Expansion of congested railway capacity using triple-track line operation based on hourly passenger demand

This paper proposes a method of improving level of service in congested urban railways by means of a triple-track line operation for a highly dense urban area with special travel demand characteristics. Where the future travel demand forecasts show sluggish growth or no growth at all, there is little to no incentives for heavy railway investments like quadruple-track extension and construction of new railway routes to alleviate current railway congestion problems. In such a situation, triple-track line operation can be the best alternative due to its moderate investment cost and ease in land acquisition for just an additional single track along the existing tracks. Our simulation investigation in one of the congested railway lines in Tokyo showed that triple track line operation increases railway capacity by 26% and shortens travel time by 38% in peak direction during morning peak hours. These results are encouraging and are useful for removing current railways problems in Tokyo and in similar urban situations elsewhere.

The impact of trade liberalization on import demand

In this paper, we empirically analyze the effects of trade reforms on import demand and derive their implications on economic development in Turkey, a country that underwent sudden and substantial trade liberalization in the mid-1980s. The tool for this analysis is the estimation of disaggregated import demand elasticities. The adoption of a more liberal trade regime as well as radical attempts to foster economic development makes the Turkish experience particularly interesting for analysis. Almost all of our elasticities are estimated to be significant, unlike those of most previous studies in the literature on other countries. We test for different elasticities over “closed” and “open” economy periods, and find that the effects of the trade reforms of the 1980s were significant for a number of industries that form the backbone of the Turkish economy. We also compare our results with elasticity estimates from past studies for developed countries.

Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting

This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.

The functional significance of ventilation frequency, and its relationship to oxygen demand in the resting brown long-eared bat, Plecotus auritus

Mean oxygen consumption and simultaneous ventilation frequency of nine non-reproductive brown long-eared bats (body mass 8.53–13.33 g) were measured on 159 occasions. Ambient (chamber) temperature at which the measurements were made ranged from 10.8 to 41.1°C. Apneic ventilation occurred in 22 of the 59 measurements made when mean oxygen consumption was less than 0.5 ml·min-1. No records of apneic ventilation were obtained when it was over 0.5 ml·min-1. The relationship between ventilation frequency and mean oxygen consumption depended on whether ventilation was apneic or non-apneic. When ventilation was non-apneic the relationship was positive and log-linear. When ventilation was apneic the relationship was log-log. Within the thermoneutral zone ventilation frequency was not significantly different from that predicted from allometric equations for a terrestrial mammal of equivalent body mass, but was significantly greater than that predicted for a bird. A reduction in the amount of oxygen consumed per breath occurred at ambient temperatures above the upper critical temperature (39°C).

Bayesian model averaging of load demand forecasts from neural network models

Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.

Examining performance of aggregation algorithms for neural network-based electricity demand forecasting

The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with certain modifications and are employed on the forecasts obtained from all individual NN models. The output of the aggregation algorithms is analyzed and compared with the individual NN models used in NN ensemble and with a Naive approach. Thirty-minutes interval electricity demand data from Australian Energy Market Operator (AEMO) and the New York Independent System Operator's web site (NYISO) are used in the empirical analysis. It is observed that the aggregation algorithm perform better than many of the individual NN models. In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.

Experimental and stochastic approaches to assessing the strain demand of pipelines and flexibility requirements for coatings

A criterion for selecting a coating for an energy pipeline is that the coating should have a suitable flexibility to meet the high strain demand during hydrostatic testing and during field bending. This requires knowledge of the level of strain demand for the pipeline, and also the maximum strain that could be
tolerated by the coating system. Whereas average strains imposed during manufacturing and construction are reasonably well predicted, there is insufficient understanding on the factors leading to localised deformation of the pipe. Significant work has been carried out in the past to develop tests for assessing
the coatings’ ability to handle a certain amount of strain based on bend testing, tensile testing and burst testing. However, there is a concern as to whether these tests properly represent localised micro-strains associated with construction activities including field bending and pressure testing, particularly pressure testing of pipelines designed for operation at 80% of specified minimum yield strength (SMYS). Consequently coatings considered "suitable" for modern pipelines may fail. The first issue discussed in this paper is main factors affecting strain localisation. The non-deterministic distributions of heterogeneities over the pipe provide a ground to consider the mechanisms of localisation as a stochastic process. An approach is proposed to quantify the maximum localised strain demand through cold field bending and hydrostatic experiments. Another issue discussed in this paper is the experimental assessment of coating flexibility under the effects of localised strains. Preliminary mandrel tests have been carried out to assess the uniformity of the imposed strain. Although mandrel testing has been shown to be a useful method for relative comparison of coating flexibility, it has several weaknesses that could significantly affect the reliability and reproducibility of the results.

Student voice in 'skills for sustainability': a missing component from the demand side of Australian vocational education and training

The implementation of the Green Skills Agreement ratified by the Council of Australian Governments (COAG) in 2010 provides the national policy context for this analysis of skills for sustainability. Data from three different but complementary studies provide powerful insight into the attitudes and perceptions of young people who are studying, or are recent graduates of, Australian Vocational Education and Training (VET) programs. We argue that the voices of the young people who participate as students are largely absent from analysis and policy-making, despite policy rhetoric about a demand driven Australian tertiary education sector responsive to consumer (student) interest and need. The combination of these three studies contributes to an improved understanding of what these young adults think and are learning with regard to skills for sustainability in their VET courses and in their workplaces. Most notably, these VET students reported that increasingly changes around skills for sustainability are being implemented into both their work roles and their courses of study.