Social Media and Public Sphere in China

This chapter seeks to answer the question of whether a microblog can function as a promising form of public sphere. Utilizing a combined framework of public sphere based on the theories of Mouffe and Dahlgren, it examines the political discussion and interrogation on Sina Weibo, China's leading microblog site, concerning the Wenzhou high-speed train derailment accident in July of 2011 through a critical discourse analysis. Its results suggest that Weibo enables the creation of new social imaginary and genre of discourse as well as the construction of new social identities.

Train Derailment Process Analysis on Heavy Haul Railway Bridge under Ship Impact

In order to research train derailment law under ship impact, the spatial vibration calculation model of a freight train-track-bridge (FTTB) system is used to establish the vibration model of the FTTB system under ship impact. Meanwhile, the calculation method of a train derailment process under ship impact is proposed based on the random analysis method of train derailment energy. Further, the train derailment process on a bridge under ship impact is calculated, and the variation law of the FTTB system spatial vibration response under different impact loads and speeds is analyzed. The results show that the ship impact load has a great influence on wheel lift value. When the impact load is greater than 15 MN, the wheel derails more easily. With the increase of impact load, the derailment coefficient, wheel load reduction rate, and lateral relative displacement of bogie and rail, the lateral displacement of the bridge increases significantly, but the limits of them make it difficult to determine whether the wheel has derailed. The lateral relative displacement of the bogie and rail considering the safety factor is calculated at the moment of derailment, which is taken as the early warning threshold of train derailment. The above conclusions can provide a reference for controlling train safety under ship impact.

Multiscale Simulation-Based Mixed Train Derailment Analysis: A Case Study

The makeup of mixed-manifest freight trains is often determined through rigid operating rules designed based on steady-state in-train forces under nominal vehicle and track conditions. Despite compliance with these rules, excessive forces experienced during exceptional situations still hold significant potential to result in derailments. This paper presents a case study involving a mixed train derailment in a sharp curve following an undesired brake release. The operating conditions and sequence of events are modelled using a novel multiscale simulation approach to combine industry-established longitudinal and multi-body dynamics simulation packages. The root cause of the derailment in this case study is assessed, along with the effectiveness of subsequent rule changes in mitigating the corresponding risk. The case study and results are further discussed in the broader context of a research initiative to utilize near real-time data collection from wayside and vehicle-mounted measurement systems, together with robust multiscale simulation approaches, to improve the area of mixed train marshalling.

An Evaluation of Derailment Mechanics and Derailment Analysis Methodologies

Detailed analyses of vehicle and train collisions are a common part of new vehicle design projects. It is relatively simple to describe appropriate collision scenarios for a train and the resulting collision mechanics are reasonably controlled if the trains remain upright and in-line. These scenarios are well suited to advanced dynamic finite element simulation codes. Alternatively, train derailment analyses are less common and have unique characteristics that make the analyses difficult. The derailment event can involve the interaction of many cars and have a relatively long duration compared to other crash events. Freight derailments can involve trains in excess of 100 cars long and the duration of the derailment response can be on the order of a minute before coming to rest. Further complicating the analysis are the many parameters that are not well characterized or controlled. The motions of rail cars after leaving the tracks are not well known and difficult to model. The wheels and trucks can plough through ground or remaining track sections. The material properties and geometry of the ground can have large variations and are typically not well known or characterized for specific derailment events. Additionally, the geometry of the surrounding terrain can have a wide range of variability at derailment sites. As a result of these complexities, there are far fewer standardized methodologies used for the analysis of derailments. The detailed finite element models are applied in some cases, but the computational requirements to model these events in high fidelity are quite high. This paper provides a review of some past derailment modeling efforts and recent investigations and analyses of derailment events to provide insights into the derailment mechanics of freight trains. The objective is to assess the relative magnitudes of effects such as the braking characteristics, brake application delay time, and blockage force caused by the derailed and overturned cars on the subsequent deceleration of the trailing cars on the rail.

Evaluation of Interaction Between Emergency Medical System and Hospital Network During a Train Derailment in Milano

On January 25, 2018 a 5-car train derailed in Pioltello, 10 kilometers North-East of Milano City. A standardized post-hoc form was distributed to the hospitals involved in the management of the victims and allowed for an evaluation of the response to the incident. The management of the incident by EMS (Emergency Medical System) was effective in terms of organization of the scene and distribution of the patients, although the time for the first severe patient to reach the closest appropriate hospital was very long (2 hours). This can be partially explained by the extrication time. None of the alerted hospitals exceeded their capacity, as patients were distributed carefully among the hospitals. The overall outcome was quite satisfactory; no deaths were reported except for those on scene. Some responding hospitals reported that there was an over-activation based on the services ultimately needed. However this is common in MCIs, as an over-activation is preferable to an under-estimation. To address this concern, as more data are available, activation should be scaled down based on a plan established prior to it; this mechanism of scaling down seems to have failed in this event. It is of note that the highest performing hospitals underwent recently to an educational program on MCI management.

Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network

In recent years, transportation system safety analysis has become increasingly challenging and highly demanding. Unstructured data contain sufficient information from which inherent interactions can be extracted. Determining how to process and fuse a large amount of unstructured data is a challenging task. In this paper, we propose a text-based Bayesian network (TBN) method to establish a Bayesian network (BN) based on text records, where the BN’s arcs are obtained from barrier relationships identified by a graphical model and its prior probabilities stem from fault trees. The comparative experimental results illustrate that the text-based method in TBN is efficient. The precision, recall and F-measure of TBN are 8.64%, 10.70% and 9.84% higher, respectively, than the most frequent (MF) result. Moreover, compared to the traditional BN, whose prior probabilities are frequently acquired from experts, the prior probabilities of the proposed text-based BN (TBN) have a high confidence. The experimental results of a train derailment accident case study show that with changes in the train derailment probabilities and the safety potentials of the barriers, the TBN generates quantitative results and reveals the critical risks of derailment accidents. Additionally, this work demonstrates relevant nonlinear relationships to improve the assessment results. Therefore, based on text-based data, this study reveals that barrier safety analysis has the potential to identify high-risk barriers, which can guide managers to enhance these barriers.