Generating Realistic Ride-Hailing Datasets Using GANs

Abstract

This article focuses on the synthetic generation of human mobility data in urban areas. We present a novel application of generative adversarial networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride-sharing/hailing services from four major cities to train our GANs model. Our model captures the spatial and temporal variability of the ride request patterns observed for all four cities over a typical week. Previous works have characterized the spatial and temporal properties of human mobility datasets using the fractal dimensionality and the densification power law , respectively, which we utilize to validate our GANs-generated synthetic datasets. We also validate the synthetic datasets using a dynamic vehicle placement application. Such synthetic datasets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility.