Tracking rail safety : incident and accident investigation in Australia

Scoping Project: Currently no national or structured learning framework available in Aus or NZ Current Project: Develop a national training program & capability framework for rail incident investigators - Establish the potential market demand - Define the curricula for a multi-level national training program - Explore training providers & delivery options

An integrated rail track degradation model

There has been a worldwide trend to increase axle loads and train speeds. This means that railway track degradation will be accelerated, and track maintenance costs will be increased significantly. There is a need to investigate the consequences of increasing traffic load. The aim of the research is to develop a model for the analysis of physical degradation of railway tracks in response to changes in traffic parameters, especially increased axle loads and train speeds. This research has developed an integrated track degradation model (ITDM) by integrating several models into a comprehensive framework. Mechanistic relationships for track degradation hav~ ?een used wherever possible in each of the models contained in ITDM. This overcc:mes the deficiency of the traditional statistical track models which rely heavily on historical degradation data, which is generally not available in many railway systems. In addition statistical models lack the flexibility of incorporating future changes in traffic patterns or maintenance practices. The research starts with reviewing railway track related studies both in Australia and overseas to develop a comprehensive understanding of track performance under various traffic conditions. Existing railway related models are then examined for their suitability for track degradation analysis for Australian situations. The ITDM model is subsequently developed by modifying suitable existing models, and developing new models where necessary. The ITDM model contains four interrelated submodels for rails, sleepers, ballast and subgrade, and track modulus. The rail submodel is for rail wear analysis and is developed from a theoretical concept. The sleeper submodel is for timber sleepers damage prediction. The submodel is developed by modifying and extending an existing model developed elsewhere. The submodel has also incorporated an analysis for the likelihood of concrete sleeper cracking. The ballast and subgrade submodel is evolved from a concept developed in the USA. Substantial modifications and improvements have been made. The track modulus submodel is developed from a conceptual method. Corrections for more global track conditions have been made. The integration of these submodels into one comprehensive package has enabled the interaction between individual track components to be taken into account. This is done by calculating wheel load distribution with time and updating track conditions periodically in the process of track degradation simulation. A Windows-based computer program ~ssociated with ITDM has also been developed. The program enables the user to carry out analysis of degradation of individual track components and to investigate the inter relationships between these track components and their deterioration. The successful implementation of this research has provided essential information for prediction of increased maintenance as a consequence of railway trackdegradation. The model, having been presented at various conferences and seminars, has attracted wide interest. It is anticipated that the model will be put into practical use among Australian railways, enabling track maintenance planning to be optimized and potentially saving Australian railway systems millions of dollars in operating costs.

Obstacle-free range determination for rail track maintenance vehicles

Maintenance trains travel in convoy. In Australia, only the first train of the convoy pays attention to the track sig- nalization (the other convoy vehicles simply follow the preceding vehicle). Because of human errors, collisions can happen between the maintenance vehicles. Although an anti-collision system based on a laser distance meter is already in operation, the existing system has a limited range due to the curvature of the tracks. In this paper, we introduce an anti-collision system based on vision. The two main ideas are, (1) to warp the camera image into an image where the rails are parallel through a projective transform, and (2) to track the two rail curves simultaneously by evaluating small parallel segments. The performance of the system is demonstrated on an image dataset.

Conducting statistical tests of hypotheses : five common misconceptions found in transportation research

Most statistical methods use hypothesis testing. Analysis of variance, regression, discrete choice models, contingency tables, and other analysis methods commonly used in transportation research share hypothesis testing as the means of making inferences about the population of interest. Despite the fact that hypothesis testing has been a cornerstone of empirical research for many years, various aspects of hypothesis tests commonly are incorrectly applied, misinterpreted, and ignored—by novices and expert researchers alike. On initial glance, hypothesis testing appears straightforward: develop the null and alternative hypotheses, compute the test statistic to compare to a standard distribution, estimate the probability of rejecting the null hypothesis, and then make claims about the importance of the finding. This is an oversimplification of the process of hypothesis testing. Hypothesis testing as applied in empirical research is examined here. The reader is assumed to have a basic knowledge of the role of hypothesis testing in various statistical methods. Through the use of an example, the mechanics of hypothesis testing is first reviewed. Then, five precautions surrounding the use and interpretation of hypothesis tests are developed; examples of each are provided to demonstrate how errors are made, and solutions are identified so similar errors can be avoided. Remedies are provided for common errors, and conclusions are drawn on how to use the results of this paper to improve the conduct of empirical research in transportation.

Influence of iodine value on combustion and NOx emission characteristics of a DI diesel engine

Biodiesel is a renewable fuel that has been shown to reduce many exhaust emissions, except oxides of nitrogen (NOx), in diesel engine cars. This is of special concern in inner urban areas that are subject to strict environmental regulations, such as EURO norms. Also, the use of pure biodiesel (B100) is inhibited because of its higher NOx emissions compared to petroleum diesel fuel. The aim of this present work is to investigate the effect of the iodine value and cetane number of various biodiesel fuels obtained from different feed stocks on the combustion and NOx emission characteristics of a direct injection (DI) diesel engine. The biodiesel fuels were chosen from various feed stocks such as coconut, palm kernel, mahua (Madhuca indica), pongamia pinnata, jatropha curcas, rice bran, and sesame seed oils. The experimental results show an approximately linear relationship between iodine value and NOx emissions. The biodiesels obtained from coconut and palm kernel showed lower NOx levels than diesel, but other biodiesels showed an increase in NOx. It was observed that the nature of the fatty acids of the biodiesel fuels had a significant influence on the NOx emissions. Also, the cetane numbers of the biodiesel fuels are affected both premixed combustion and the combustion rate, which further affected the amount of NOx formation. It was concluded that NOx emissions are influenced by many parameters of biodiesel fuels, particularly the iodine value and cetane number.

Upper-body morbidity following breast cancer treatment is common, may persist longer-term and adversely influences quality of life

Background: Impairments in upper-body function (UBF) are common following breast cancer. However, the relationship between arm morbidity and quality of life (QoL) remains unclear. This investigation uses longitudinal data to describe UBF in a population-based sample of women with breast cancer and examines its relationship with QoL. ---------- Methods: Australian women (n = 287) with unilateral breast cancer were assessed at three-monthly intervals, from six- to 18-months post-surgery (PS). Strength, endurance and flexibility were used to assess objective UBF, while the Disability of the Arm, Shoulder and Hand questionnaire and the Functional Assessment of Cancer Therapy- Breast questionnaire were used to assess self-reported UBF and QoL, respectively. ---------- Results: Although mean UBF improved over time, up to 41% of women revealed declines in UBF between sixand 18-months PS. Older age, lower socioeconomic position, treatment on the dominant side, mastectomy, more extensive lymph node removal and having lymphoedema each increased odds of declines in UBF by at least twofold (p < 0.05). Lower baseline and declines in perceived UBF between six- and 18-months PS were each associated with poorer QoL at 18-months PS (p < 0.05). ---------- Conclusions: Significant upper-body morbidity is experienced by many following breast cancer treatment, persisting longer term, and adversely influencing the QoL of breast cancer survivors.

Conflict resolution through negotiation in a railway open access market : a multi-agent system approach

Open access reforms to railway regulations allow multiple train operators to provide rail services on a common infrastructure. As railway operations are now independently managed by different stakeholders, conflicts in operations may arise, and there have been attempts to derive an effective access charge regime so that these conflicts may be resolved. One approach is by direct negotiation between the infrastructure manager and the train service providers. Despite the substantial literature on the topic, few consider the benefits of employing computer simulation as an evaluation tool for railway operational activities such as access pricing. This article proposes a multi-agent system (MAS) framework for the railway open market and demonstrates its feasibility by modelling the negotiation between an infrastructure provider and a train service operator. Empirical results show that the model is capable of resolving operational conflicts according to market demand.

Bottlenecks detection of track allocation schemes at rail stations by Petri nets

Robustness of the track allocation problem is rarely addressed in literatures and the obtained track allocation schemes (TAS) embody some bottlenecks. Therefore, an approach to detect bottlenecks is needed to support local optimization. First a TAS is transformed to an executable model by Petri nets. Then disturbances analysis is performed using the model and the indicators of the total trains' departure delays are collected to detect bottlenecks when each train suffers a disturbance. Finally, the results of the tests based on a rail hub linking six lines and a TAS about thirty minutes show that the minimum buffer time is 21 seconds and there are two bottlenecks where the buffer times are 57 and 44 seconds respectively, and it indicates that the bottlenecks do not certainly locate at the area where there is minimum buffer time. The proposed approach can further support selection of multi schemes and robustness optimization.

An investigation of rail condition monitoring by Fibre Bragg Grating sensors

Condition monitoring on rails and train wheels is vitally important to the railway asset management and the rail-wheel interactions provide the crucial information of the health state of both rails and wheels. Continuous and remote monitoring is always a preference for operators. With a new generation of strain sensing devices in Fibre Bragg Grating (FBG) sensors, this study explores the possibility of continuous monitoring of the health state of the rails; and investigates the required signal processing techniques and their limitations.

Signature analysis on wheel-rail interaction for rail defect detection

Fibre Bragg Grating (FBG) sensors have been installed along an existing line for the purposes of train detection and weight measurement. The results show fair accuracy and high resolution on the vertical force acted on track when the train wheels are rolling upon. While the sensors are already in place and data is available, further applications beyond train detection are explored. This study presents the analysis on the unique signatures from the data collected to characterise wheel-rail interaction for rail defect detection. Focus of this first stage of work is placed on the repeatability of signals from the same wheel-rail interactions while the rail is in healthy state. Discussions on the preliminary results and hence the feasibility of this condition monitoring application, as well as technical issues to be addressed in practice, are given.

Carbon dioxide emissions from diesel and compressed natural gas buses during acceleration

Motor vehicle emission factors are generally derived from driving tests mimicking steady state conditions or transient drive cycles. However, neither of these test conditions completely represents real world driving conditions. In particular, they fail to determine emissions generated during the accelerating phase – a condition in which urban buses spend much of their time. In this study we analyse and compare the results of time-dependant emission measurements conducted on diesel and compressed natural gas (CNG) buses during an urban driving cycle on a chassis dynamometer and we derive power-law expressions relating carbon dioxide (CO2) emission factors to the instantaneous speed while accelerating from rest. Emissions during acceleration are compared with that during steady speed operation. These results have important implications for emission modelling particularly under congested traffic conditions.

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China

The multi-criteria decision making methods, Preference METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site > urban site > roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8 ± 8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.