ArticleSensors (Basel, Switzerland)2019
Evaluating Origin-Destination Matrices Obtained from CDR Data.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Multi-Source Sensor Dataset for Spain: Integrating Air Quality, Meteorological, Mobility and Calendar Records.Sensors (Basel, Switzerland) · 2026Article
- Article
- Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network.Sensors (Basel, Switzerland) · 2023Article
- Territorial differences in the spread of COVID-19 in European regions and US counties.PloS one · 2023Article
- An LBS and agent-based simulator for Covid-19 research.Scientific reports · 2022Article
- Mobility functional areas and COVID-19 spread.Transportation · 2022Article
- A Bayesian Method for Dynamic Origin-Destination Demand Estimation Synthesizing Multiple Sources of Data.Sensors (Basel, Switzerland) · 2021Article
- Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness.Japanese journal of statistics and data science · 2021Article
- Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience.Journal of big data · 2021Article
- Measuring the impact of COVID-19 confinement measures on human mobility using mobile positioning data. A European regional analysis.Safety science · 2020Article
- Human mobility and COVID-19 initial dynamics.Nonlinear dynamics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Understanding and correctly modeling urban mobility is a crucial issue for the development of smart cities. The estimation of individual trips from mobile phone positioning data (i.e., call detail records (CDR)) can naturally support urban and transport studies as well as marketing applications. Individual trips are often aggregated in an origin-destination (OD) matrix counting the number of trips from a given origin to a given destination. In the literature dealing with CDR data there are two main approaches to extract OD matrices from such data: (a) in time-based matrices, the analysis focuses on estimating mobility directly from a sequence of CDRs; (b) in routine-based matrices (OD by purpose) the analysis focuses on routine kind of movements, like home-work commute, derived from a trip generation model. In both cases, the OD matrix measured by CDR counts is scaled to match the actual number of people moving in the area, and projected to the road network to estimate actual flows on the streets. In this paper, we describe prototypical approaches to estimate OD matrices, describe an actual implementation, and present a number of experiments to evaluate the results from multiple perspectives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.