System and method for generating an occupancy model
First Claim
1. A computer implemented method comprising:
- receiving occupancy data for each of a plurality of zones, each zone comprising a respective set of cells and including a sensor for each of the cells in the set of cells, each of the cells being in one of a plurality of occupancy states at a time, the occupancy states including an occupied state and an unoccupied state, each of the sensors being in only one of a reporting state, in which the sensor reports the occupancy state of the respective cell, and a non-reporting state, in which the sensor does not report the occupancy state of the cell, at a time, the occupancy data comprising, for each of a plurality of times;
an observed occupancy, which is based on a number of cells in the respective zone that are reported by the sensors to be in the occupied state, anda number of the sensors in the respective zone that are in the reporting state, at least one of the sensors of at least some of the zones being in the non-reporting state for at least one of the plurality of times;
with a processor, learning at least one predictive occupancy model configured for predicting an occupancy of at least one of the zones, the learning being based on at least a portion of the received occupancy data, at least one of the at least one predictive occupancy models including a sensor noise model which assumes that a probability of a sensor being in the reporting state is dependent on whether the respective cell is in the occupied state.
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Abstract
A system and method for generating an occupancy model are disclosed. The model is learned using occupancy data for zones, each zone including cells, which are occupied or not at a given time, each with a sensor, which may be reporting or not. The data provides an observed occupancy corresponding to a number of cells in the respective zone which have reporting sensors, and the number of those sensors which are reporting that the respective cell is occupied. The occupancy model is based on a demand model and a sensor noise model which accounts for behavior of the non-reporting sensors. The noise model assumes that the probability of a sensor being in the reporting state is dependent on whether the respective cell is occupied or not. The model can fit the occupancy data better than one which assumes that non-reporting cells are occupied with the same frequency as reporting ones.
13 Citations
25 Claims
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1. A computer implemented method comprising:
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receiving occupancy data for each of a plurality of zones, each zone comprising a respective set of cells and including a sensor for each of the cells in the set of cells, each of the cells being in one of a plurality of occupancy states at a time, the occupancy states including an occupied state and an unoccupied state, each of the sensors being in only one of a reporting state, in which the sensor reports the occupancy state of the respective cell, and a non-reporting state, in which the sensor does not report the occupancy state of the cell, at a time, the occupancy data comprising, for each of a plurality of times; an observed occupancy, which is based on a number of cells in the respective zone that are reported by the sensors to be in the occupied state, and a number of the sensors in the respective zone that are in the reporting state, at least one of the sensors of at least some of the zones being in the non-reporting state for at least one of the plurality of times; with a processor, learning at least one predictive occupancy model configured for predicting an occupancy of at least one of the zones, the learning being based on at least a portion of the received occupancy data, at least one of the at least one predictive occupancy models including a sensor noise model which assumes that a probability of a sensor being in the reporting state is dependent on whether the respective cell is in the occupied state. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. An occupancy prediction system comprising:
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memory which receives occupancy data for each of a plurality of zones, each zone comprising a respective set of cells and including a sensor for each of the cells in the set of cells, each of the cells being in one of an occupied state and an unoccupied state at a time, each of the sensors being in one of a reporting state, in which the sensor reports the occupancy state of the respective cell, and a non-reporting state, in which the sensor does not report the occupancy state of the cell, at a time, the occupancy data comprising; an observed occupancy, which is based on a number of cells in the respective zone that are reported to be in the occupied state, and a number of the sensors in the respective zone that are in the reporting state; at least one occupancy model stored in memory which is configured for predicting occupancy of at least one of the zones, the predicted occupancy including a predicted occupancy of cells in the at least one zone which have a sensor in the non-reporting state, each of the at least one occupancy models being based on; a demand model which predicts occupancy of each zone as a function of a capacity of the zone and an occupancy rate, the capacity of each zone being based on a total number of the cells in the respective set of cells; and a respective sensor noise model, at least one of the sensor noise models assuming that a probability of a sensor being in the reporting state is dependent on whether the respective cell is in the occupied state or the unoccupied state. - View Dependent Claims (22, 23, 24)
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25. A computer implemented method comprising:
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providing at least one occupancy model which is configured for predicting occupancy of at least one of a plurality of zones, each zone comprising a respective set of cells and including a sensor for each of the cells in the set of cells, each of the cells being in one of an occupied state and an unoccupied state at a time, each of the sensors being in one of a reporting state and a non-reporting state at a time, the predicted occupancy including a predicted occupancy of cells in the at least one zone which have a sensor in the non-reporting state, each of the at least one occupancy models being based on a demand model and a respective sensor noise model jointly learned from prior occupancy data acquired for the at least one zone, at least one of the noise models assuming that a probability of a sensor being in the reporting state is dependent on whether the respective cell is in the occupied state, the demand model predicting occupancy of each zone as a function of a capacity of the zone and an occupancy rate, the capacity of each zone being based on a total number of the cells in the respective set of cells; with a computer system, receiving current occupancy data for at least one of the plurality of zones, the current and prior occupancy data comprising; an observed occupancy, which is based on a number of cells in the respective zone that are reported to be in the occupied state, and a number of the sensors in the respective zone that are in the reporting state, wherein at least one of the sensors is in the non-reporting state and the occupancy state of the at least one sensor in the non-reporting state is not known by the computer system; and with a processor of the computer system, predicting an occupancy of the at least one of the zones using at least one of the at least one occupancy models.
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Specification