2018
DOI: 10.1140/epjds/s13688-018-0168-2
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Unraveling pedestrian mobility on a road network using ICTs data during great tourist events

Abstract: Tourist flows in historical cities are continuously growing in a globalized world and adequate governance processes, politics and tools are necessary in order to reduce impacts on the urban livability and to guarantee the preservation of cultural heritage. The ICTs offer the possibility of collecting large amount of data that can point out and quantify some statistical and dynamic properties of human mobility emerging from the individual behavior and referring to a whole road network. In this paper we analyze … Show more

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Cited by 26 publications
(19 citation statements)
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“…Mobile phone satellite position records and cell phone usage have also opened up multiple opportunities such as identifying urban activities and their spatial-temporal evolution almost in real time [51] and understanding tourist travel behaviour [52]. The potential to identify different user profiles with this data source has been shown in the heritage cities such as Rome [51], Venice [53] and Florence [54]. However, cell phone tracking encompasses certain limitations related to the uneven spatial accuracy (limited by the density of cell towers over the study area and thus posing a problem when studying movements on a local scale).…”
Section: Mobile Phone Satellite Position Recordsmentioning
confidence: 99%
“…Mobile phone satellite position records and cell phone usage have also opened up multiple opportunities such as identifying urban activities and their spatial-temporal evolution almost in real time [51] and understanding tourist travel behaviour [52]. The potential to identify different user profiles with this data source has been shown in the heritage cities such as Rome [51], Venice [53] and Florence [54]. However, cell phone tracking encompasses certain limitations related to the uneven spatial accuracy (limited by the density of cell towers over the study area and thus posing a problem when studying movements on a local scale).…”
Section: Mobile Phone Satellite Position Recordsmentioning
confidence: 99%
“…A custom feature selection algorithm based on Quadratic Discriminant Analysis was used to identify an optimal low-dimensional signature able to discriminate between NN and NP samples based on a method developed by the authors, described in [20][21][22], which had been used previously to analyse biological and social data [13,14,21]. The algorithm evaluates all possible pairs of transcripts which it groups onto a transcript-transcript network weighted by classification performance.…”
Section: Signature Identificationmentioning
confidence: 99%
“…The putative signatures are obtained through thresholding based on classification performance for the disconnected components resulting from the initial weighted network. However, optimal performance of the algorithm requires a larger sample size than that available in this study [14,[20][21][22], to allow more performance threshold values to be tested. The smaller sample size used here tends to generate a single giant component with a large number of transcripts.…”
Section: Signature Identificationmentioning
confidence: 99%
“…The study leverages massive smartphones LTE RSRP MDT measurements. MDT is an LTE 3GPP standard (TS 37.320) [1][2][3][4][5] that associates User Equipment periodic radio measurements to GPS position, when available. Data analysis exploited R Studio statistical tool environment [6].…”
Section: Introductionmentioning
confidence: 99%