DCGAN to model spatial and temporal distributions of real ride sharing data

DCGAN to model spatial and temporal distributions of real ride sharing data

Brad Stocks

Brad Stocks

, California

Developing a novel Generative Adversarial Network to synthesize data provided by Uber, Facebook and Emirates.

Artificial Intelligence

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Description

The system is designed to take a wide variety of data primarily driven by ride sharing data collected from major cities. Using this data the temporal and spatial distributions will be captured and synthesized using a novel DCGAN structure. The data generation will be guided by a spectrum of conditional, meta-data inputs that will allow for generation of previously unseen scenarios modeled on a variety of given parameters.

The work is still in progress and I am unable to share further details on a public forum.

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Brad S. created project DCGAN to model spatial and temporal distributions of real ride sharing data

Medium 31e1b0e8 c4b7 4e18 ace7 9cfc1fa37d3b

DCGAN to model spatial and temporal distributions of real ride sharing data

The system is designed to take a wide variety of data primarily driven by ride sharing data collected from major cities. Using this data the temporal and spatial distributions will be captured and synthesized using a novel DCGAN structure. The data generation will be guided by a spectrum of conditional, meta-data inputs that will allow for generation of previously unseen scenarios modeled on a variety of given parameters.

The work is still in progress and I am unable to share further details on a public forum.

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