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.
Highlights
In recent years, there has been a growing interest in the development of ICT technologies that can succeed in collecting, processing, and analyzing mobility data with simple, efficient, and privacy-preserving procedures.The widespread diffusion of mobile phones and cell networks provides a practical way to collect location-based information from large user populations
In this work we focus on the estimation of origin-destination (OD) matrices: individual trips are often aggregated in an origin–destination matrix counting the number of trips from a given origin to a given destination
The goal of this work is to present in a coherent framework the main approaches to compute time-based matrices and routine-based matrices from CDR data, in particular focusing on the home–work commute
Summary
The widespread diffusion of mobile phones and cell networks provides a practical way to collect location-based information from large user populations. The analysis of such data is a key asset in the development of several applications, including location-based services, traffic forecasting, urban planning and management [1,2,3,4]. The estimation of individual trips from mobile phone positioning data (i.e., call detail records (CDR)) is an important application in this area, and it can naturally support urban and transport studies, as well as marketing applications, by allowing us to estimate the passage of potential customers on a given path [5,6,7,8,9]. CDRs provide approximate location samples of the phone’s owner, and a sequence of CDRs can provide their mobility pathways and trips
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