Adaptive Kalman Filtering for Multi-Step ahead Traffic Flow Prediction

International audienceGiven the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring

A New Robust Approach for Highway Traffic Density Estimation

International audienceIn this paper we present a robust mode selector for the uncertain graph-constrained Switching Mode Model (SMM), which we use to describe the highway traffic density evolution. Assuming an uncertain speed of the congestion wave, the proposed selector relies on a transition digraph suitably incorporating the present and historical statistical traffic information, to determine the most probable current mode of the SMM. Its effectiveness is demonstrated on the problem of traffic density reconstruction via a switching observer, in an instrumented 2.2 km highway section of Grenoble south ring in France

Grenoble Traffic Lab: An experimental platform for advanced traffic monitoring and forecasting

International audienceThis paper describes the main features of the "Grenoble Traffic Lab" (GTL), a new experimental platform for the collection of traffic data coming from a dense network of wireless sensors installed in the south ring of Grenoble, in France. The main challenges related to the configuration of the platform and data validation are discussed, and two relevant traffic monitoring and forecasting applications are presented to illustrate the operation of GTL

Adaptive Kalman Filtering for Multi-Step ahead Traffic Flow Prediction

International audienceGiven the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring

Graph constrained-CTM observer design for the Grenoble south ring

International audienceAn important problem in traffic estimation, forecasting, and control is the reconstruction of densities in portions of the road links not equipped with sensors. In this paper, and based on ideas from Morarescu and Canudas-de Wit [2011], we use a deterministic constrained model that reduces the number of possible affine dynamics of the system and preserves the number of vehicles in the network. In particular we reformulate the idea in Morarescu and Canudas-deWit [2011] with the correct number of feasible modes, and introduce the concept of graph constrained-CTM observer, which is used to reconstruct the densities from the Grenoble south ring use case that contains 45 cells organized in 9 links, and is simulated using a calibrated AISUM micro-simulator. This work is performed in connection with HYCON2 traffic show case (www.hycon2.eu), and with the Grenoble Traffic Lab (GTL) (http://necs.inrialpes.fr/pages/reseach/gtl.php)

A many-body singlet prepared by a central spin qubit

Controllable quantum many-body systems are platforms for fundamental investigations into the nature of entanglement and promise to deliver computational speed-up for a broad class of algorithms and simulations. In particular, engineering entanglement within a dense spin ensemble can turn it into a robust quantum memory or a computational platform. Recent experimental progress in dense central spin systems motivates the design of algorithms that use a central-spin qubit as a convenient proxy for the ensemble. Here we propose a protocol that uses a central spin to initialize two dense spin ensembles into a pure anti-polarized state and from there creates a many-body entangled state -- a singlet -- from the combined ensemble. We quantify the protocol performance for multiple material platforms and show that it can be implemented even in the presence of realistic levels of decoherence. Our protocol introduces an algorithmic approach to preparation of a known many-body state and to entanglement engineering in a dense spin ensemble, which can be extended towards a broad class of collective quantum states.Comment: 11 pages, 6 figures, and supplementary material

A Principle-based Analysis for Numerical Balancing

peer reviewedThe more recent philosophical literature concerned with foundational questions about normativity often appeals to the notion of normative reasons, or considerations that count in favor or against actions, and their interaction. The interaction between reasons is standardly conceived of in terms of weighing reasons on (normative) weight scales. Knoks and van der Torre [8] have recently proposed a formal framework that allows one to think about the interaction between reasons as a kind of inference pattern. This paper extends that framework by introducing and exploring what we call numerical balancing operators. These operators represent the weights or magnitudes of reasons by means of numbers, and they are particularly well-suited for capturing the intuition of aggregating and weighing reasons. We define a number of concrete classes of balancing operators and explore them using a principle-based analysis

Flow-based immunomagnetic enrichment of circulating tumor cells from diagnostic leukapheresis product

The clinical utility of circulating tumor cells (CTCs) is hampered by the low number of cells detected. Diagnostic leukapheresis (DLA) offers a solution but, due to the observed non-specific binding and clumping, processing of DLA samples using the CellSearch system only allows for the processing of aliquots consisting of ~ 2% of the total DLA sample per test. Here, we introduce a flow enrichment target capture Halbach-array (FETCH)-based separation method in combination with a DNase preprocessing step to capture CTCs from larger fractions of DLA products without clumping. To evaluate the FETCH method, we processed peripheral blood samples from 19 metastatic castration-naïve prostate cancer (mCNPC) patients with CellSearch, and processed 2% aliquots of leukapheresis samples from the same patients with CellSearch as well as FETCH with or without DNase preprocessing. Using 2% aliquots from six patients, the use of FETCH with fewer immunomagnetic epithelial cellular adhesion molecule (EpCAM) conjugated ferrofluids was tested, whereas 20% aliquots from four patients were used to evaluate the processing of 10-fold larger DLA samples using FETCH. Results show that the cell clumping normally seen after immunomagnetic enrichment of DLA material was greatly reduced with the use of DNase pretreatment, while the number of CTCs detected was not affected. The number of CTCs detected in 2% aliquots of DLA using FETCH was unchanged compared to CellSearch and did not decrease when using down to 10% of the volume of immunomagnetic anti-EpCAM ferrofluids normally used in a CellSearch test, whereas the number of co-enriched white blood cells reduced a median 3.2-fold. Processing of a 20% aliquot of DLA with FETCH resulted in a 14-fold increase in CTCs compared to the processing of 2% aliquots of DLA using CellSearch and a total 42-fold median increase in CTCs compared to peripheral-blood CellSearch.</p