7,526 research outputs found
A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation
Origin-Destination Matrix (ODM) estimation is a classical problem in
transport engineering aiming to recover flows from every Origin to every
Destination from measured traffic counts and a priori model information. In
addition to traffic counts, the present contribution takes advantage of probe
trajectories, whose capture is made possible by new measurement technologies.
It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the
information about the flow distribution on links and containing inherently the
ODM assignment. Further, an original formulation of LODM estimation, from
traffic counts and probe trajectories is presented as an optimisation problem,
where the functional to be minimized consists of five convex functions, each
modelling a constraint or property of the transport problem: consistency with
traffic counts, consistency with sampled probe trajectories, consistency with
traffic conservation (Kirchhoff's law), similarity of flows having close
origins and destinations, positivity of traffic flows. A primal-dual algorithm
is devised to minimize the designed functional, as the corresponding objective
functions are not necessarily differentiable. A case study, on a simulated
network and traffic, validates the feasibility of the procedure and details its
benefits for the estimation of an LODM matching real-network constraints and
observations
Continuity of the Effective Path Delay Operator for Networks Based on the Link Delay Model
This paper is concerned with a dynamic traffic network performance model,
known as dynamic network loading (DNL), that is frequently employed in the
modeling and computation of analytical dynamic user equilibrium (DUE). As a key
component of continuous-time DUE models, DNL aims at describing and predicting
the spatial-temporal evolution of traffic flows on a network that is consistent
with established route and departure time choices of travelers, by introducing
appropriate dynamics to flow propagation, flow conservation, and travel delays.
The DNL procedure gives rise to the path delay operator, which associates a
vector of path flows (path departure rates) with the corresponding path travel
costs. In this paper, we establish strong continuity of the path delay operator
for networks whose arc flows are described by the link delay model (Friesz et
al., 1993). Unlike result established in Zhu and Marcotte (2000), our
continuity proof is constructed without assuming a priori uniform boundedness
of the path flows. Such a more general continuity result has a few important
implications to the existence of simultaneous route-and-departure choice DUE
without a priori boundedness of path flows, and to any numerical algorithm that
allows convergence to be rigorously analyzed.Comment: 12 pages, 1 figur
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
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
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