72,360 research outputs found
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
Scaling laws for the movement of people between locations in a large city
Large scale simulations of the movements of people in a ``virtual'' city and
their analyses are used to generate new insights into understanding the dynamic
processes that depend on the interactions between people. Models, based on
these interactions, can be used in optimizing traffic flow, slowing the spread
of infectious diseases or predicting the change in cell phone usage in a
disaster. We analyzed cumulative and aggregated data generated from the
simulated movements of 1.6 million individuals in a computer (pseudo
agent-based) model during a typical day in Portland, Oregon. This city is
mapped into a graph with nodes representing physical locations such
as buildings. Connecting edges model individual's flow between nodes. Edge
weights are constructed from the daily traffic of individuals moving between
locations. The number of edges leaving a node (out-degree), the edge weights
(out-traffic), and the edge-weights per location (total out-traffic) are fitted
well by power law distributions. The power law distributions also fit subgraphs
based on work, school, and social/recreational activities. The resulting
weighted graph is a ``small world'' and has scaling laws consistent with an
underlying hierarchical structure. We also explore the time evolution of the
largest connected component and the distribution of the component sizes. We
observe a strong linear correlation between the out-degree and total
out-traffic distributions and significant levels of clustering. We discuss how
these network features can be used to characterize social networks and their
relationship to dynamic processes.Comment: 18 pages, 10 figure
The structure of Inter-Urban traffic: A weighted network analysis
We study the structure of the network representing the interurban commuting
traffic of the Sardinia region, Italy, which amounts to 375 municipalities and
1,600,000 inhabitants. We use a weighted network representation where vertices
correspond to towns and the edges to the actual commuting flows among those. We
characterize quantitatively both the topological and weighted properties of the
resulting network. Interestingly, the statistical properties of commuting
traffic exhibit complex features and non-trivial relations with the underlying
topology. We characterize quantitatively the traffic backbone among large
cities and we give evidences for a very high heterogeneity of the commuter
flows around large cities. We also discuss the interplay between the
topological and dynamical properties of the network as well as their relation
with socio-demographic variables such as population and monthly income. This
analysis may be useful at various stages in environmental planning and provides
analytical tools for a wide spectrum of applications ranging from impact
evaluation to decision-making and planning support.Comment: 12 pages, 12 figures, 4 tables; 1 missing ref added and minor
revision
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
- …