# Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example

arXiv: https://arxiv.org/abs/1912.01835 · HTML: https://mlsanity.com/paper/1912.01835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy](https://mlsanity.com/paper/2201.01139/) (2022)
- [Data Poisoning Attacks in Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2407.15855/) (2024)
- [Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires](https://mlsanity.com/paper/2304.06233/) (2023)
- [FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation](https://mlsanity.com/paper/2307.11214/) (2023)
- [Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems](https://mlsanity.com/paper/2407.14541/) (2024)

## Abstract (arXiv)

Passively-generated data, such as GPS data and cellular data, bring tremendous opportunities for human mobility analysis and transportation applications. Since their primary purposes are often non-transportation related, the passively-generated data need to be processed to extract trips. Most existing trip extraction methods rely on data that are generated via a single positioning technology such as GPS or triangulation through cellular towers (thereby called single-sourced data), and methods to extract trips from data generated via multiple positioning technologies (or, multi-sourced data) are absent. And yet, multi-sourced data are now increasingly common. Generated using multiple technologies (e.g., GPS, cellular network- and WiFi-based), multi-sourced data contain high variances in their temporal and spatial properties. In this study, we propose a 'Divide, Conquer and Integrate' (DCI) framework to extract trips from multi-sourced data. We evaluate the proposed framework by applying it to an app-based data, which is multi-sourced and has high variances in both location accuracy and observation interval (i.e. time interval between two consecutive observations). On a manually labeled sample of the app-based data, the framework outperforms the state-of-the-art SVM model that is designed for GPS data. The effectiveness of the framework is also illustrated by consistent mobility patterns obtained from the app-based data and an externally collected household travel survey data for the same region and the same period.