Optimal Pricing for Urban Road Transport Externalities

A partial equilibrium model for the urban transport market is described. The urban transport market is represented as a set of interrelated transport submarkets, one per type of mode or vehicle and period. This allows to represent in detail the different external costs associated with the use of different modes: congestion, accidents, air pollution and noise. The model allows to find second best optima that combine optimally given pricing and environmental regulation instruments. The model is demonstrated for Brussels. For this city the welfare effects of alternative sets of instruments are compared.

Optimal pricing for urban road transport externalities.

A partial equilibrium model for the urban transport market is described. The urban transport market is represented as a set of interrelated transport submarkets, one per type of mode or vehicle and period. This allows to represent in detail the different external costs associated with the use of different modes: congestion, accidents, air pollution and noise. The model allows to find second best optima that combine optimally given pricing and environmental regulation instruments. The model is demonstrated for Brussels. For this city the welfare effects of alternative sets of instruments are compared.

Utility Analysis for Optimizing Compact Adaptive Spectral Imaging Systems for Subpixel Target Detection Applications

Since the development of spectral imaging systems where we transitioned from panchromatic, single band images to multiple bands, we have pursued a way to evaluate the quality of spectral images. As spectral imaging capabilities improved and the bands collected wavelengths outside of the visible spectrum they could be used to gain information about the earth such as material identification that would have been a challenge with panchromatic images. We now have imaging systems capable of collecting images with hundreds of contiguous bands across the reflective portion of the electromagnetic spectrum that allows us to extract information at subpixel levels. Prediction and assessment methods for panchromatic image quality, while well-established are continuing to be improved. For spectral images however, methods for analyzing quality and what this entails have yet to form a solid framework. In this research, we built on previous work to develop a process to optimize the design of spectral imaging systems. We used methods for predicting quality of spectral images and extended the existing framework for analyzing efficacy of miniature systems. We comprehensively analyzed utility of spectral images and efficacy of compact systems for a set of application scenarios designed to test the relationships of system parameters, figures of merit, and mission requirements in the trade space for spectral images collected by a compact imaging system from design to operation. We focused on subpixel target detection to analyze spectral image quality of compact spaceborne systems with adaptive band selection capabilities. In order to adequately account for the operational aspect of exploiting adaptive band collection capabilities, we developed a method for band selection. Dimension reduction is a step often employed in processing spectral images, not only to improve computation time but to avoid errors associated with high dimensionality. An adaptive system with a tunable filter can select which bands to collect for each target so the dimension reduction happens at the collection stage instead of the processing stage. We developed the band selection method to optimize detection probability using only the target reflectance signature. This method was conceived to be simple enough to be calculated by a small on-board CPU, to be able to drive collection decisions, and reduce data processing requirements. We predicted the utility of the selected bands using this method, then validated the results using real images, and cross-validated them using simulated image associated with perfect truth data. In this way, we simultaneously validated the band selection method we developed and the combined use of the simulation and prediction tools used as part of the analytic process to optimize system design. We selected a small set of mission scenarios and demonstrated the use of this process to provide example recommendations for efficacy and utility based on the mission. The key parameters we analyzed to drive the design recommendations were target abundance, noise, number of bands, and scene complexity. We found critical points in the system design trade space, and coupled with operational requirements, formed a set of mission feasibility and system design recommendations. The selected scenarios demonstrated the relationship between the imaging system design and operational requirements based on the mission. We found key points in the spectral imaging trade space that indicated relationships within the spectral image utility trade space that can be used to further solidify the frameworks for compact spectral imaging systems

The new hyperspectral satellite prisma: Imagery for forest types discrimination

Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups

Study to define unsteady flow fields and their statistical characteristics

Preliminary estimates of space shuttle fluctuating pressure environments were made based on analyses of wind tunnel data, and empirical prediction techniques. Particular emphasis was given to the external tank and solid rocket boosters for the transonic speed regime during launch of a parallel-burn space shuttle configuration. Predicted environments are presented as space-averaged zonal profiles with progressive shading from zone to zone to illustrate spatial variations in the magnitude of the fluctuating pressure coefficient over the surfaces of the external tank and solid rocket boosters. Predictions are provided for the transonic Mach number range from 0.8 equal to or less than M sub infinity equal to or less than 1.5, and for supersonic Mach numbers of 2.0 and 3.0

Does Britain or the United States Have the Right Gasoline Tax?

This paper develops an analytical framework for assessing the second-best optimal level of gasoline taxation, taking into account unpriced pollution, congestion, and accident externalities and interactions with the broader fiscal system. We provide calculations of the optimal taxes for the United States and the United Kingdom under a wide variety of parameter scenarios, with the gasoline tax substituting for a distorting tax on labor income. Under our central parameter values, the second-best optimal gasoline tax is $1.01 per gallon for the United States and$1.34 per gallon for the United Kingdom. These values are moderately sensitive to alternative parameter assumptions. The congestion externality is the largest component in both nations, and the higher optimal tax for the United Kingdom is due mainly to a higher assumed value for marginal congestion cost. Revenue-raising needs, incorporated in a “Ramsey” component, also play a significant role, as do accident externalities and local air pollution. The current gasoline tax in the United Kingdom ($2.80 per gallon) is more than twice this estimated optimal level. Potential welfare gains from reducing it are estimated at nearly one-fourth the production cost of gasoline used in the United Kingdom. Even larger gains in the United Kingdom can be achieved by switching to a tax on vehicle miles with equal revenue yield. For the United States, the welfare gains from optimizing the gasoline tax are smaller, but those from switching to an optimal tax on vehicle miles are very large.gasoline tax, pollution, congestion, accidents, fiscal interactions

Multispectral persistent surveillance

The goal of a successful surveillance system to achieve persistence is to track everything that moves, all of the time, over the entire area of interest. The thrust of this thesis is to identify and improve upon the motion detection and object association aspect of this challenge by adding spectral information to the equation. Traditional motion detection and tracking systems rely primarily on single-band grayscale video, while more current research has focused on sensor fusion, specifically combining visible and IR data sources. A further challenge in covering an entire area of responsibility (AOR) is a limited sensor field of view, which can be overcome by either adding more sensors or multi-tasking a single sensor over multiple areas at a reduced frame rate. As an essential tool for sensor design and mission development, a trade study was conducted to measure the potential advantages of adding spectral bands of information in a single sensor with the intention of reducing sensor frame rates. Thus, traditional motion detection and object association algorithms were modified to evaluate system performance using five spectral bands (visible through thermal IR), while adjusting frame rate as a second variable. The goal of this research was to produce an evaluation of system performance as a function of the number of bands and frame rate. As such, performance surfaces were generated to assess relative performance as a function of the number of bands and frame rate

Comparisons Between Spectral Quality Metrics and Analyst Performance in Hyperspectral Target Detection

Quantitative methods to assess or predict the quality of a spectral image continue to be the subject of a number of current research activities. An accepted methodology would be highly desirable for use in data collection tasking or data archive searching in ways analogous to the current prediction of panchromatic image quality through the National Imagery Interpretation Rating Scale (NIIRS) using the General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published, but most capture only the performance of automated algorithms applied to the spectral data. One recently introduced metric, however, the General Spectral Utility Metric (GSUM), provides for a framework to combine the performance from the spectral aspects together with the spatial aspects. In particular, this framework allows the metric to capture the utility of a spectral image resulting when the human analyst is included in the process. This is important since nearly all hyperspectral imagery analysis procedures include an analyst. To investigate the relationships between candidate spectral metrics and task performance from volunteer human analysts in conjunction with the automated results, simulated images are generated and processed in a blind test. The performance achieved by the analysts is then compared to predictions made from various spectral quality metrics to determine how well the metrics function. The task selected is one of finding a specific vehicle in a cluttered environment using a detection map produced from the hyperspectral image along with a panchromatic rendition of the image. Various combinations of spatial resolution, number of spectral bands, and signal-to-noise ratios are investigated as part of the effort