1,796 research outputs found

    Advanced model-based risk reasoning on automatic railway level crossings

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    Safety is a core issue in the railway operation. In particular, as witnessed by accident/incident statistics, railway level crossing (LX) safety is one of the most critical points in railways. In the present paper, a Bayesian network (BN) based framework for causal reasoning related to risk analysis is proposed. It consists of a set of integrated stages, namely risk scenario definition, real field data collection and processing, BN model establishment and model performance validation. In particular, causal structural constraints are introduced to the framework forthe purpose of combining empirical knowledge with automatic learning approaches, thus to identify effective causalities and avoid inappropriate structural connections. Then, the proposed framework is applied to risk analysis of LX accidents in France. In details, the BN risk model is established on the basis of real field data and the model performance is validated. Moreover, forward and reverse inferences based on the BN risk model are performed to predict LX accident occurrence and quantify the contribution degree of various impacting factors respectively, so as to identify the riskiest factors. Besides, influence strength and sensitivity analyses are further carried out to scrutinize the influence strength of various causal factors on the LX accident occurrence likelihood and determine which factors the LX accident occurrence is most sensitive to. The main outputs of our study attest that the proposed framework is sound and effective in terms of risk reasoning analysis and offers significant insights on exploring practical recommendations to prevent LX accidents

    Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

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    With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for the collection of the public opinions upon the influencing factors that an infrastructure project may have. Then, this model implements the backpropagation Artificial Neural Network (BP-ANN) algorithm to simulate traffic flows and generate outputs as partial quantitative references for evaluation. The traffic simulation outputs used as partial inputs to the Analytic Hierarchy Process (AHP) based Fuzzy logic module of the system for the determination of the minimum traffic flows that a construction scheme in corresponding feasibility study should meet. This study bases on a real scenario of constructing a railway-crossing facility in a college town. The research results indicated that BP-ANN was well applied to simulate 15-minute small-scale pedestrian and vehicle flow with minimum overall logarithmic mean squared errors (Log-MSE) of 3.80 and 5.09, respectively. Also, AHP-based Fuzzy evaluation significantly decreased the evaluation subjectivity of selecting construction schemes by 62.5%. It concluded that the FNCEM model has strong potentials of enriching the methodology of conducting a feasibility study of the infrastructure project

    Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

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    Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems

    Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

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    The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation

    A review of key planning and scheduling in the rail industry in Europe and UK

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    Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.EPSR

    Text Mining Applied to Rail Accidents

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    Indian Railway is the most popular way of transportation. So rail safety represents an important safety concern for transportation industry. In 6 years period between 2009-2010 and 2014-2015, there were a total of 803 accidents in Indian Railway killing 620 people and injuring 1855 people. 47% of these accidents were due to derailment of trains. To better understand the reason to these extreme accidents, Indian Railway has submitted reports that contain both fixed field and narratives that describe the characteristics of the accident. While a number of studies have looked at the fixed fields, none have done an extensive analysis of the narratives. It describes the use of text mining with a combination of techniques to automatically discover accident characteristics that can inform a better understanding of the contributors to the accidents. It will let the user know about of type of accident which can occur. The analysis can also be used for the safety improvement of Indian Railway

    Review Based Study On Risk Management Model For High Speed Indian Railway System

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    In this paper, a framework for risk management at railways has been studied and integrated into global safety management system of railways. Furthermore we studied how it was applied to a manually controlled full barrier road rail level crossing in Morocco. We studied different aspects that should be considered during the system definition phase where we suggested using functional diagrams for modeling operations at LC from the perspective of LC actors. It is a critical part for risk management and specifically for hazard identification where we provided different techniques that can be used; our experience shows that involvement of all stakeholders is a prerequisite to the success to this phase. Initiating events can be unveiled through brainstorming sessions and FTA can model complex interactions of events that have the potential to lead to accidents. Risk analysis can then be carried out provided that historical LC accident and incident data is available to estimate frequencies and consequences; ETA is the ideal tool for estimating consequences of hazards due to multiple causes. The existing risks are then classified and decisions are made regarding their tolerability, the ALARP principle can serve this purpose. A cost benefit analysis then helps prioritize risk treatment actions that should target intolerable risks. Control mechanisms should be also put in place to assess, monitor and review the risk control actions put in place. Finally, it is emphasized on the importance of having a database of historical accidents and incidents at LC for the success and efficiency for the suggested framework. Accidents at level crossings are the result of complex interactions between factors arising from the design and operations of level crossings. An important first step towards eliminating the causes of these accidents is thru understanding and assessing the risks associated with a given level crossing and acting on them. This paper presents review based study on risk management framework that serves this purpose

    Analyzing Severity of Vehicle Crashes at Highway-Rail Grade Crossings: Multinomial Logit Modeling

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    The purpose of this paper is to develop a nominal response multinomial logit model (MNLM) to identify factors that are important in making an injury severity difference and to explore the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings (HRGCs) in the United States. Vehicle-rail and pedestrian-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. A multinomial logit model is developed using SAS PROC LOGISTICS procedure and marginal effects are also calculated. The MNLM results indicate that when rail equipment with high speed struck a vehicle, the chance of a fatality resulting increased. The study also reveals that vehicle pick-up trucks, concrete, and rubber surfaces were more likely to be involved in more severe crashes. On the other hand, truck-trailer vehicles in snow and foggy weather conditions, development area types (residential, commercial, industrial, and institutional), and higher daily traffic volumes were more likely to be involved in less severe crashes. Educating and equipping drivers with good driving habits and short-term law enforcement actions, can potentially minimize the chance of severe vehicle crashes at HRGCs

    A Comprehensive Decision Support Framework in the Front-End Phase of Major Transportation Projects

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    Identifying the best project alternative is a critical challenge facing major transportation projects (MTPs) at the front-end phase. The increasing complexity and dynamism of MTPs have imposed substantial uncertainties and subjectivities in the decision-making process. Despite the efforts made in previous studies, a stochastic framework to facilitate the comprehensive assessment is still missing. In this research, a stochastic decision support framework has been developed to cope with the considerable uncertainties in MTPs. The features of the proposed decision support framework are achieved by using the Bayesian belief network modeling technique to provide a comprehensive registry of the relevant decision factors, establish the interrelationships between these decision factors, and consequently quantify uncertainties of decision indicators. The calculated probabilities for decision indicators have been interpreted to a satisfaction level of stakeholders based on their constraints as a multi-criteria decision model. A Monte Carlo simulation has been conducted to simulate a real condition using the decision indicators probability as input. Finally, MTP alternatives prioritized according to the anticipated satisfactory gained among various stakeholders. The created framework is used in a preliminary alternative assessment for case study related to Detroit River International Crossing project. The case study investigates the decision-making of key stakeholders related to prioritization of alternative projects for a new access between Detroit, US and Winsdor, Canada. The project team verified applicability of the model. The developed framework and the case study highlight the significance of identification of a stochastic project alternative assessment method. The proposed framework provides decision-makers with a decision support tool to facilitate front-end phase of MTPs
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