French National Railways Research Articles

Material Flow Analysis: An Analytical Tool for Strategic Planning Towards a Zero-Waste Solution for End-of-Life Ballast Flows on a Track and Ballast Renewal Site (French Conventional Line)

The 27,000 km of railway track in France represents approximately 100 million tonnes of ballast. This ballast requires maintenance approximately every 7 years, screening and partial renewal every 20 years, and complete replacement every 40 years. Despite its shortcomings, ballast is still widely used on railways worldwide, as there is no better or more efficient solution currently available. In an effort to conserve resources, companies such as SNCF (French national railway company) are implementing initiatives to move towards zero waste. In order to achieve the goal of promoting the recycling and recovery of end-of-life material flows, it is necessary to develop specific studies and models for production, such as direct and reverse logistics systems. This article proposes a generic material flow analysis model applied to a track and ballast renewal site, aiming to fill a gap in the literature. It is based on data from eleven track and ballast renewal sites. A flow diagram generated by STAN software presents a detailed diagnosis of ballast inputs, outputs, and stocks, including data uncertainty. The distribution of the material flows through the model is characterized by transfer coefficients in various transformation processes. Furthermore, by varying the quantities in this model, it is possible to study different scenarios based on the current situation. This will facilitate the projection and analysis of future management strategies aimed at achieving zero waste and reducing the discharge of toxic substances based on specific performance indicators.

A Case Study of Negated Adjectives in Commuters’ Twitter Complaints

In today’s digital society, social networks such as Twitter are a preferred place for expressing one’s emotions, especially when they are negative. Despite a growing interest in the variety of linguistic realizations of commuters’ complaints, little attention has so far been paid to writers’ choices, especially when morphologically or syntactically simpler alternative formulations are available. A typical example is the “inference towards the antonym” triggered by the negation of contrary adjectives, an effect that is stronger for positive compared to negative adjectives. In the context of railway transport, a customer could use the negative statement The train is not clean instead of the corresponding affirmative sentence The train is dirty. It remains unclear, in our current state of knowledge, why online customers would prefer more complex constructions to voice their criticisms. Based on a large corpus of tweets sent to the French and Belgian national railway companies by their customers, I have semi-automatically extracted instances of not (very) + adjective (ADJ). Based on previous observations in the literature, I expected positive adjectives to be more frequently used in these negative environments compared to negative ones. As recent research demonstrates that one’s desire to save the interlocutor’s face is not necessarily the only reason why positive adjectives are used in linguistically negative environments, other motivations will also be considered. More precisely, I suggest that in a context where negativity is prevalent, customers using negated positive adjectives kill two birds with one stone: not only do they signal an issue with a product or a service, pointing to expectations that have not been met by the company, but they also mitigate the impact of their negative comments to the positive face of the service managers with whom they are interacting. By offering a quantitative, corpus-based analysis of negative constructions, complemented by a qualitative linguistic analysis of selected examples, this research sheds new light on users’ lexical choices in online negative customer feedback.

French Metallic Train Sheds of 1850 to 1930: Structural Specificities and the Evolution of the Restoration Practices

ABSTRACT Surviving historic train sheds constitute a broad and representative sample of metal construction of the period 1850 to 1930 in France. This article gives a historical overview of the life of iron or steel train shed structures and questions what makes them authentic, discussing the validity of some of the restoration choices made since the 20th century regarding heritage preservation. First, the specificities of this architectural heritage are highlighted and compared with other countries and other types of construction. Then, based on extensive archive material held by the French national railway company SNCF, the modifications the metallic structures underwent as restoration projects were carried out are analysed. It appears that the restoration practices significantly evolved between the 1970s-1980s and the beginning of the 21st century, becoming more sensitive to heritage preservation. However, choices regarding roofing materials have an impact on the loading of the structure, while structural interventions change the appearance and the load distribution. This enhances the need to include considerations regarding the preservation of the structure in the early stages of restoration projects.

An interpretable model for bridge scour risk assessment using explainable artificial intelligence and engineers’ expertise

A machine learning (ML) model would be difficult to apply in practice without knowing how the prediction is made. To address this issue, this paper uses XAI and engineers’ expertise to interpret an ML model for bridge scour risk prediction. By using data from the French National Railway Company (SNCF), an extreme gradient boosting (XGBoost) algorithm-based ML model was constructed at first. Later, XAI approaches were employed to have global and local explanations as well as explicit expressions for ML model interpretation. Meanwhile, a group of engineers from SNCF were asked to rank the input parameters based on their engineering judgment. In the end, feature importance obtained from XAI approaches and engineers’ survey was compared. It was found that for both XAI and engineers’ interpretations, observation of local scour around the bridge foundation is the most important feature for decision-making. The differences between XAI interpretations and human expertise emphasize the importance of knowledge in hydrology and hydromorphology for scour risk assessment, since currently engineers make decisions primarily based on observed damages (e.g., scour hole, crack). The results of this paper could make the ML model trustworthy by understanding how the prediction is made and provide valuable guidance for improving current inspection procedures.

Machine learning (ML) based predictive maintenance policy for bridges crossing waterways

Scour is one of the major causes for bridge failures. This study established a machine learning based predictive maintenance policy for scour risk mitigation. Data used in this study was provided the French National Railway Company (SNCF). After comparing three commonly used ML algorithms, it is found that the extreme gradient boosting (XGBoost) algorithm outperforms others and achieves very encouraging results. Moreover, the trained classifier was then tested in another 40 bridge piers and the results were compared with a junior engineer's evaluation. Results show that XGBoost classifier had a slightly better performance in terms of accuracy and precision. The proposed methodology in this paper allows engineers to determine scour risk of bridge piers in an accurate yet rapid way.

A novel extreme gradient boosting algorithm based model for predicting the scour risk around bridge piers: application to French railway bridges

Bridge failures inevitably cause severe social consequences in terms of the economic losses or even human fatalities. Scour is one of the leading causes for bridge failures. Due to its complexity and multidisciplinary nature, the mechanism of scour has not been completely understood yet. Inspired by the rapid development of Machine Learning (ML) techniques, this paper aims to construct a novel data-driven extreme gradient boosting (XGBoost) algorithm based model to predict the scour risk around bridge piers. Data used in the present study is provided by the French National Railway Company (SNCF). The performance of XGBoost-based model is compared with three commonly used algorithms: support vector machine, random forest and multilayer perceptron. Results show that the XGBoost classifier achieves high accuracy (0.959/0.938), precision (0.970/0.961), recall (0.974/0.956) and low false positive rate (0.085/0.114) for training and test set respectively. Moreover, the classifier obtains an area under the ROC curve (AUC) score equal to 0.974 for test set (a perfect classifier has an AUC equal to 1). This paper presents a cutting-edge application of XGBoost algorithm in the maintenance of railway bridges. The proposed methodology may allow engineers to determine the scour risk of bridge piers in an accurate and rapid way.

Book Review: Last Train to Auschwitz The French National Railways and the Journey to Accountability

The book Last Train to Auschwitz: The French National Railways and the Journey to Accountability, written by Sarah Federman traces the SNCF’s journey toward accountability in France and the United States. Told from the Holocaust survivors’ perspective the volume illustrates the long-term effects of the railroad’s complicity with the Nazis on individuals, and transitional justice that leads to corporate accountability. In a time when corporations are increasingly granted the same rights as people, Federman’s detailed account demonstrates the obligations businesses to atone for aiding and abetting governments in committing atrocities.

ОБҐРУНТУВАННЯ НЕОБХІДНОСТІ КАПІТАЛІЗАЦІЇ ЗАЛІЗНИЧНОГО ТРАНСПОРТУ

The article examines the need for capitalization of railway transport and notes that a stable railway transport is a guarantee of development of all sectors of the economy, and its effectiveness is determined by the dynamics of recovery of fixed assets, growth of demand for rail transport, structural reform, efficiency of tariff policy, state-regulated natural monopoly sectors, the intensity of use of international transport corridors. The contribution of railway transport to the economic development of the country is determined, which is manifested in the effect of the network, improvement of results, reliability, market size and productivity. The main factors that prompted railway transport to capitalize are identified, which include: restructuring (involves market introduction and competition in areas that are not a natural monopoly), market liberalization and the creation of joint ventures or public-private partnerships. These factors were considered on the example of railway transport in European countries, in particular, the restructuring was considered on the basis of the experience of Polish Railways, market liberalization - Lithuanian Railways, and public-private partnership - French National Railways. Consideration of the capitalization of railway transport took into account its attribution to the critical infrastructure and the commitment to perform socially important tasks. The analysis allowed to identify a number of advantages (relative efficiency, the possibility of using different types of packaging and forms of transportation, flexibility of delivery, independence from climatic conditions and seasonality) and disadvantages (large infrastructure, increased costs in case of increasing transportation distance, transshipment in case of intercontinental transportation etc.). It is substantiated that the formation of a systematic approach to the capitalization of railway transport will increase the efficiency of equity use, maximum satisfaction of demand in the market of transport services, the formation of economically sound management structure and maximize the efficiency of financial investments.

Ridesharing to the Rescue: Usage and Welfare Impact of BlaBlaCar during the 2018 French Railway Strike

This work estimates the impact of the 2018 French national railway strike (April to June) on the inter-city ridesharing usage and user welfare. We collected daily trip-level data from the API of BlaBlaCar, the largest inter-city ridesharing platform in Europe, and show that on an average strike day, demand increased by 29% while supply increased by 7% during the strike period. We conduct a calibration exercise to estimate the consumer surplus of each route per day, and then use propensity score matching to extend our estimates to additional routes to measure the overall impact in France. On an average non-strike day, BlaBlaCar generated roughly 145,000 € of consumer surplus, while on an average strike day, it generated 179,000 €, an increase of 34,000 €. Our result shows that inter-city ridesharing contributes to the social welfare, especially during a major disruption in the transport system, and serves as a flexible substitute for the railway service. We suggest that policy-makers integrate ridesharing into the design and management of the transportation network.

Linguistic (in)directness in twitter complaints: A contrastive analysis of railway complaint interactions

In this paper we describe and apply a method probing into linguistic (in)directness in complaint tweets. The sample analyzed consists of French-language Twitter complaint interactions, half of which are between the SNCF (the French National Railway company) and its customers and half between the SNCB (the Belgian National Railway company) and its customers. The aim of this paper is two-fold: first, we present and test a methodology that measures the explicitness (or linguistic (in)directness) of complaints, which, we argue, in line with Decock and Depraetere (2018), must be differentiated from perceived (im)politeness or perceived face-threat. Linguistic (in)directness is analyzed in the complete complaint situation, that is, in the first tweet as well as in the ensuing interaction. Secondly, as the sample shows differences in the realization of complaints in two linguistically close communities, we put forward some hypotheses concerning the relative impact of cultural differences and the companies’ response strategy.

Corporate Leadership and Mass Atrocity

With the last Holocaust survivors quietly passing away, one might also expect to see accountability debates slowing to a trickle. Surprisingly, however, recent years show an upswing in corporate World War II-related atonement debates. Interest in corporate participation in mass atrocity has expanded worldwide; yet what constitutes ethical corporate behavior during and after war remains understudied. This article considers these questions through a study of the French National Railways’ (SNCF) roles during the German occupation and its more recent struggle to make amends. This study demonstrates that ethical business leadership requires taking responsibility for past as well as current decisions. Most executives grappling with complex corporate histories work in isolation, in part because the scholarship on business ethics fails to provide guidance. Without such guidance, corporations often respond to accusations about their pasts with carefully crafted statements and legal strategies rather than deep expressions of moral leadership. To assist in remedying this tendency, this paper simultaneously encourages companies to engage in deeper reflection on corporate history, while urging scholars to help guide corporations through critical ethical conversations.

Spatial patterns of the French rail strikes from social networks using weighted <i>k</i>-nearest neighbour

The information analysis provided by millions of social network users is one of the most important sources of information yielding interesting insights of spatial patterns about socio-political events. During the recent French National Railway strikes (from April to June, 2018), Twitter was used as platform where people expressed their opinions, with millions of French National Railway Company (SNCF) tweets posted over the strike period. In this paper, we have discussed a methodology which allows the utilisation and interpretation of Twitter data to determine spatial patterns over French territory. The identification of a geographic strike landscape is achieved through spatial interpolation using weighted k-nearest neighbour. This study shows the benefits of geo-statistical learning for extracting sentiment polarities of social events across France.

Spatial patterns of the French rail strikes from social networks using weighted <i>k</i>-nearest neighbour

The information analysis provided by millions of social network users is one of the most important sources of information yielding interesting insights of spatial patterns about socio-political events. During the recent French National Railway strikes (from April to June, 2018), Twitter was used as platform where people expressed their opinions, with millions of French National Railway Company (SNCF) tweets posted over the strike period. In this paper, we have discussed a methodology which allows the utilisation and interpretation of Twitter data to determine spatial patterns over French territory. The identification of a geographic strike landscape is achieved through spatial interpolation using weighted k-nearest neighbour. This study shows the benefits of geo-statistical learning for extracting sentiment polarities of social events across France.

The ‘ideal perpetrator’: The French National Railways and the social construction of accountability

Mass atrocity requires the participation of numerous individuals and groups, yet only a few find themselves held accountable. How are these few selected? This article offers a framework that is useful for understanding how the condemned often embody attributes that keep them in the spotlight. Because norms used to identify perpetrators can set the context for future violence, long-term security requires interrupting both the actions of perpetrators and the discourses about them. A form of praxis, this study of the contemporary conflict over the French National Railways’ (SNCF) amends-making for its World War II transport of deportees towards death camps considers how certain perpetrators come to stand for the many. The SNCF remains in the spotlight not because of greater culpability or an unwillingness to make amends but because it embodies attributes of an ‘ideal’ perpetrator: it is (1) strong, (2) abstractable, (3) representative of the nature of the crime, and (4) has a champion-opponent who focuses attention on the perpetrator. Understanding the labeling process makes visible who and what we ignore at our own peril.

Predictors of elevated capillary blood glucose in overweight railway French employees: a cross-sectional analysis

BackgroundHyperglycaemia is a risk factor of cardiovascular disease and a high risk state for progression to type 2 diabetes. Moreover, overweight, defined as a body mass index (BMI) between 25 and 29.9 kg/m2, increases the risk of diabetes. Information about the feasibility of measuring, during routine occupational health examinations, predictors of elevated capillary blood glucose in overweight individuals is scarce. This study aims to identify factors that are associated with elevated capillary blood glucose and can be routinely measured in French overweight employees to develop targeted preventive strategies in the workplace.MethodsCross-sectional study based on data collected during a workplace health promotion programme of the French National Railways Company (SNCF) from January 2011 to March 2015. A self-administered questionnaire was completed by overweight volunteers during the routine occupational health examination. Data collected included health, anthropometric, sociodemographic, occupational, and lifestyle characteristics. Elevated capillary blood glucose was defined as capillary blood glucose equal to or higher than 7 mmol/L. Multivariate logistic regression analysis was used to examine factors associated with elevated capillary blood glucose and results were described with odds ratios (OR) and 95% confidence intervals (CI).ResultsThe analysis concerned 2248 overweight employees (mean age: 43 years) with complete data (total population: 7724). The prevalence of elevated capillary blood glucose was 20.0%. In the multivariate analysis, significant predictors of elevated capillary blood glucose were: male sex (OR 1.66, 95% CI 1.21–2.28), age ≥ 50 years (OR 1.61, 95% CI 1.01–2.55), high blood pressure (OR 1.35, 95% CI 1.07–1.69), and daily intake of sugary food (OR 1.53, 95% CI 1.17–2.00). No association with occupational characteristics (work schedule, job seniority, professional grade, and job sector) was found possibly due to lack of statistical power.ConclusionsOur findings provide information for setting up specific diabetes prevention strategies in the workplace. Overweight men, aged 50 and older, with high blood pressure and daily sugary food intake should be considered for capillary blood glucose measurements during their occupational medical surveillance. Hypertension screening and management as well as health policy measures to target sugary food consumption could be included in workplace prevention strategies.

Track circuit reliability assessment for preventing railway accidents

The safe operation of a railway signaling system depends on accurate and up-to-date information on the position and movements of trains provided by train detection devices. Track circuits are the most popular systems used over the world to provide information on position and movements of trains and ensure safety of circulations. Track circuits’ failure or malfunction have two consequences: operating unreliability causing train delays, and accidents. In this paper, we focus on potential accidents.In close cooperation with experts and practitioners of the French national railways company SNCF, we have designed a reliability model of track circuits taking into account local conditions: environment, vehicles, track, traffic, track circuit technology and use. Combined with an assessment of potential accidents’ severity due to TC failure, this model aims at supporting managers’ strategies to reduce the risks and optimize resources allocation.This model is also used to predict potential locations of track circuits that may generate malfunctions, using a measure of similarity with track circuits’ locations where malfunctions occurred. Using the database of past malfunctions, the similarity measure uses the model’s parameters to set up and update a classification of malfunction types. Each type is compared with the database of track circuits to identify those that are similar enough to raise attention and check if appropriate prevention measures are already in place or need to be added.The model is used as a support tool by the local staff to optimize the resources needed to ensure safety by setting priorities among track circuits and determining which ones need prevention and/or protection measures.The model was designed and implemented in a French region where track circuits malfunctions have a higher probability to occur due to climatic conditions in autumn that pollute the wheel-rail contact.

Genocide Studies and Corporate Social Responsibility: The Contemporary Case of the French National Railways (SNCF)

Genocide studies considers the accountability various of perpetrators, as well as the needs mass atrocity creates. The inclusion of market actors, however, remains marginalized. This article considers factors perpetuating this marginalization and its costs, arguing for greater inclusion of market actors in genocide-related discussions. Relegating the importance of these actors makes the field, not their role, tangential. To examine this intersection of business and genocide, this article introduces a contemporary conflict involving the United States and France over the French National Railways (SNCF) and its role in the transport of deportees towards death camps during World War II. The lengthy, vitriolic conflict suggests the need for better discursive spaces to explore the complex role of market actors. Developing the intersection of genocide studies and corporate social responsibility in scholarship and increasing the participation of businesses in post-conflict work can help do this work.

A risk assessment study on accidents at French level crossings using Bayesian belief networks

ABSTRACTLevel crossing (LX) safety continues to be one of the most critical issues for railways despite an ever increasing focus on improving design and practices. In the present paper, a framework of probabilistic risk assessment and improvement decision based on Bayesian belief networks (PRAID-BBN) is proposed. The developed framework aims to analyse various impacting factors which may cause LX accidents, and quantify the contribution of these factors so as to identify the crucial factors which contribute most to the LX accidents. A detailed statistical analysis is first carried out based on the accident/incident data. A BBN risk model is established according to the statistical results. Then, we apply the PRAID-BBN framework on the basis of the accident/incident data provided by SNCF, the French national railway operator. The main outputs of our study are conducive to efficiently focusing on the effort/budget to make LXs safer.

Genocide Studies and Corporate Social Responsibility: The Contemporary Case of the French National Railways (SNCF)

Genocide studies considers the accountability various of perpetrators, as well as the needs mass atrocity creates. The inclusion of market actors, however, remains marginalized. This article considers factors perpetuating this marginalization and its costs, arguing for greater inclusion of market actors in genocide-related discussions. Relegating the importance of these actors makes the field, not their role, tangential. To examine this intersection of business and genocide, this article introduces a contemporary conflict involving the United States and France over the French National Railways (SNCF) and its role in the transport of deportees towards death camps during World War II. The lengthy, vitriolic conflict suggests the need for better discursive spaces to explore the complex role of market actors. Developing the intersection of genocide studies and corporate social responsibility in scholarship and increasing the participation of businesses in post-conflict work can help do this work.

Risk analysis on level crossings using a causal Bayesian network based approach

Abstract Safety is a core issue in railway operation. In particular, Level Crossing (LX) safety is one of the most critical issues that railway stakeholders need to deal with. Accidents at European LXs account for about one-third of the entire railway accidents. They result in more than 300 deaths every year in Europe. However, due to non-deterministic causes, complex operation background and the lack of thorough statistical analysis based on detailed accident/incident data, the risk assessment of LXs remains a challenging task. In this paper, a general approach of Causal Statistic Risk Assessment based on hierarchical Causal Bayesian Networks (CSRA-CBN) is developed to analyze the various impacting factors which may cause accidents, and identify the factors which contribute most to the accidents at LXs, thus allowing for risk quantification. The detailed statistical analysis is carried out firstly according to the accident/incident database, then the CBN risk model is established based on the statistical results. In order to validate the effectiveness of this approach, we apply the CSRA-CBN approach on the basis of the accident data from SNCF, the French national railway operator. The CBN model allows for quantifying the causal relationships between the risk of accident occurring and the impacting factors considered. Moreover, the hierarchical structured modeling offers interesting benefits in terms of clarity, which makes it possible to highlight the complex factors influenced by a mass of parameters and identify the factors that contribute most to LX accidents. In addition, the main output of such a modeling system is conducive to improving safety at LXs, meanwhile, allowing for efficiently focusing on the effort/budget to make LXs safer.