MobilityMirror: Bias-Adjusted Transportation Datasets

TL;DR

This paper presents a method for creating bias-adjusted synthetic transportation datasets that preserve utility while removing discriminatory causal relationships, facilitating privacy protection and fair data sharing.

Contribution

It introduces a causality-based algorithm for bias removal in origin-destination data, enabling privacy-preserving and equitable transportation data publication.

Findings

The adjusted datasets retain utility comparable to original data.

Biases related to discrimination and contractual constraints are effectively removed.

The method is validated on real bike share and taxi datasets from Seattle and New York.

Abstract

We describe customized synthetic datasets for publishing mobility data. Private companies are providing new transportation modalities, and their data is of high value for integrative transportation research, policy enforcement, and public accountability. However, these companies are disincentivized from sharing data not only to protect the privacy of individuals (drivers and/or passengers), but also to protect their own competitive advantage. Moreover, demographic biases arising from how the services are delivered may be amplified if released data is used in other contexts. We describe a model and algorithm for releasing origin-destination histograms that removes selected biases in the data using causality-based methods. We compute the origin-destination histogram of the original dataset then adjust the counts to remove undesirable causal relationships that can lead to discrimination or violate contractual obligations with data owners. We evaluate the utility of the algorithm on real data from a dockless bike share program in Seattle and taxi data in New York, and show that these adjusted transportation datasets can retain utility while removing bias in the underlying data.