Exploring universal patterns in human home-work commuting from mobile phone data
arXiv:1311.2911 · doi:10.1371/journal.pone.0096180
Abstract
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.
References in corpus (4)
Cited by in corpus (41)
- From mobile phone data to the spatial structure of cities
- Uncovering the spatial structure of mobility networks
- An analysis of visitors' behavior in the Louvre Museum: A study using Bluetooth data
- The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective
- Estimating city-level travel patterns using street imagery: a case study of using Google Street View in Britain
- Urban Magnetism Through The Lens of Geo-tagged Photography
- Assessing the quality of home detection from mobile phone data for official statistics
- Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity
- Cities through the Prism of People's Spending Behavior
- Structure of 311 Service Requests as a Signature of Urban Location
- COVID-19 is linked to changes in the time-space dimension of human mobility
- Migrant mobility flows characterized with digital data
- Identifying the structural discontinuities of human interactions
- DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns
- Constructing multi-level urban clusters based on population distributions and interactions
- Great cities look small
- Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy
- Bayesian estimate of position in mobile phone network
- Mining individual daily commuting patterns of dockless bike-sharing users: a two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees
- Performance and sensitivities of home detection from mobile phone data
- Choosing the right home location definition method for the given dataset
- Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data
- Assessing Refugees' Integration via Spatio-temporal Similarities of Mobility and Calling Behaviors
- The fallacy of the closest antenna: Towards an adequate view of device location in the mobile network
- Optimizing city-scale traffic through modeling observations of vehicle movements
- Detecting home locations from CDR data: introducing spatial uncertainty to the state-of-the-art
- A Bayesian approach to location estimation of mobile devices from mobile network operator data
- Pattern and Anomaly Detection in Urban Temporal Networks
- Digital Urban Sensing: A Multi-layered Approach
- Impact Of Urban Technology Deployments On Local Commercial Activity
- Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service
- Understanding the variability of daily travel-time expenditures using GPS trajectory data
- A Large-scale Examination of "Socioeconomic" Fairness in Mobile Networks
- Mobile Phone Application Data for Activity Plan Generation
- User Localization Based on Call Detail Records
- Pattern Ensembling for Spatial Trajectory Reconstruction
- House Price Modeling with Digital Census
- Profiling presence patterns and segmenting user locations from cell phone data
- Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection
- Exploring Invariants & Patterns in Human Commute time
- Leveraging Sidewalk Robots for Walkability-Related Analyses