4,635 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
Fusing Loop and GPS Probe Measurements to Estimate Freeway Density
In an age of ever-increasing penetration of GPS-enabled mobile devices, the
potential of real-time "probe" location information for estimating the state of
transportation networks is receiving increasing attention. Much work has been
done on using probe data to estimate the current speed of vehicle traffic (or
equivalently, trip travel time). While travel times are useful to individual
drivers, the state variable for a large class of traffic models and control
algorithms is vehicle density. Our goal is to use probe data to supplement
traditional, fixed-location loop detector data for density estimation. To this
end, we derive a method based on Rao-Blackwellized particle filters, a
sequential Monte Carlo scheme. We present a simulation where we obtain a 30\%
reduction in density mean absolute percentage error from fusing loop and probe
data, vs. using loop data alone. We also present results using real data from a
19-mile freeway section in Los Angeles, California, where we obtain a 31\%
reduction. In addition, our method's estimate when using only the real-world
probe data, and no loop data, outperformed the estimate produced when only loop
data were used (an 18\% reduction). These results demonstrate that probe data
can be used for traffic density estimation
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
We are witnessing increasing interests in the effective use of road networks.
For example, to enable effective vehicle routing, weighted-graph models of
transportation networks are used, where the weight of an edge captures some
cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions
or travel time. It is a precondition to using a graph model for routing that
all edges have weights. Weights that capture travel times and GHG emissions can
be extracted from GPS trajectory data collected from the network. However, GPS
trajectory data typically lack the coverage needed to assign weights to all
edges. This paper formulates and addresses the problem of annotating all edges
in a road network with travel cost based weights from a set of trips in the
network that cover only a small fraction of the edges, each with an associated
ground-truth travel cost. A general framework is proposed to solve the problem.
Specifically, the problem is modeled as a regression problem and solved by
minimizing a judiciously designed objective function that takes into account
the topology of the road network. In particular, the use of weighted PageRank
values of edges is explored for assigning appropriate weights to all edges, and
the property of directional adjacency of edges is also taken into account to
assign weights. Empirical studies with weights capturing travel time and GHG
emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark)
offer insight into the design properties of the proposed techniques and offer
evidence that the techniques are effective.Comment: This is an extended version of "Using Incomplete Information for
Complete Weight Annotation of Road Networks," which is accepted for
publication in IEEE TKD
Feasibility of expanding traffic monitoring systems with floating car data technology
Trajectory information reported by certain vehicles (Floating Car Data or FCD) can be applied to monitor the road network. Policy makers face difficulties when deciding to invest in the expansion of their infrastructure based on inductive loops and cameras, or to invest in a FCD system. This paper targets this decision. The provided FCD functionality is investigated, minimum requirements are determined and reliability issues are researched. The communication cost is derived and combined with other elements to assess the total costs for different scenarios. The outcome is to target a penetration rate of 1%, a sample interval of 10 seconds and a transmission interval of 30 seconds. Such a deployment can accurately determine the locations of incidents and traffic jams. It can also estimate travel times accurately for highways, for urban roads this is limited to a binary categorization into normal or congested traffic. No reliability issues are expected. The most cost efficient scenario when deploying a new FCD system is to launch a smartphone application. For Belgium, this costs 13 million EUR for 10 years. However, it is estimated that purchasing data from companies already acquiring FCD data through their own product could reduce costs with a factor 10
Characterizing Driving Context from Driver Behavior
Because of the increasing availability of spatiotemporal data, a variety of
data-analytic applications have become possible. Characterizing driving
context, where context may be thought of as a combination of location and time,
is a new challenging application. An example of such a characterization is
finding the correlation between driving behavior and traffic conditions. This
contextual information enables analysts to validate observation-based
hypotheses about the driving of an individual. In this paper, we present
DriveContext, a novel framework to find the characteristics of a context, by
extracting significant driving patterns (e.g., a slow-down), and then
identifying the set of potential causes behind patterns (e.g., traffic
congestion). Our experimental results confirm the feasibility of the framework
in identifying meaningful driving patterns, with improvements in comparison
with the state-of-the-art. We also demonstrate how the framework derives
interesting characteristics for different contexts, through real-world
examples.Comment: Accepted to be published at The 25th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL
2017
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