# A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data

arXiv: https://arxiv.org/abs/1811.09486 · HTML: https://mlsanity.com/paper/1811.09486/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning](https://mlsanity.com/paper/2312.07371/) (2023)
- [Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs](https://mlsanity.com/paper/2507.05507/) (2025)

## Abstract (arXiv)

In recent years, and especially since the development of the smartphone, enormous amounts of data relevant for transportation have become available. These data hold out the potential to redefine how transportation system (i.e. design, planning and operations) is done. While researchers in both academia and industry are making advances in using this data to transportation system ends (e.g. information inference from collected data), little attention has been paid to four larger scale challenges that will need to be overcome if the potential for Big Transportation Data is to be harnessed for transportation decision-making purposes. This paper aims to provide awareness of these large-scale challenges and provides insight into how we believe these challenges are likely to be met.