Non-trading,lexicographic and inconsistent behaviour in stated choice data

This paper discusses a number of issues relating to the pre-analysis and cleaning of stated choice data, where we look specifically at the problems caused by non-trading, lexicographic and inconsistent response patterns. We argue that this process is considerably more complex and challenging than many in the field have hitherto acknowledged, with the standard practice being the use of rather ad hoc procedures for the identification of these phenomena. A detailed analysis on four different stated choice datasets highlights the potential impacts of these methods on model estimation results.     

Improving pollutant emissions in diesel engines for heavy-duty transportation using retarded intake valve closing strategies

A combined theoretical and experimental study has been carried out to determine the real potential of reducing pollutant emissions in a HD diesel engine by means of retarding the intake valve closing. The effects produced by this alteration of the basic operation cycle have been examined by a preliminary modelling study, and from the obtained results, a modified camshaft was manufactured with a delayed intake valve closing of 60 crank angle degrees. Single-cylinder engine tests were carried out with this modified camshaft, and the emissions and fuel consumption were recorded. The results showed that the retarded intake valve closing can enhance the premixed combustion phase, and thus simultaneously reduce soot and NOx emissions. Moreover, the combustion process attained is extremely tolerant to exhaust gas recirculation, and by adoption of this measure, Euro-5 emissions limits can be achieved at the tested conditions without after-treatment.     

Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

In the case of full vehicle models, the technique of multi-body simulation (MBS) is frequently used to study their highly non-linear dynamic behaviour. Many non-linearities in vehicle models are induced by force elements like springs, shock absorbers, bushings and tires. Commonly, spline functions are used to represent the force responses of these components. If the non-linear relationships are more complicated, the spline approximations are no more accurate. An alternative approach is based on empirical neural networks which are based on the mathematical approximation of measured data. It is well known that neural networks are able to represent and predict complex component responses accurately. The aim of this paper is to perform a dynamic full vehicle simulation using a thermomechanically coupled hybrid neural network shock absorber model. In this shock absorber model, the spline approach is combined with a temperature-dependent neural network. Based on a displacement-controlled excitation on a four post test rig in the ADAMS/Car MBS software, a rugged test track is simulated. In this way, the front and rear shock absorbers are dynamically loaded with comfort-relevant frequencies in the range of 0.75-30 Hz and velocity amplitudes up to 2 m/s. By the simulation, stability of the hybrid neural network model is demonstrated. Furthermore, the damping force, the vertical acceleration of the chassis and the required simulation times are compared. The standard spline approach is used as a reference.     

A hybrid model for better predicting capabilities under parallel wind conditions for NOx concentrations from highways in Ireland

Atmospheric dispersion models based on the Gaussian line source formulation tend to give poor results for low wind speed and parallel wind directions. Certain dispersion models like HIWAY-2 have an inbuilt dispersion algorithm to adjust in such situations but no correction exists for mathematical based Gaussian dispersion models. We evaluate the performance of a hybrid model a mathematical model, to assess its suitability as an alternative to the Gaussian based models.     

Condition monitoring of wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are to large extent based on visual analysis. In this paper a machine vision based approach has been considered to emulate the visual abilities of the human operator to enable automation of the process. Digital images from either ends (left and right) of the sleepers have been acquired. A pattern recognition approach has been adopted to classify the condition of the sleeper into classes (good or bad) and thereby achieve automation. Appropriate image analysis techniques were applied and relevant features such as the number of cracks on a sleeper, average length and width of the crack and the condition of the metal plate were determined. Feature fusion has been proposed in order to integrate the features obtained from each end for the classification task which follows. The effect of using classifiers like multi-layer perceptron and support vector machines has been tested and compared. Results obtained from the experiments show that multi-layer perceptron and support vector machines have achieved encouraging results, with a classification accuracy of 90%; thereby exhibiting a competitive performance when compared to a human operator.     

Biologically induced differences in erodibility and aggregation of subtidal and intertidal sediments: a possible cause for seasonal changes in sediment deposition

This study was carried out to describe the difference in erodibility and aggregation in a tidal basin including both subtidal and intertidal study sites and to use these results to explain the shifting erosion/deposition cycles at the sites. Erosion thresholds, erosion rates and settling velocities of the eroded material were measured at a mudflat transect and at sediment cores taken from a nearby tidal channel during surveys made in May 2000 and March 2002. Surface samples were analysed for grain-size, chl. a content, faecal pellet content, dry bulk density and organic content. Additionally, surface samples were taken at eight occasions in the period January 2002 to May 2003 from shallow tidal channels in the area. These samples were analysed for mud content and showed that major shifts in sediment distribution occurred in the period. The erodibility of the mudflat was generally high due to pelletization by the mudsnail Hydrobia ulvae but close to the salt marsh much lower erodibility was found, probably due to stabilisation by microphytobenthos. In contrast, the erodibility of the channel bed seemed to be very little influenced by biological activity and the relatively low erodibility found here was caused by physical characteristics of the sediment. The sediment eroded from the mudflat was generally strongly pelletized and showed high settling velocities whereas less aggregation and lower settling velocities were found for the channel bed sediments. Temporal variations of the mudflat stability and hydrodynamics resulted in temporal variations of deposition and erosion and the changing stability at the mudflat is likely to be one of the main reasons for a general transport of fine-grained sediment from the mudflat to the channel in the cold seasons and vice versa during the rest of the year.     

Spatial fisheries ecology: Recent progress and future prospects

We review recent progresses made in the study of fish distribution and survival over space — i.e., fisheries spatial ecology. This is achieved by first surveying the most common statistical approaches and relative challenges associated with the analysis of fisheries spatial data, loosely grouped in geostatistical and regression approaches. Then we review a selected number of case-studies implementing the discussed techniques. We conclude by proposing new areas of statistical and ecological research to further our understanding of how fish distribute and survive in space. This review serves a dual purpose by emphasizing the scientific importance of studying spatial interactions to better understand the temporal dynamics of fish abundance, and by promoting the development of new analytical and ecological approaches for the analysis of spatial data. Through our survey we cover different statistical techniques, marine ecosystems and life stages. This analytical, geographic and ontogenetic variety is also purposely selected to highlight the importance of comparative and multidisciplinary studies across diverging ecological disciplines, ecosystems and life stages. Besides having a general ecological relevance this review also bears a more applied significance, owing to the increasing need for protecting renewable marine resources along with their primary habitat.     

Measuring the potential for automobile fuel savings in the US: The impact of obesity

High rates of oil consumption and obesity in the US have become important socioeconomic concerns. While these concerns may seem unrelated at first, growing obesity rates in the US increase fuel consumption by adding passenger weight to vehicles. This paper estimates the additional amount of fuel (i.e., gasoline) consumed annually by noncommercial passenger highway vehicles in the US due to passenger overweight and obesity. The mathematical model presented in this paper estimates that as many as one billion additional gallons of gasoline are consumed each year due to overweight and obesity in the US, accounting for up to 0.8% of the fuel consumed by these vehicles annually. This additional fuel consumption causes carbon dioxide emissions of up to 20 billion pounds or more, accounting for up to 0.5% of the annual carbon dioxide emissions in the transportation sector.     

Topological analysis of powertrains for refuse-collecting vehicles based on real routes–Part I: Hybrid hydraulic powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on the simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this first part, a characterization of a standard route is performed, analyzing the average power consumption and the most frequent working points of an internal combustion engine (ICE) in real routes. This information is used to define alternative powertrain architectures. A hybrid hydraulic powertrain architecture is proposed and modelled. The proposed powertrain model is executed using two different control algorithms, with and without predictive strategies, with data obtained from real routes. A calculation engine (an algorithm which runs the vehicle models on real routes), is presented and used for simulations. This calculation engine has been specifically designed to analyze if the different alternative powertrain delivers the same performance of the original ICE. Finally, the overall performance of the different architectures and control strategies are summarized into a fuel and energy consumption table, which will be used in the second part of this paper to compare with the different architectures based on hybrid electric powertrain. The overall performance of the different architectures indicates that the use of a hybrid hydraulic powertrain with simple control laws can reduce the fuel consumption up to a 14 %.