Evaluating Origin–Destination Matrices Obtained from CDR Data
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year = 2019,
keywords = {mobility patterns; CDR data; OD matrices},
status = {Published},
venue_list = {--},
number = 20,
url = {https://www.mdpi.com/1424-8220/19/20/4470},
month = {October},
urlpdf = {https://www.mdpi.com/1424-8220/19/20/4470/pdf},
issn = {1424-8220},
journal = {Sensors},
author = {Mamei, Marco and Bicocchi, Nicola and Lippi, Marco and Mariani, Stefano and Zambonelli, Franco},
title = {Evaluating Origin–Destination Matrices Obtained from CDR Data},
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.},
articleNo = 4470,
volume = 19,
doi = {10.3390/s19204470}}