City-scale synthetic individual-level vehicle trip data

TL;DR

This paper introduces a method to generate city-scale synthetic individual vehicle trip data that balances data privacy with utility, enabling transportation research without compromising privacy.

Contribution

The paper presents a novel approach to produce privacy-preserving synthetic trip data at the city scale, maintaining data utility for research purposes.

Findings

Synthetic data is consistent with real data at aggregate levels

Synthetic data preserves individual trip patterns reasonably

Experiments demonstrate the data's reliability across various analyses

Abstract

Trip data that records each vehicle's trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.