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    TRAFFIC OBSTRUCTION DETECTION
    6.
    发明申请
    TRAFFIC OBSTRUCTION DETECTION 审中-公开
    交通障碍检测

    公开(公告)号：US20170076227A1

    公开(公告)日：2017-03-16

    申请号：US15122750

    申请日：2015-02-27

    Applicant: INRIX INC.,

    Inventor： Judith Rosalyn Elgie ,  Dominic Jordan

    IPC: G06N99/00 ,  G08G1/01

    Abstract: One or more techniques and/or systems are provided for training and/or utilizing a traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. For example, a training dataset, comprising sample vehicle location points (e.g., global positioning system location points of vehicles) and traffic obstruction identification labels (e.g., locations of known traffic obstructions such as stop signs, crosswalks, stop lights, etc.), may be evaluated to extract a set of training features indicative of traffic flow patterns. The set of training features and the traffic obstruction identification labels may be used to train a traffic obstruction identification model to create a trained traffic obstruction identification model. The trained traffic obstruction identification model may be used to determine whether a road segment has a traffic obstruction or not.

    Abstract translation: 提供一种或多种技术和/或系统用于基于车辆位置点数据来训练和/或利用用于识别交通障碍物的交通障碍物识别模型。 例如，训练数据集包括车辆位置点（例如，车辆的全球定位系统位置点）和交通障碍物识别标签（例如，已知交通障碍物的位置，例如停车标志，人行横道，停车灯等） 可以被评估以提取一组指示交通流模式的训练特征。 训练特征和交通阻塞识别标签可用于训练交通阻塞识别模型，以创建训练有素的交通阻塞识别模型。 可以使用经过训练的交通障碍物识别模型来确定道路段是否具有交通阻塞。

    TARGETED ADVERTISEMENTS FOR TRAVEL REGION DEMOGRAPHICS
    8.
    发明申请
    TARGETED ADVERTISEMENTS FOR TRAVEL REGION DEMOGRAPHICS 审中-公开
    针对旅游地区人口统计学的广告

    公开(公告)号：US20140279012A1

    公开(公告)日：2014-09-18

    申请号：US13843904

    申请日：2013-03-15

    Applicant: INRIX Inc.

    Inventor： Christopher L. Scofield ,  Dominic Jordan ,  Uri Lavee ,  Timothy David McHugh ,  Kevin James Foreman ,  William Schwebel ,  Kenneth Kranseler

    IPC: G06Q30/02

    CPC classification number: G06Q30/0269 ,  G06Q30/0261

    Abstract: For an advertisement opportunity near a travel region, advertisements may be selected that are targeted to individuals who are likely to view the advertisement. However, travel patterns among individuals sharing particular traits may exist that facilitate targeted advertising, but may be non-intuitive and therefore difficult to predict, and other techniques, such as population surveys, may be costly and inaccurate. Presented herein are techniques for automatically evaluating travel patterns by tracking the routes of particular individuals, and inferring demographics for such individuals based on the locations of their routes (e.g., an individual whose route frequently includes a residence may be presumed to share the population demographics of the residential neighborhood). Extrapolating such individual demographics may enable inference of shared demographics at particular advertisement opportunities (e.g., among travelers who frequently travel on a particular road at a particular time of day) and the selection of advertisements more closely targeting such individuals.

    Abstract translation: 对于旅游区域附近的广告机会，可以选择针对可能观看广告的个人的广告。 然而，可能存在共享特定特征的个体之间的旅行模式，其促进有针对性的广告，但是可能是非直观的，因此难以预测，并且诸如人口调查的其他技术可能是昂贵的和不准确的。 这里提出的是通过跟踪特定个人的路线来自动评估旅行模式的技术，并且基于他们的路线的位置来推断这些个人的人口统计学（例如，其经常包括住所的路线可以被推定为共享人口统计学 住宅区）。 外推这种个人人口特征可以使特定广告机会（例如，在特定时间内经常在特定道路上旅行的旅行者中）的共享人口统计特征以及更紧密地针对这样的个人的广告的选择。

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