Impact of nuclear data uncertainties in the Phase-1B Benchmark

Accurate control over the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. Consequently, the reactivity of spent fuel and its isotopic content must be accurately determined. Nowadays, to predict isotopic evolution throughout irradiation and decay periods is not a problem thanks to the development of powerful codes and methodologies. In order to have a realistic confidence level in the prediction of spent fuel isotopic content, it is desirable to determine how uncertainties in the basic nuclear data affect isotopic prediction calculations by quantifying their associated uncertainties

Impact of Nuclear Data Uncertainties in the Phase-1B Benchmark

Accurate control over the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. Consequently, the reactivity of spent fuel and its isotopic content must be accurately determined.

Isotopic uncertainty assessment due to nuclear data uncertainties in high-burnup samples.

The accurate prediction of the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. This isotopic evolution can be predicted using powerful codes and methodologies throughout irradiation as well as cooling time periods. However, in order to have a realistic confidence level in the prediction of spent fuel isotopic content, it is desirable to determine how uncertainties affect isotopic prediction calculations by quantifying their associated uncertainties.

High-Speed Rail Versus Air Transportation: Case Study of Madrid–Barcelona, Spain.

Travel time savings, better quality of the supplied services, greater comfort for the users, and improved accessibility are the main factors of success of High Speed Rail(HSR)links. This paper presents the results from a revealed and stated preference survey conducted to both HSR and air transport users in the Madrid Barcelona corridor. The data gathered from the stated preference survey was used to calibrate a modal choice model aiming at explaining competition between HSR and air transportation in the corridor. From the model, the authors obtain that prices and service frequency are the most important variables to compete with the other mode. In addition, they found that check-in and security controls at the airport are a crucial variable for the users in their modal choice. Other policies, such as the improvement of parking facilities at the train stations, play a secondary role.

Transportation infrastructure impacts on firm location: the effect of a new metro line in the suburbs of Madrid

Firm location patterns emerge as a consequence of multiple factors, including firm considerations, labor force availability, market opportunities, and transportation costs. Many of these factors are influenced by changes in accessibility wrought by new transportation infrastructure. In this paper we use spatial statistical techniques and a micro-level data base to evaluate the effects of Madrid?s metro line 12 (known as Metrosur) expansion on business location patterns. The case study is the municipality of Alcorcon, which is served by the new metro line since 2003. Specifically, we explore the location patterns by different industry sectors, to evaluate if the new metro line has encouraged the emergence of a ?Metrosur spatial economy?. Our results indicate that the pattern of economic activity location is related to urban accessibility and that agglomeration, through economies of scale, also plays an important role. The results presented in this paper provide evidence useful to inform efficient transportation, urban, and regional economic planning.

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles.

Variable Speed Limits. Review and development of an aggregate indicator based on floating car data.

Changing factors (mainly traffic intensity and weather conditions) affecting road conditions require a suitable optimal speed at any time. To solve this problem, variable speed limit systems (VSL) ? as opposed to fixed limits ? have been developed in recent decades. This term has included a number of speed management systems, most notably dynamic speed limits (DSL). In order to avoid the indiscriminate use of both terms in the literature, this paper proposes a simple classification and offers a review of some experiences, how their effects are evaluated and their results This study also presents a key indicator, which measures the speed homogeneity and a methodology to obtain the data based on floating cars and GPS technology applying it to a case study on a section of the M30 urban motorway in Madrid (Spain).

Neighborhood mobility: A Long-Gone Pardigm? Approaching proximity and land use mix using data from home based surveys.

El WCTR es un congreso de reconocido prestigio internacional en el ámbito de la investigación del transporte y aunque las actas publicadas están en formato digital y sin ISSN ni ISBN, lo consideramos lo suficientemente importante como para que se considere en los indicadores. Policies trying to increase walking within urban mobility modal split usually highlight the importance of the functional patterns and the environmental quality of the urban space as major drivers of citizens modal choices. Functional characteristics would be mainly associated to an appropriate mix of land uses within neighbourhoods, whereas environmental quality would be associated to the characteristics of urban spaces. The purpose of this research is threefold: first, to identify relevant proxy indicators, which could characterize pedestrian-friendly land use mix and environmental quality. Second, to assess, for both traits, existing disparities among neighbourhoods in a major metropolitan area. And finally, to explore the association between both indicators and children mobility patterns: according to their built environment, which neighbourhoods have a greater proportion of children and, how is their mobility? Using data from the 2004 household mobility survey in the 128 neighbourhoods of the municipality of Madrid, this paper concludes that potentially favourable conditions at the neighbourhood level seem to have only a modest influence in,mobility patterns , in terms of both, selection of closer destinations and a higher share of walking within modal split. The citys policy choices, with intensive investment in road and public transport infrastructure may explain why short-distance mobility is not as important as it could have been expected in those neighbourhoods with more pedestrian-friendly conditions. The metropolitan transport system is providing mobility conditions, which make far-away destinations attractive to most citizens.

Neighbourhood mobility: a longgone Paradigm? Approaching "proximity" and "land use mix" using data from home-based surveys

Policies trying to increase walking within urban mobility modal split usually highlight the importance of the functional patterns and the environmental quality of the urban space as major drivers of citizens modal choices. Functional characteristics would be mainly associated to an appropriate mix of land uses within neighbourhoods, whereas environmental quality would be associated to the characteristics of urban spaces. The purpose of this research is threefold: first, to identify relevant proxy indicators, which could characterize pedestrian-friendly land use mix and environmental quality. Second, to assess, for both traits, existing disparities among neighbourhoods in a major metropolitan area. And finally, to explore the association between both indicators and children mobility patterns: according to their built environment, which neighbourhoods have a greater proportion of children and, how is their mobility? Using data from the 2004 household mobility survey in the 128 neighbourhoods of the municipality of Madrid, this paper concludes that potentially favourable conditions at the neighbourhood level seem to have only a modest influence in,mobility patterns , in terms of both, selection of closer destinations and a higher share of walking within modal split. The city s policy choices, with intensive investment in road and public transport infrastructure may explain why short-distance mobility is not as important as it could have been expected in those neighbourhoods with more pedestrian-friendly conditions. The metropolitan transport system is providing mobility conditions, which make far-away destinations attractive to most citizens.

Using floating car data to analyse the effects of ITS measures and eco-driving

The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied.

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patternsof this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

The Role of Citizen Participation in Wildlife-Sensitive Transportation Projects

An extensive and growing road system in the United States bisects vital wildlife habitat and is causing deleterious ecological effects on many wildlife species. The primary impacts include collisions between wildlife and vehicles, altered movement patterns within habitat, and/or the complete blockage of movements between vital habitats. The increasing size of the road network and number of vehicles will only intensify the problem unless proactive wildlife mitigation measures are developed to minimize these adverse effects. Therefore, this capstone project examines the role of citizen advocacy for promoting wildlife protection in the planning and development of wildlife-sensitive transportation projects in the United States. Based upon a data analysis of 21 questionnaires from qualified participants, it was determined that citizen participation is an important component associated with the development of wildlife-sensitive transportation projects. However, four major barriers to facilitating effective citizen participation processes were identified. 1) A lack of awareness. Citizens are only minimally aware of wildlife and transportation issues, including: a) the ecological impacts of roads, b) the solutions available to mitigate these impacts, and c) the opportunities to advocate for the protection of wildlife during transportation planning processes; 2) Public apathy or a lack of citizen interest in wildlife and transportation issues; 3) Ineffective citizen participation techniques and processes; and 4) Poor communication with citizens. Four recommendations were provided to assist in overcoming these barriers and to help define a better role for citizen advocacy in protecting wildlife from the growing road network.

Evaluation of fleet maintenance data as a data source for repairability ratings. Final report.

National Highway Traffic Safety Administration, Washington, D.C.