147,919 research outputs found
Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks
This article presents two new models of Dynamic User Equilibrium that are particularly suited for ITS applications, where the evolution of vehicle flows and travel times must be simulated on large road networks, possibly in real-time. The key feature of the proposed models is the detail representation of the main congestion phenomena occurring at nodes of urban networks, such as vehicle queues and their spillback, as well as flow conflicts in mergins and diversions. Compared to the simple word of static assignment, where only the congestion along the arc is typically reproduced through a separable relation between vehicle flow and travel time, this type of DTA models are much more complex, as the above relation becomes non-separable, both in time and space.
Traffic simulation is here attained through a macroscopic flow model, that extends the theory of kinematic waves to urban networks and non-linear fundamental diagrams: the General Link Transmission Model. The sub-models of the GLTM, namely the Node Intersection Model, the Forward Propagation Model of vehicles and the Backward Propagation Model of spaces, can be combined in two different ways to produce arc travel times starting from turn flows. The first approach is to consider short time intervals of a few seconds and process all nodes for each temporal layer in chronological order. The second approach allows to consider long time intervals of a few minutes and for each sub-model requires to process the whole temporal profile of involved variables. The two resulting DTA models are here analyzed and compared with the aim of identifying their possible use cases.
A rigorous mathematical formulation is out of the scope of this paper, as well as a detailed explanation of the solution algorithm.
The dynamic equilibrium is anyhow sought through a new method based on Gradient Projection, which is capable to solve both proposed models with any desired precision in a reasonable number of iterations. Its fast convergence is essential to show that the two proposed models for network congestion actually converge at equilibrium to nearly identical solutions in terms of arc flows and travel times, despite their two diametrical approaches wrt the dynamic nature of the problem, as shown in the numerical tests presented here
Complexity in city systems: Understanding, evolution, and design
6.4 Exemplars of complex systems There are many signatures of complexity revealed in the space-time patterning of cities (Batty, 2005) and here we will indicate three rather different but nevertheless linked exemplars. Our first deals with ..
Bayesian Fused Lasso regression for dynamic binary networks
We propose a multinomial logistic regression model for link prediction in a
time series of directed binary networks. To account for the dynamic nature of
the data we employ a dynamic model for the model parameters that is strongly
connected with the fused lasso penalty. In addition to promoting sparseness,
this prior allows us to explore the presence of change points in the structure
of the network. We introduce fast computational algorithms for estimation and
prediction using both optimization and Bayesian approaches. The performance of
the model is illustrated using simulated data and data from a financial trading
network in the NYMEX natural gas futures market. Supplementary material
containing the trading network data set and code to implement the algorithms is
available online
Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms
In temporal ordered clustering, given a single snapshot of a dynamic network
in which nodes arrive at distinct time instants, we aim at partitioning its
nodes into ordered clusters such that for , nodes in cluster arrived
before nodes in cluster , with being a data-driven parameter
and not known upfront. Such a problem is of considerable significance in many
applications ranging from tracking the expansion of fake news to mapping the
spread of information. We first formulate our problem for a general dynamic
graph, and propose an integer programming framework that finds the optimal
clustering, represented as a strict partial order set, achieving the best
precision (i.e., fraction of successfully ordered node pairs) for a fixed
density (i.e., fraction of comparable node pairs). We then develop a sequential
importance procedure and design unsupervised and semi-supervised algorithms to
find temporal ordered clusters that efficiently approximate the optimal
solution. To illustrate the techniques, we apply our methods to the vertex
copying (duplication-divergence) model which exhibits some edge-case challenges
in inferring the clusters as compared to other network models. Finally, we
validate the performance of the proposed algorithms on synthetic and real-world
networks.Comment: 14 pages, 9 figures, and 3 tables. This version is submitted to a
journal. A shorter version of this work is published in the proceedings of
IEEE International Symposium on Information Theory (ISIT), 2020. The first
two authors contributed equall
Temporal stability of network partitions
We present a method to find the best temporal partition at any time-scale and
rank the relevance of partitions found at different time-scales. This method is
based on random walkers coevolving with the network and as such constitutes a
generalization of partition stability to the case of temporal networks. We show
that, when applied to a toy model and real datasets, temporal stability
uncovers structures that are persistent over meaningful time-scales as well as
important isolated events, making it an effective tool to study both abrupt
changes and gradual evolution of a network mesoscopic structures.Comment: 15 pages, 12 figure
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