xiii, 313 : il; 24 cm

The diamond transport is generated by the uniform law on a diamond-shaped copula support. Since a classical optimal transport (OT) objective is affine in the coupling, this transport cannot be the unique minimizer in the classical setting. We study a broader family of transports, called diamond-type transports, in non-classical settings such as quadratic-form optimal transport (QOT) and distorted optimal transport (DOT), which are generally nonconvex. Our main results are within the QOT framework: for symmetric one-dimensional marginals, the diamond transport is an optimizer for a large class of QOT problems whose costs depend on within-coordinate distances. Examples include product costs under positive-definiteness and convexity conditions and, in particular, mixed rectangular costs. For rectangular costs, we show that the diamond transport is the unique minimizer except for boundary cases. In the DOT framework, diamond-type transports are minimizers for a natural class of cost, and the diamond transport is the unique minimizer in specialized examples. We also identify the intersection between DOT and QOT, which corresponds precisely to quadratic distortion functions.

John D. Edwards, editor.xiv, 865 p. : ill., maps ; 29 cm

Strong gradient regions in tokamaks, such as the pedestal or internal transport barriers, are regions of reduced turbulence where neoclassical transport can play a dominant role. However, standard neoclassical transport theory assumes that the gradient length scales of density, temperature, and potential are of the order of the system size. In the pedestal, gradient length scales are much shorter and are measured to be of the order of the ion poloidal gyroradius. We present an extension of neoclassical theory that is applicable in transport barriers of large aspect ratio tokamaks. We show that particle and momentum transport are connected in such a way that a source of parallel momentum can drive a significant neoclassical ion particle flux. In strong gradient regions, density, electric potential, mean parallel flow, and ion temperature are shown to no longer be flux functions. Instead, they have a small but important poloidally varying piece that modifies the transport equations to lowest order. This introduces a nonlinearity in the transport problem through the coupling with quasineutrality that yields multiple co-existing solutions when solving for the plasma profiles. The different solutions could be connected to low and high transport states and jumps between solutions could be an indication of H-L back-transitions.

In this talk, we propose a general model for solving the Transportation Network Design Problem (TNDP). Since in real-scale networks the number of feasible solutions to be examined does not allow an exhaustive search and objective functions are not convex, it is necessary to adopt metaheuristic algorithms to obtain sub-optimal solutions within suitable calculation times. Hence, we show and analyse some algorithms proposed in the literature for solving TNDPs both in urban and extra-urban contexts in order to highlight the importance of metaheuristic algorithms in large-scale transportation problems

Under mild regularity assumptions, the transport problem is stable in the following sense: if a sequence of optimal transport plans $π_1, π_2, \ldots$ converges weakly to a transport plan $π$, then $π$ is also optimal (between its marginals). Alfonsi, Corbetta and Jourdain asked whether the same property is true for the martingale transport problem. This question seems particularly pressing since martingale transport is motivated by robust finance where data is naturally noisy. On a technical level, stability in the martingale case appears more intricate than for classical transport since optimal transport plans $π$ are not characterized by a `monotonicity'-property of their support. In this paper we give a positive answer and establish stability of the martingale transport problem. As a particular case, this recovers the stability of the left curtain coupling established by Juillet. An important auxiliary tool is an unconventional topology which takes the temporal structure of martingales into account. Our techniques also apply to the the weak transport problem introduced by Gozlan, Roberto, Samson and Tetali.

An Artificial neural network (ANN) is a mathematical model that imitates biological neural network properties, and thus approximates operations of the human brain. Neural networks application help to solve difficult problems that the human brain or other computational techniques can’t. Neural networks are being widely used to real world problems; one of them is transportation systems. Nowadays the biggest problem for many cities is a poorly organized traffic management system, traffic flows rapidly increase in last years and cities are often not ready for this. ANN application can give a possibility to improve, optimise the transportation system. ANN’s have already been successfully applied for different prediction, forecasting, classification, detection and estimation tasks. In the paper, the neural networks based approaches (Multilayer, The Kohonen maps, Radial Basis function) for solving different transportation planning problems such as traffic flow, travel time and passenger demand forecasting, sign and moving object on the road recognition, incident detection are describe

Using McCann's transportation map, we establish a transport inequality on compact manifolds with positive Ricci curvature. This inequality contains the sharp spectral comparison estimates.

A critical function of intelligent transportation systems is studying and analyzing the effects of road condition variables (e.g. construction, severe weather, and the like) on traffic to aid in improving road designs, estimating travel time, and increasing safety. In this thesis, Multivariate Distance Matrix Regression (MDMR), a well-studied algorithm applied in brain research, is explored and applied in the transportation domain to assess the relationship and the effects of traffic conditions on transportation system performance. The Multivariate Distance Matrix Regression (MDMR) is utilized to study the relationship between input experimental factors and the association of response variables. When studying transportation, input factors can be represented as any factor that may have an effect on traffic, and response variables can be represented by traffic speed values over time for each segment of a road. The output is represented as a probability Value (P-Value) for each segment of the road as an indication of an effect of the studied factor on that specific segment. The National Performance Management Research Dataset (NPMRDS), (i.e., a probe-based traffic dataset) was used to study traffic performance based on specific factors by applying MDMR under different traffic scenarios.Moreover, a novel clustering algorithm for time series data is proposed by optimizing the F-statistic (i.e., a measurement metric to study the significance difference of two or more groups) to find the best segregation of time series between two or more groups. The clustering algorithm gave promising preliminary results when compared with K-means

We introduce z-transportability, the problem of estimating the causal effect of a set of variables X on another set of variables Y in a target domain from experiments on any subset of controllable variables Z where Z is an arbitrary subset of observable variables V in a source domain. z-Transportability generalizes z-identifiability, the problem of estimating in a given domain the causal effect of X on Y from surrogate experiments on a set of variables Z such that Z is disjoint from X;. z-Transportability also generalizes transportability which requires that the causal effect of X on Y in the target domain be estimable from experiments on any subset of all observable variables in the source domain. We first generalize z-identifiability to allow cases where Z is not necessarily disjoint from X. Then, we establish a necessary and sufficient condition for z-transportability in terms of generalized z-identifiability and transportability. We provide a correct and complete algorithm that determines whether a causal effect is z-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the causal effect of X on Y in the target domain using information elicited from the results of experimental manipulations of Z in the source domain and observational data from the target domain. Our results also show that do-calculus is complete for z-transportability.

A common problem in the pediatric primary care setting is missed appointments. Missed appointments have been shown to decrease health outcomes for patients as well as decrease productivity and financial loss for the provider. This scholarly project aimed to decrease missed appointments through utilization of a transportation service, Uber Health through a pilot project at one pediatric primary care office. During a two-month period, the social worker was able to use Uber Health as a tool to aid in patient transportation to and from the clinic. Results showed there was a cost reduction when compared with taxi services as well as a decrease in missed appointments when compared with the same time period of the year prior.Manuscrip

Unbalanced optimal transport (UOT) extends classical optimal transport to measures with different total masses, but statistical guarantees for Monge-type estimation remain limited. We study unbalanced transport with quadratic cost and Kullback-Leibler marginal penalties and argue that the natural population target is not a map alone, but a transport-growth pair. Consequently, we develop two estimators for the transport-growth pairs under several setups: an optimal transport plan-based estimator for a general case, and a kernel-based estimator for a case with smooth densities. We also show that an error of the estimator achieves the minimax optimal rate by deriving a matching lower bound of the minimax risk. Our main technical contribution is a value-based stability reduction that converts perturbations of the UOT objective into transport and growth risks through a UOT gap condition. These results provide a statistical foundation for Monge-type estimation in unbalanced optimal transport.

P(論文)There is a trend toward that world's manufacturing sites are moving to East Asia. After the production, these products are transported to the adavanced nations for their consumption demands. Among such advanced nations, U.S.A. has the largest demand and then Japan and other European countries follow. It should be noted that these Asian districts used for the production sites are rather restricted. Therefore, the volume of products transported from these restricted districts of Asia to U.S.A. is becoming tremendously large. This situation is causing very serious taraffic problems. The new products are required to be transported swiftly by air. Once the consumption and market demands are stable, the products should be sent rather slowly but in larger amount. However, the airports of China are quite restricted and while the amount of transportation is becoming large. As a result, transportation cost is increasing and also the time for transportation is increasing. Here, the third method is appearing. This is the so-called Sea and Air transportation. The cost and time in transit take mean positions between Air and Ocean fleight servises. There ecists no reasonable strategy how allocate these three methods for the taransportation. This paper is an attempt to theoretically describe this mechanism and to find out the optimal result.departmental bulletin pape

Single-molecule junctions - nanoscale systems where a molecule is connected to metallic electrodes - offer a unique platform for studying charge, spin and energy transport in non-equilibrium many-body quantum systems, with few parallels in other areas of condensed matter physics. Over the past decades, these systems have revealed a wide range of remarkable quantum phenomena, including quantum interference, non-equilibrium spin-crossover, diode-like behavior, or chiral-induced spin selectivity, among many others. To develop a detailed understanding, it turned out essential to have available ab initio-based tools for accurately describing quantum transport in such systems. They need to be capable of capturing the intricate electronic structure of molecules, sometimes in the presence of electron-electron or electron-phonon interactions, in out-of-equilibrium environments. Such tools are indispensable also for experimentally observed phenomena explained in terms of parametrized tight-binding models for the quantum transport problem. While FHI-aims also offers specialized transport routines, e.g. for chemically functionalized nanotubes or nanotube networks, our focus in this section is on the AITRANSS package designed for simulations of single-molecule transport. AITRANSS is an independent post-processing tool that combined with FHI-aims enables the calculation of electronic transport properties, as well as atom-projected density of states, spin properties and the simulation of scanning tunneling microscope images in molecular junctions. Pilot versions of the code extend some of these capabilities to non-linear transport in the applied bias, with plans to include these features in future releases of the package.

openThis paper, written during the internship experience, analyzes the logistics flows of the shipping process of Verallia, a leading manufacturer of glass packaging for beverages and food products. The central problem considered in the thesis concerns the non-value-added activities and wasted time that occur during the shipping process in all Verallia plants worldwide. The lack of a common standard for all and the need for innovation led the company to create the "Easy Go" project to improve and digitalize the shipping process. The ultimate goals are standardization, increased security, elimination of waste, efficiency and creation of KPIs. The problem was addressed by analyzing and mapping the processes of the "AS IS" situation and identifying critical points and inefficiencies. From there, a new digitalized shipping process was developed in which, using new devices and apps, the elimination of wasted time and real-time data capture is aspired for. Within the paper, there is also a reference to Sixfold, another project under development for vehicle tracking. This, along with the Easy Go project are directed toward the formation of a transportation management system. Finally, in order to understand what the results would be upon implementation of the project, a direct comparison between the current and future situations and a SWOT analysis is conducted. It emerges that the company expects to partially meet all the above-mentioned goals in the short term, while in the long term it aims to fully meet all the goals and have a complete TMS for booking, load management, and vehicle tracking

In this article, we define the transport dimension of probability measures on $\mathbb{R}^m$ using ramified optimal transportation theory. We show that the transport dimension of a probability measure is bounded above by the Minkowski dimension and below by the Hausdorff dimension of the measure. Moreover, we introduce a metric, called "the dimensional distance", on the space of probability measures on $\mathbb{R}^m$. This metric gives a geometric meaning to the transport dimension: with respect to this metric, we show that the transport dimension of a probability measure equals to the distance from it to any finite atomic probability measure.

textEconomic analyses of transportation investments are a common component of transportation planning. The profile of economic analyses has continued to rise due to increasing budget constraints and increasing emphasis on infrastructure's role in spurring economic development. One tool that has been developed specifically for transportation economic impact evaluation is the Transportation Economic Development Impact System (TREDIS). Common inputs for TREDIS are changes in travel characteristics caused by network improvements. Another commonly used planning tool is a travel demand model. Since 2003, the Texas Department of Transportation (TXDOT) has worked on the development of the Statewide Analysis Model (SAM) which estimates travel characteristics for passenger and freight modes. Together, the models have the potential to improve project evaluation and to highlight the roles that certain projects have in creating economic development. The hypothesis explored for this study is that SAM and TREDIS can be feasibly used together to help TXDOT evaluate the economic impacts of investments in freight corridors and other transportation investments. This report begins with general discussions of transportation economic analyses and the two models used. Then, the current SAM is evaluated using TREDIS followed by a discussion of the results and ways that this type of integrated planning can be incorporated by agencies. Through this study, key results include: the process of incorporating results from the SAM for use in TREDIS is feasible, an overall benefit-cost ratio of 4 for the entire set of long range roadway projects included in the SAM and 8 for only the interstate projects included in the SAM, rail and other transportation analyses are feasible using SAM and TREDIS.Civil, Architectural, and Environmental Engineerin

We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal transport algorithm on toy examples and on the unpaired image-to-image translation.

In our environment, a manufacturer procures material from a supplier and the supplier brings it in bulk to a warehouse. This material is then consigned to the plant area, where it is utilized as an input of the production process. This consignment process is outsourced by the manufacturer and a transportation company is selected via a bidding mechanism. Primarily, we consider the problem of designing parameters of a given contract for the transportation activity. We define three subproblems within the contract design problem that interact with each other to a certain extent. These subproblems are the vehicle dispatching problem, inventory control problem, and contract value problem. We define these problems, exploit their interactions, and propose solution methods. Moreover, we present an approach to design such transportation contracts, which is based on solving these subproblems in an order for an adequate number of contract parameter combinations and selecting the one that minimizes expected total costs for the manufacturer

We present a mathematically simple procedure for explaining and visualizing the dynamics of quantized transport in topological insulators. The procedure serves to illustrate and clarify the dynamics of topological transport in general, but for the sake of concreteness, it is phrased here in terms of electron transport in a charge-ordered chain, which may be mapped exactly onto transport between edge channels in the Integer Quantum Hall Effect. It has the advantage that it allows a direct visualization of the real-space and real-time evolution of the electronic charges throughout the topological pumping cycle, thus demystifying how charge flows between remote edges separated by an insulating bulk, why the amount of transported charge is given by a topological invariant, and how continuous driving yields a discrete, quantized amount of transported charge.