Daily Mobility Practices Through Mobile Phone Data: An Application in Lombardy Region

Pucci, Paola; Manfredini, Fabio; Tagliolato, Paolo

doi:10.1007/978-3-319-14833-5_3

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The spatial and temporal resolution of geospatial big datasets has grown over time. Telecommunication network data, called Call Detail Records (CDR), are widely involved in social and geographical analyses, specifically in urban studies (Louail, T. et al 2014;Pucci, P. 2015;Jiang, S. et al 2016;Razavi, S.M. et al 2018;Egedy, T. and Ságvári, B.…”

Section: Big Data In Tourism Mobility Researchmentioning

confidence: 99%

Mobility patterns of satellite travellers based on mobile phone cellular data

et al. 2023

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For a long time, tourism statistics were the only reliable source of information on tourism mobility. Tourism statistics are inadequate for the analysis of tourist mobility within state borders and across Schengen Borders without using registered accommodations. Big data offers the opportunity to gain a better understanding of tourism movements, for example, same-day tourist flows in metropolitan areas. Here, we introduce the concept of the satellite traveller to more effectively investigate the nature of tourism between the large city and its surroundings. As tourists communicate via cellular devices, the use of mobile phones offers an opportunity for researchers to explore the mobility pattern of tourists. In this article, we discuss the specificities of mobility in Hungary by SIM card users registered in foreign countries. The analysis is based on the Telekom database. We seek to answer the question to what extent the information from the satellite tourists’ mobile phone use can help to understand their movements and to identify frequented places less commonly accounted for in tourism statistics. The most important findings of our investigation are (1) the confirmation of former knowledge about spatial characteristics of same-day tourist flows in the Budapest Metropolitan Region, (2) the insight that far away settlements are also visited by satellite travellers, and (3) the methodological limitations of mobile phone cellular data for tourism mobility analysis.

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Section: Big Data In Tourism Mobility Researchmentioning

confidence: 99%

Mobility patterns of satellite travellers based on mobile phone cellular data

et al. 2023

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“…Several studies have reviewed current methods and practices, potentials and limitations of using cellular network data for transportation planning analyses (Caceres et al 2008;Chen et al 2016;Çolak et al 2015;Huang et al 2019;Jiang et al 2013;Wang et al 2018). Studies investigating cellular network data to better understand human mobility patterns typically focus on identifying activities, trips, and spatialtemporal variations in travel patterns (Becker et al 2011;Bekhor and Shem-Tov 2015;Pucci et al 2015;Xu et al 2017;Zahedi and Shafahi 2017). Specifically, detecting home locations is considered important to yield useful insights into people's travel patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nevertheless, it is possible to obtain BTS cell-to-cell OD matrices from cellular network data. In the transportation literature, several studies have focused on extracting OD matrices from cellular network data for different regions around the world (Caceres et al 2013;Caceres et al 2007;Calabrese et al 2011;Demissie et al 2016;Iqbal et al 2014;Larijani et al 2015;Mellegard et al 2011;Nanni et al 2014;Pucci et al 2015;Zhang et al 2010). The extracted OD matrices are then used for different purposes, such as optimizing public transport network service (Berlingerio et al 2013) or estimating traffic flows (Gundlegård et al 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

Vaughan

Faghih-Imani

Yusuf³

et al. 2020

European Journal of Transport and Infrastructure Research

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This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.

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