653 research outputs found
Limits of Predictability in Commuting Flows in the Absence of Data for Calibration
The estimation of commuting flows at different spatial scales is a fundamental problem for different areas of study. Many current methods rely on parameters requiring calibration from empirical trip volumes. Their values are often not generalizable to cases without calibration data. To solve this problem we develop a statistical expression to calculate commuting trips with a quantitative functional form to estimate the model parameter when empirical trip data is not available. We calculate commuting trip volumes at scales from within a city to an entire country, introducing a scaling parameter α to the recently proposed parameter free radiation model. The model requires only widely available population and facility density distributions. The parameter can be interpreted as the influence of the region scale and the degree of heterogeneity in the facility distribution. We explore in detail the scaling limitations of this problem, namely under which conditions the proposed model can be applied without trip data for calibration. On the other hand, when empirical trip data is available, we show that the proposed model's estimation accuracy is as good as other existing models. We validated the model in different regions in the U.S., then successfully applied it in three different countries
Systematic comparison of trip distribution laws and models
Trip distribution laws are basic for the travel demand characterization
needed in transport and urban planning. Several approaches have been considered
in the last years. One of them is the so-called gravity law, in which the
number of trips is assumed to be related to the population at origin and
destination and to decrease with the distance. The mathematical expression of
this law resembles Newton's law of gravity, which explains its name. Another
popular approach is inspired by the theory of intervening opportunities which
argues that the distance has no effect on the destination choice, playing only
the role of a surrogate for the number of intervening opportunities between
them. In this paper, we perform a thorough comparison between these two
approaches in their ability at estimating commuting flows by testing them
against empirical trip data at different scales and coming from different
countries. Different versions of the gravity and the intervening opportunities
laws, including the recently proposed radiation law, are used to estimate the
probability that an individual has to commute from one unit to another, called
trip distribution law. Based on these probability distribution laws, the
commuting networks are simulated with different trip distribution models. We
show that the gravity law performs better than the intervening opportunities
laws to estimate the commuting flows, to preserve the structure of the network
and to fit the commuting distance distribution although it fails at predicting
commuting flows at large distances. Finally, we show that the different
approaches can be used in the absence of detailed data for calibration since
their only parameter depends only on the scale of the geographic unit.Comment: 15 pages, 10 figure
Predicting human mobility through the assimilation of social media traces into mobility models
Predicting human mobility flows at different spatial scales is challenged by
the heterogeneity of individual trajectories and the multi-scale nature of
transportation networks. As vast amounts of digital traces of human behaviour
become available, an opportunity arises to improve mobility models by
integrating into them proxy data on mobility collected by a variety of digital
platforms and location-aware services. Here we propose a hybrid model of human
mobility that integrates a large-scale publicly available dataset from a
popular photo-sharing system with the classical gravity model, under a stacked
regression procedure. We validate the performance and generalizability of our
approach using two ground-truth datasets on air travel and daily commuting in
the United States: using two different cross-validation schemes we show that
the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure
Supersampling and network reconstruction of urban mobility
Understanding human mobility is of vital importance for urban planning,
epidemiology, and many other fields that aim to draw policies from the
activities of humans in space. Despite recent availability of large scale data
sets related to human mobility such as GPS traces, mobile phone data, etc., it
is still true that such data sets represent a subsample of the population of
interest, and then might give an incomplete picture of the entire population in
question. Notwithstanding the abundant usage of such inherently limited data
sets, the impact of sampling biases on mobility patterns is unclear -- we do
not have methods available to reliably infer mobility information from a
limited data set. Here, we investigate the effects of sampling using a data set
of millions of taxi movements in New York City. On the one hand, we show that
mobility patterns are highly stable once an appropriate simple rescaling is
applied to the data, implying negligible loss of information due to subsampling
over long time scales. On the other hand, contrasting an appropriate null model
on the weighted network of vehicle flows reveals distinctive features which
need to be accounted for. Accordingly, we formulate a "supersampling"
methodology which allows us to reliably extrapolate mobility data from a
reduced sample and propose a number of network-based metrics to reliably assess
its quality (and that of other human mobility models). Our approach provides a
well founded way to exploit temporal patterns to save effort in recording
mobility data, and opens the possibility to scale up data from limited records
when information on the full system is needed.Comment: 14 pages, 4 figure
On the use of human mobility proxy for the modeling of epidemics
Human mobility is a key component of large-scale spatial-transmission models
of infectious diseases. Correctly modeling and quantifying human mobility is
critical for improving epidemic control policies, but may be hindered by
incomplete data in some regions of the world. Here we explore the opportunity
of using proxy data or models for individual mobility to describe commuting
movements and predict the diffusion of infectious disease. We consider three
European countries and the corresponding commuting networks at different
resolution scales obtained from official census surveys, from proxy data for
human mobility extracted from mobile phone call records, and from the radiation
model calibrated with census data. Metapopulation models defined on the three
countries and integrating the different mobility layers are compared in terms
of epidemic observables. We show that commuting networks from mobile phone data
well capture the empirical commuting patterns, accounting for more than 87% of
the total fluxes. The distributions of commuting fluxes per link from both
sources of data - mobile phones and census - are similar and highly correlated,
however a systematic overestimation of commuting traffic in the mobile phone
data is observed. This leads to epidemics that spread faster than on census
commuting networks, however preserving the order of infection of newly infected
locations. Match in the epidemic invasion pattern is sensitive to initial
conditions: the radiation model shows higher accuracy with respect to mobile
phone data when the seed is central in the network, while the mobile phone
proxy performs better for epidemics seeded in peripheral locations. Results
suggest that different proxies can be used to approximate commuting patterns
across different resolution scales in spatial epidemic simulations, in light of
the desired accuracy in the epidemic outcome under study.Comment: Accepted fro publication in PLOS Computational Biology. Abstract
shortened to fit Arxiv limits. 35 pages, 6 figure
Measuring Accessibility using Gravity and Radiation Models
Since the presentation of the Radiation Model, much work has been done to
compare its findings with those obtained from Gravitational Models. These
comparisons always aim at measuring the accuracy with which the models
reproduce the mobility described by origin-destination matrices. This has been
done at different spatial scales using different datasets, and several versions
of the models have been proposed to adjust to various spatial systems. However
the models, to our knowledge, have never been compared with respect to policy
testing scenarios. For this reason, here we use the models to analyze the
impact of the introduction of a new transportation network, a Bus Rapid
Transport system, in the city of Teresina in Brazil. We do this by measuring
the estimated variation in the trip distribution, and formulate an
accessibility to employment indicator for the different zones of the city. By
comparing the results obtained with the two approaches, we are able, not only
to better assess the goodness of fit and the impact of this intervention, but
also to understand reasons for the systematic similarities and differences in
their predictions.Comment: 12 Pages, 4 Figure
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