Erratum: "Towards a Statistical Physics of Human Mobility"

Gallotti, Riccardo; Bazzani, Armando; Rambaldi, Sandro

doi:10.1142/s0129183113920010

Cited by 21 publications

(56 citation statements)

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“…Thus, human movements could possibly be identified as truncated Lévy flights only1144. In contrast, displacements at the urban scale consistently display an exponential tail192425. Short tails also emerge when studying the distribution P ( t ) of travel times t of individual trajectories originating in different cities.…”

Section: Resultsmentioning

confidence: 99%

A stochastic model of randomly accelerated walkers for human mobility

et al. 2016

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Recent studies of human mobility largely focus on displacements patterns and power law fits of empirical long-tailed distributions of distances are usually associated to scale-free superdiffusive random walks called Lévy flights. However, drawing conclusions about a complex system from a fit, without any further knowledge of the underlying dynamics, might lead to erroneous interpretations. Here we show, on the basis of a data set describing the trajectories of 780,000 private vehicles in Italy, that the Lévy flight model cannot explain the behaviour of travel times and speeds. We therefore introduce a class of accelerated random walks, validated by empirical observations, where the velocity changes due to acceleration kicks at random times. Combining this mechanism with an exponentially decaying distribution of travel times leads to a short-tailed distribution of distances which could indeed be mistaken with a truncated power law. These results illustrate the limits of purely descriptive models and provide a mechanistic view of mobility.

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Section: Resultsmentioning

confidence: 99%

A stochastic model of randomly accelerated walkers for human mobility

et al. 2016

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“…A dynamic model to simulate the pedestrian dynamics on the Venice road network based on the individual dynamics has to includes tracts covered at constant velocity and breaks due to the presence of points of interests, crowded situations or to recover from the walking fatigue. We consider some statistical properties of the reconstructed mobility paths to check if they are consistent with other statistical laws suggested by the analysis of mobility datasets in urban contexts [4,6,9,10]. In Fig.…”

Section: Statistical Properties Of Mobility Pathsmentioning

confidence: 78%

“…The fast development of Information Communication Technologies (ICT) offers new opportunities for the realization of innovative analytical tools in the framework of big data analytics, which is one of the future challenges of the Complexity Science [1,2]. Various authors have considered the large georereferenced datasets on individuals mobility, studying the statistical laws at the base of human movements [3][4][5][6][7][8] and the dynamic properties of human mobility in urban contexts [9,10]. The aim of this research activity in the framework of Complex Systems Physics is to provide to stakeholders new knowledge tools to improve the sustainability of the mobility demand in future cities [8,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…The study of the mobility through the reconstruction of the device trajectories on a road network, requires a precision of few meters in the device location that is characteristic of GPS data. In previous works [6,28] we have studied the private vehicle mobility on urban road networks using GPS dataset recorded for insurance reasons that contains information on the vehicle trajectories at a scale of 1 km or 30 sec, on a sample of 3% of the Italian vehicle population. In this work we apply the same methodologies to the GPS data recorded from mobile devices.…”

Section: Introductionmentioning

confidence: 99%

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Unraveling pedestrian mobility on a road network using ICTs data during great tourist events

et al. 2018

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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 a new dataset that has been collected by the Italian mobile phone company TIM, which contains the GPS positions of a relevant sample of mobile devices when they actively connected to the cell phone network. Our aim is to propose innovative tools allowing to study properties of pedestrian mobility on the whole road network. Venice is a paradigmatic example for the impact of tourist flows on the resident life quality and on the preservation of cultural heritage. The GPS data provide anonymized georeferenced information on the displacements of the devices. After a filtering procedure, we develop specific algorithms able to reconstruct the daily mobility paths on the whole Venice road network. The statistical analysis of the mobility paths suggests the existence of a travel time budget for the mobility and points out the role of the rest times in the empirical relation between the mobility time and the corresponding path length. We succeed to highlight two connected mobility subnetworks extracted from the whole road network, that are able to explain the majority of the observed mobility. Our approach shows the existence of characteristic mobility paths in Venice for the tourists and for the residents. Moreover the data analysis highlights the different mobility features of the considered case studies and it allows to detect the mobility paths associated to different points of interest. Finally we have disaggregated the Italian and foreigner categories to study their different mobility behaviors.

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“…For instance, human mobility has been studied using large-scale trajectory datasets including bank notes [16], taxi data [18,19], GPS observations [20], Wi-Fi [15], cell phone call recordings [21,22], and social media posts [23][24][25][26][27]. These studies have found that mobility follows power-laws [14,16,18,21,22,[28][29][30][31][32][33], log-normal [19,20], exponential distribution [34][35][36][37][38][39] or a combination of power-law and exponential distributions [35,38].…”

Section: Introductionmentioning

confidence: 99%

Quantifying human mobility resilience to extreme events using geo-located social media data

2019

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Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socioeconomic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience strategies.

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Erratum: "Towards a Statistical Physics of Human Mobility"

Cited by 21 publications

References 0 publications

A stochastic model of randomly accelerated walkers for human mobility

A stochastic model of randomly accelerated walkers for human mobility

Unraveling pedestrian mobility on a road network using ICTs data during great tourist events

Quantifying human mobility resilience to extreme events using geo-located social media data

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