62 research outputs found
Information measures and cognitive limits in multilayer navigation
Cities and their transportation systems become increasingly complex and
multimodal as they grow, and it is natural to wonder if it is possible to
quantitatively characterize our difficulty to navigate in them and whether such
navigation exceeds our cognitive limits. A transition between different
searching strategies for navigating in metropolitan maps has been observed for
large, complex metropolitan networks. This evidence suggests the existence of
another limit associated to the cognitive overload and caused by large amounts
of information to process. In this light, we analyzed the world's 15 largest
metropolitan networks and estimated the information limit for determining a
trip in a transportation system to be on the order of 8 bits. Similar to the
"Dunbar number," which represents a limit to the size of an individual's
friendship circle, our cognitive limit suggests that maps should not consist of
more than about connections points to be easily readable. We also show
that including connections with other transportation modes dramatically
increases the information needed to navigate in multilayer transportation
networks: in large cities such as New York, Paris, and Tokyo, more than
of trips are above the 8-bit limit. Multimodal transportation systems in large
cities have thus already exceeded human cognitive limits and consequently the
traditional view of navigation in cities has to be revised substantially.Comment: 16 pages+9 pages of supplementary materia
Anatomy and efficiency of urban multimodal mobility
International audienceThe growth of transportation networks and their increasing interconnections, although positive,has the downside effect of an increasing complexity which make them difficult to use, to assess, andlimits their efficiency. On average in the UK, 23% of travel time is lost in connections for trips withmore than one mode, and the lack of synchronization decreases very slowly with population size.This lack of synchronization between modes induces differences between the theoretical quickest tripand the ‘time-respecting’ path, which takes into account waiting times at interconnection nodes.We analyse here the statistics of these paths on the multilayer, temporal network of the entire,multimodal british public transportation system. We propose a statistical decomposition – the‘anatomy’ – of trips in urban areas, in terms of riding, waiting and walking times, and which showshow the temporal structure of trips varies with distance and allows us to compare different cities.Weaknesses in systems can be either insufficient transportation speed or service frequency, but thekey parameter controlling their global efficiency is the total number of stop events per hour for allmodes. This analysis suggests the need for better optimization strategies, adapted to short, longunimodal or multimodal trips
A stochastic model of randomly accelerated walkers for human mobility
The recent availability of large databases allows to study macroscopic
properties of many complex systems. However, inferring a model from a fit of
empirical data without any knowledge of the dynamics might lead to erroneous
interpretations [6]. We illustrate this in the case of human mobility [1-3] and
foraging human patterns [4] where empirical long-tailed distributions of jump
sizes have been associated to scale-free super-diffusive random walks called
L\'evy flights [5]. Here, we introduce a new class of accelerated random walks
where the velocity changes due to acceleration kicks at random times, which
combined with a peaked distribution of travel times [7], displays a jump length
distribution that could easily be misinterpreted as a truncated power law, but
that is not governed by large fluctuations. This stochastic model allows us to
explain empirical observations about the movements of 780,000 private vehicles
in Italy, and more generally, to get a deeper quantitative understanding of
human mobility.Comment: 10 pages, 6 figures + Supplementary informatio
Entropic measures of individual mobility patterns
Understanding human mobility from a microscopic point of view may represent a
fundamental breakthrough for the development of a statistical physics for
cognitive systems and it can shed light on the applicability of macroscopic
statistical laws for social systems. Even if the complexity of individual
behaviors prevents a true microscopic approach, the introduction of mesoscopic
models allows the study of the dynamical properties for the non-stationary
states of the considered system. We propose to compute various entropy measures
of the individual mobility patterns obtained from GPS data that record the
movements of private vehicles in the Florence district, in order to point out
new features of human mobility related to the use of time and space and to
define the dynamical properties of a stochastic model that could generate
similar patterns. Moreover, we can relate the predictability properties of
human mobility to the distribution of time passed between two successive trips.
Our analysis suggests the existence of a hierarchical structure in the mobility
patterns which divides the performed activities into three different
categories, according to the time cost, with different information contents. We
show that a Markov process defined by using the individual mobility network is
not able to reproduce this hierarchy, which seems the consequence of different
strategies in the activity choice. Our results could contribute to the
development of governance policies for a sustainable mobility in modern cities
The multilayer temporal network of public transport in Great Britain
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset
Statistical Laws in Urban Mobility from microscopic GPS data in the area of Florence
The application of Statistical Physics to social systems is mainly related to
the search for macroscopic laws, that can be derived from experimental data
averaged in time or space,assuming the system in a steady state. One of the
major goals would be to find a connection between the statistical laws to the
microscopic properties: for example to understand the nature of the microscopic
interactions or to point out the existence of interaction networks. The
probability theory suggests the existence of few classes of stationary
distributions in the thermodynamics limit, so that the question is if a
statistical physics approach could be able to enroll the complex nature of the
social systems. We have analyzed a large GPS data base for single vehicle
mobility in the Florence urban area, obtaining statistical laws for path
lengths, for activity downtimes and for activity degrees. We show also that
simple generic assumptions on the microscopic behavior could explain the
existence of stationary macroscopic laws, with an universal function describing
the distribution. Our conclusion is that understanding the system complexity
requires dynamical data-base for the microscopic evolution, that allow to solve
both small space and time scales in order to study the transients.Comment: 17 pages, 14 figures .jpg, use imsart.cl
Understanding the variability of daily travel-time expenditures using GPS trajectory data
12+6 Pages, 6+2 Figures, 1+1 TablesTransportation planning is strongly influenced by the assumption that every individual has for his daily mobility a constant daily budget of ~1 hour. However, recent experimental results are proving this assumption as wrong. Here, we study the differences in daily travel-time expenditures among 24 Italian cities, extracted from a large set of GPS data on vehicles mobility. To understand these variations at the level of individual behaviour, we introduce a trip duration model that allows for a description of the distribution of travel-time expenditures in a given city using two parameters. The first parameter reflects the accessibility of desired destinations, whereas the second one can be associated to a travel-time budget and represents physiological limits due to stress and fatigue. Within the same city, we observe variations in the distributions according to home position, number of mobility days and a driver's average number of daily trips. These results can be interpreted by a stochastic time-consumption model, where the generalised cost of travel times is given by a logarithmic-like function, in agreement with the Weber-Fechner law. Our experimental results show a significant variability in the travel-time budgets in different cities and for different categories of drivers within the same city. This explicitly clashes with the idea of the existence of a constant travel-time budget and opens new perspectives for the modeling and governance of urban mobility
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
Diurnal Patterns in the Spread of COVID-19 Misinformation on Twitter within Italy
Social media manipulation poses a significant threat to cognitive autonomy
and unbiased opinion formation. Prior literature explored the relationship
between online activity, and emotional state, cognitive resources, sunlight,
and weather. However, a limited understanding exists regarding the role of time
of day in content spread and the impact of user activity patterns and
chronotype on susceptibility to mis- and disinformation. This work uncovers a
strong correlation between user activity patterns and the tendency to spread
manipulated content. Through quantitative analysis of Twitter data, we examine
how user activity throughout the day aligns with chronotypical archetypes.
Evening types exhibit a significantly higher inclination towards spreading
potentially manipulated content, which is generally more likely between 2:30 AM
and 4:15 AM. This knowledge can become crucial for developing targeted
interventions and strategies that mitigate misinformation spread by addressing
vulnerable periods and user groups more susceptible to manipulation
Unraveling the hidden organisation of urban systems and their mobility flows
Increasing evidence suggests that cities are complex systems, with structural
and dynamical features responsible for a broad spectrum of emerging phenomena.
Here we use a unique data set of human flows and couple it with information on
the underlying street network to study, simultaneously, the structural and
functional organisation of 10 world megacities. We quantify the efficiency of
flow exchange between areas of a city in terms of integration and segregation
using well defined measures. Results reveal unexpected complex patterns that
shed new light on urban organisation. Large cities tend to be more segregated
and less integrated, while their overall topological organisation resembles
that of small world networks. At the same time, the heterogeneity of flows
distribution might act as a catalyst for further integrating a city. Our
analysis unravels how human behaviour influences, and is influenced by, the
urban environment, suggesting quantitative indicators to control integration
and segregation of human flows that can be used, among others, for restriction
policies to adopt during emergencies and, as an interesting byproduct, allows
us to characterise functional (dis)similarities of different metropolitan
areas, countries, and cultures.Comment: The first version on the arxiv is the extended version of the report
presented to the Foursquare Future City Challenge 2019
https://www.futurecitieschallenge.com and presented at the NetMob19
Conference In Oxford. The second is the preprint of the published pape
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