehicle location information deviates from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method further includes generating scheduling information for a user based on user data and the public transportation data, determining that the real-time vehicle location information deviates from at least one of the historical real-time vehicle location information or the scheduled vehicle location information by more than a threshold deviation, and updating the scheduling information for the user based on the user data, the public transportation data, and the determined deviation.

Abstract: An exemplary method includes determining whether real-time vehicle location information deviates from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method further includes generating a schedule degradation mode based on a deviation of real-time vehicle location information from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method also includes generating first scheduling information for a user based on the user data and public transportation data when the deviation does not exceed a threshold. The exemplary method farther includes generating second scheduling information, different from the first scheduling information, that includes an indication of the schedule degradation mode for the user based on user data, public transportation data, and the deviation when the deviation exceeds the threshold.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Methods and systems for route summarization are described herein. The method includes generating a route from an origin to a destination, the route including a plurality of maneuvers. One or more maneuvers are removed from the plurality of maneuvers. A summarized route is generated that reflects the removal of the one or more maneuvers from the plurality of maneuvers such that the summarized route has fewer maneuvers than the route.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Transportation information systems and methods are disclosed, which facilitate the use of transportation, including trip planning. An exemplary method includes receiving user data associated with public transportation usage. The exemplary method also includes receiving public transportation data including real-time public transportation data. The exemplary method further includes generating scheduling information based on the user data and real-time public transportation data.

Abstract: Methods and systems for route summarization are described herein. The method includes generating a route from an origin to a destination, the route including a plurality of maneuvers. One or more maneuvers are removed from the plurality of maneuvers based, at least in part, upon geographic information about a selected user. A summarized route is generated that reflects the removal of the one or more maneuvers from the plurality of maneuvers such that the summarized route has fewer maneuvers than the route.

Abstract: Transportation information systems and methods are disclosed, which facilitate the use of transportation, including trip planning. An exemplary method includes generating first scheduling information for a first user based on first user data, public transportation data, a first starting location, and a first destination location. The exemplary method also includes determining that a second user starting from a second starting location different from the first starting location would like to join the first user. The exemplary method further includes generating second scheduling information for the second user to meet the first user based on second user data, public transportation data, the second starting location, real-time location information for the first user, and a second destination location.

Abstract: An exemplary method includes determining whether real-time vehicle location information deviates from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method further includes generating a schedule degradation mode based on a deviation of real-time vehicle location information from at least one of historical real-time vehicle location information or scheduled vehicle location information. The exemplary method also includes generating first scheduling information for a user based on the user data and public transportation data when the deviation does not exceed a threshold. The exemplary method farther includes generating second scheduling information, different from the first scheduling information, that includes an indication of the schedule degradation mode for the user based on user data, public transportation data, and the deviation when the deviation exceeds the threshold.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Methods, systems, and apparatus, including executable instructions encoded on one or more machine-readable storage media, for correlating transportation data are described. A transportation schedule that includes planned records for first trips and associations between the first trips and corresponding first trip IDs may be accessed. A transportation status update that includes actual records for second trips and associations between the second trips and corresponding second trip IDs may be received. Information relating one or more of the first trip IDs to one or more of the second trip IDs may be obtained. The information may be used to identify a trip that is common to both the transportation schedule and the transportation status update. An update for the trip using the transportation status update may be provided.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: A method in a computerized system including an image sensor mounted in a moving vehicle. The image sensor captures image frames consecutively in real time. In one of the image flames, a spot is detected of measurable brightness; the spot is matched in subsequent image frames. The image frames are available for sharing between the computerized system and another vehicle control system. The spot and the corresponding spot are images of the same object. The object is typically one or more of headlights from an oncoming vehicle, taillights of a leading vehicle, streetlights, street signs and/or traffic signs. Data is acquired from the spot and from the corresponding spot. By processing the data, the object (or spot) is classified. producing an object classification. The vehicle control system controls preferably headlights of the moving vehicle based on the object classification.