METHODS AND SYSTEMS FOR ADJUSTING COMPENSATION FOR TASKS
    3.
    发明申请
    METHODS AND SYSTEMS FOR ADJUSTING COMPENSATION FOR TASKS 审中-公开
    调整任务补偿的方法和系统

    公开(公告)号:US20140372163A1

    公开(公告)日:2014-12-18

    申请号:US13919206

    申请日:2013-06-17

    CPC classification number: G06Q10/06312

    Abstract: Method and system for dynamically adjusting compensation for one or more tasks are disclosed. The method includes estimating the compensation for each task in a batch of tasks based on at least one of a minimum wage for a task in the batch of tasks, one or more attributes associated with the worker, a number and type of tasks in the batch of tasks, or a target level of service. The compensation for the each task of the batch of tasks is then adjusted based on at least one of an observed level of service associated with the batch of tasks and the target level of service. The method is performed using a processor.

    Abstract translation: 公开了一种或多种任务的动态调整补偿方法和系统。 所述方法包括基于所述批次任务中的任务的最小工资,与所述工作人员相关联的一个或多个属性,所述批处理中的任务的数量和类型中的至少一个来估计一批任务中的每个任务的所述补偿 任务或目标服务级别。 然后,基于与该批任务相关联的所观察到的服务水平和目标服务水平中的至少一个来对该批任务的每个任务的补偿进行调整。 该方法使用处理器执行。

    Methods, systems and processor-readable media for optimizing intelligent transportation system strategies utilizing systematic genetic algorithms
    5.
    发明授权
    Methods, systems and processor-readable media for optimizing intelligent transportation system strategies utilizing systematic genetic algorithms 有权
    使用系统遗传算法优化智能交通系统策略的方法,系统和处理器可读介质

    公开(公告)号:US09183742B2

    公开(公告)日:2015-11-10

    申请号:US13661856

    申请日:2012-10-26

    Abstract: Methods, systems and processor-readable media for modeling and optimizing multiple ITS (Intelligent Transportation System) strategies utilizing a systematic genetic algorithm. A traffic simulation model can be configured in conjunction with a genetic algorithm based optimization engine for optimizing the transportation models. An origin-destination matrix that minimizes discrepancies between a simulated and an observed link traffic count can be estimated by considering a road network and a traffic count with respect to a region. A driver behavior can then be determined utilizing the origin-destination matrix via calibration so that the simulation model can replicate a freeway traffic flow in the region. An optimal parameter with respect to the ITS strategies can be determined to optimize a set goal with respect to a given constraint. Such an approach meets a level of service (LOS) metric as well as a revenue target under the applied ITS strategies.

    Abstract translation: 方法,系统和处理器可读介质,用于建模和优化利用系统遗传算法的多个ITS(智能交通系统)策略。 交通仿真模型可以与基于遗传算法的优化引擎配合,优化运输模型。 可以通过考虑道路网络和相对于区域的交通数量来估计最小化模拟链路和观测链路业务量之间的差异的起始 - 目的地矩阵。 然后可以通过校准利用起始 - 目的地矩阵来确定驾驶员行为,使得模拟模型可以复制该区域中的高速公路交通流。 可以确定相对于ITS策略的最佳参数以优化关于给定约束的设定目标。 这种方法符合服务水平(LOS)指标以及应用ITS策略下的收入目标。

    METHODS, SYSTEMS AND PROCESSOR-READABLE MEDIA FOR OPTIMIZING INTELLIGENT TRANSPORTATION SYSTEM STRATEGIES UTILIZING SYSTEMATIC GENETIC ALGORITHMS
    7.
    发明申请
    METHODS, SYSTEMS AND PROCESSOR-READABLE MEDIA FOR OPTIMIZING INTELLIGENT TRANSPORTATION SYSTEM STRATEGIES UTILIZING SYSTEMATIC GENETIC ALGORITHMS 有权
    使用系统遗传算法优化智能交通系统策略的方法,系统和处理器可读介质

    公开(公告)号:US20140122032A1

    公开(公告)日:2014-05-01

    申请号:US13661856

    申请日:2012-10-26

    Abstract: Methods, systems and processor-readable media for modeling and optimizing multiple ITS (Intelligent Transportation System) strategies utilizing a systematic genetic algorithm. A traffic simulation model can be configured in conjunction with a genetic algorithm based optimization engine for optimizing the transportation models. An origin-destination matrix that minimizes discrepancies between a simulated and an observed link traffic count can be estimated by considering a road network and a traffic count with respect to a region. A driver behavior can then be determined utilizing the origin-destination matrix via calibration so that the simulation model can replicate a freeway traffic flow in the region. An optimal parameter with respect to the ITS strategies can be determined to optimize a set goal with respect to a given constraint. Such an approach meets a level of service (LOS) metric as well as a revenue target under the applied ITS strategies.

    Abstract translation: 方法,系统和处理器可读介质,用于建模和优化利用系统遗传算法的多个ITS(智能交通系统)策略。 交通仿真模型可以与基于遗传算法的优化引擎配合,优化运输模型。 可以通过考虑道路网络和相对于区域的交通数量来估计最小化模拟链路和观测链路业务量之间的差异的起始 - 目的地矩阵。 然后可以通过校准利用起始 - 目的地矩阵来确定驾驶员行为,使得模拟模型可以复制该区域中的高速公路交通流。 可以确定相对于ITS策略的最佳参数以优化关于给定约束的设定目标。 这种方法符合服务水平(LOS)指标以及应用ITS策略下的收入目标。

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