# Exploring universal patterns in human home-work commuting from mobile phone data

arXiv: https://arxiv.org/abs/1311.2911 · HTML: https://mlsanity.com/paper/1311.2911/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2013.

## Built on by (most cited first, 7 of 7)

- [Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy](https://mlsanity.com/paper/2201.01139/) (2022)
- [Harnessing LLMs for Cross-City OD Flow Prediction](https://mlsanity.com/paper/2409.03937/) (2024)
- [User Localization Based on Call Detail Records](https://mlsanity.com/paper/2108.09157/) (2021)
- [Pattern Ensembling for Spatial Trajectory Reconstruction](https://mlsanity.com/paper/2101.09844/) (2021)
- [Changes in Commuter Behavior from COVID-19 Lockdowns in the Atlanta Metropolitan Area](https://mlsanity.com/paper/2302.13512/) (2023)
- [DiffMove: Group Mobility Tendency Enhanced Trajectory Recovery via Diffusion Model](https://mlsanity.com/paper/2503.18302/) (2025)
- [Downscaling human mobility data based on demographic socioeconomic and commuting characteristics using interpretable machine learning methods](https://mlsanity.com/paper/2509.21703/) (2025)

## Abstract (arXiv)

Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country and city scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)--despite substantial spatial and infrastructural differences. Furthermore, a comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors--as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance.