ABSTRACT
Increasing the level of containerization in Russia is a source of competitiveness growth, since it focuses on increasing the efficiency of nationally oriented logistics and integration into global logistics chains, ensures transparency and productivity of the transport and logistics sector. The negative consequences caused by COVID-19 have affected freight transportation by all modes of transport. The reaction of carriers to the existing challenges largely determines the trajectory of development not only of railway container transportation, but also the format of the development of Russian cargo traffic for the coming years or even decades. The purpose of this study is to develop measures to increase the mobility of container cargo turnover on Russian railways and indicative designation of possible effects from their implementation. We prove that the development of infrastructure, stabilization of tariff policy, optimization of operational activities, expansion of the digital platform significantly affects the qualitative and quantitative development of rail container transportation based on mobility and logistics efficiency. The methodology of the study is based on the analysis of the target indicators of containerization of Russian railways for 2010–2020, the main product line of container transportation and competitors of the railway by means of transport. As a result of the study, trends in the growth of the share of container cargo turn-over on the Russian railway network are determined, the support of digital tools is justified in order to increase the mobility of container cargo turnover, a set of mechanisms for supporting and developing railway container transportation based on mobility and logistics efficiency is proposed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Predictive analytics is an increasingly popular tool for enhancing decision-making processes but is in many business settings based on rule-based models. These rule-based models reach their limits in complex settings. This study compares the performance of a rule-based system with a customised LSTM encoder-decoder deep learning model for predicting train delays. For this, we use a purposefully built real-world dataset on railway transportation, where trains' interdependence over the network makes delay prediction more difficult. Results show that the deep learning model, which incorporates rich spatiotemporal interdependency information in real-time, outperforms the rule-based system by 18%, with the difference increasing to above 23% with higher complexity. The study also dissects the performance difference across different settings: dense versus rural areas, peak versus off-peak hours, low versus high delay, and before versus during the COVID-19 pandemic. The deep learning model is implemented as a proof of concept for decision support within Belgium's railway infrastructure company Infrabel. © 2023 Elsevier B.V.
ABSTRACT
In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.
ABSTRACT
U.S. rail transit (subways, metros, and light rail) and Federal Railroad Administration (FRA) regulated heavy rail (commu-ter, intercity and regional rail) operate completely separately in revenue service. This necessitates transfers between the modes at terminals. While not unique to the U.S.A., its version of this practice is extreme and prevents the development of robust seamless rail networks. Especially in the post-Covid environment, this leaves commuter rail in search of a mission and rail transit isolated from suburbs. This paper discusses the statutory regulatory scheme that divides the two modes in the U.S.A. It will analyze the justification for the segregation and its history. Such issues include potential collisions, weight, crashworthiness, electrification, signaling, loading gauge, platform height, and operating practices. This paper concludes that the regulatory barrier preventing an FRA-regulated train from going onto a non-FRA railroad are surmountable. Running through trains between the FRA-regulated system and the rail transit network would enhance regional networks. The ‘‘Karlsruhe model'' in Germany and the through running of regional trains onto the Tokyo subway network are two prime examples. Recent technological advances—such as dual mode battery multiple units, robust signaling systems such as Communications Based Train Control and Positive Train Control, and advanced car body designs able to deal with different loading gauges—make through running more practical. With little or no new right-of-way, it is possible to create far more useful rail networks. Potential shared networks at the conceptual level are discussed for Los Angeles, Seattle, Washington, D.C., Dallas, and Sacramento. © National Academy of Sciences: Transportation Research Board 2022.
ABSTRACT
Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with coronavirus disease (COVID-19) limitations. This article presents a taxonomy and review of sensing technologies based on the Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICTs) in order to: 1) monitor and predict crowding events;2) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs);and 3) inform in real time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning. © 2001-2012 IEEE.
ABSTRACT
Nowadays, all kinds of service-based organizations open online feedback possibilities for customers to share their opinion. Swiss National Railways (SBB) uses Facebook to collect commuters' feedback and opinions. These customer feedbacks are highly valuable to make public transportation option more robust and gain trust of the customer. The objective of this study was to find interesting association rules about SBB's commuters pain points. We extracted the publicly available FB visitor comments and applied manual text mining by building categories and subcategories on the extracted data. We then applied Apriori algorithm and built multiple frequent item sets satisfying the minsup criteria. Interesting association rules were found. These rules have shown that late trains during rush hours, deleted but not replaced connections on the timetable due to SBB's timetable optimization, inflexibility of fines due to unsuccessful ticket purchase, led to highly customer discontent. Additionally, a considerable amount of dissatisfaction was related to the policy of SBB during the initial lockdown of the Covid-19 pandemic. Commuters were often complaining about lack of efficient and effective measurements from SBB when other passengers were not following Covid-19 rules like public distancing and were not wearing protective masks. Such rules are extremely useful for SBB to better adjust its service and to be better prepared by future pandemics. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
2020 and 2021 were years dominated by COVID-19. Fear of an unknown pathogen, circulating conspiracy theories, introduced administrative restrictions and their loosening significantly influenced social life and all economic sectors. Large changes were also observed in transport, manifested by a decrease in the transport volume and lower revenues. The article presents the changes on Polish transport market and shows the correlation between their level and the introduced sanitary restrictions. The analysis of the available data revealed: fragility of the transport system and its lack of resistance to unexpected events;relatively quick adaptation/overcoming of difficulties on the railways, especially in the transport of goods;changes in social mobility;differences in individual countries, and draw the conclusions about: the possibility of quick adaptation of rail transport in crisis situations;the existing transport potential in the railway sector;the need to adopt solutions that make transport immune to unexpected events. The article uses data obtained as a result of a review of world literature, studies of many institutions and organizations as well as obtained from the Office of Rail Transport and individual carriers. © 2022 Kaunas University of Technology. All rights reserved.
ABSTRACT
West Japan Railway Company (hereinafter referred to as "JR West") introduced the IC card ticket system "JCOCA" and has expanded the use area of the IC card. For the local lines, the onboard type of ticket gate machine for the IC card ticket is introduced to allow to use the IC card. This type of ticket gate machine is also effective for reducing the burden on the train crew. The introduction of this on-board type of IC ticket gate is our effort to allow passengers to smoothly get on/off and to provide safe and secure transport service under the COVID-19 epidemic. © 2022 Japan Railway Engineers' Association. All rights reserved.
ABSTRACT
Ukrainian railways rank fourth on the Eurasian continent in terms of freight traffic. The initial stage of liberalization characterizes the railway transportation market in Ukraine, and the primary logistics operator in the market - JSC "Ukrzaliznytsia" - provides 82% of freight traffic. Considering the consequences of the COVID-19 pandemic, the railway industry has been actively working to increase market competitiveness. However, on February 24, 2022, Russia invaded Ukraine, dramatically changing the lives of millions of Ukrainians and the world. The authors study and determine the level of impact of the Russian invasion on railway freight flows in Ukraine. This work aims to identify the consequences of hostilities in the context of rail freight flows in order to quickly increase the competitiveness of Ukraine in the international space of rail freight. © 2022 Kaunas University of Technology. All rights reserved.
ABSTRACT
The Railway industry is facing a productivity issue as is often publicised with regular delays in rolling stock projects [1]. Plus, there is a growing need for innovation in remote services and management that have become the new normal during the COVID-19 pandemic. It drives a need for better Systems Engineering (SE) methods which include increased automation and dependence between systems and system performance, increasing number of disparate specialist engineering teams. [2] The aim of this paper is to develop an adaptable model which expresses the operational behavior of a train system in different railway environments, this model will be quickly and accurately configured to a specific environment to define the needs for a specific passenger service mission. Preventing late changes (cost and time-saving) by generating the right system requirements at the very early design phase through agile Model-Based Systems Engineering (MBSE) approach is the key benefit. Another goal includes increased productivity by minimizing unnecessary manual transcription of concepts when coordinating the work of large teams. This Generic* functional model of a Rolling Stock system can be configured to define specific products for an operator or Original Equipment Manufacturer (OEM). Copyright © 2022 by ASME
ABSTRACT
In many of the world's major cities, commuter trains provide vital transportation support and thus play an essential role in our daily lives. Therefore, it has become necessary to estimate the degree of congestion in each train car, both to improve passenger comfort levels and, more recently, to prevent worsening the COVID-19 pandemic infection rate. However, it is difficult to estimate the degree of congestion within a train without violating passenger privacy. The same issues are true for busses, which is noteworthy because we have previously developed and evaluated a system that can estimate the degree of congestion within a bus while protecting passenger privacy by using Bluetooth Low Energy (BLE) signals. In this paper, we report on our efforts to extend that system to railway use, which were conducted on actual trains in cooperation with Kintetsu Railway Co., Ltd. During this trial, we collected BLE signals and used the data to estimate congestion levels in each car using an ML regression model. The results show that the mean absolute error (MAE) and the mean absolute percentage error (MAPE) could be estimated at accuracy levels of 5.56 and 0.27, respectively. © 2022 IEEE.
ABSTRACT
Owing to the outbreak of COVID-19, researchers are exploring methods to prevent contact and non-contact infections that occur via multiple transmission routes. However, studies on pre-venting infections caused by droplet transmission in public transportation are insufficient. To prevent the spread of infectious diseases, a new ventilation system in railway vehicles must be devel-oped. In this study, a novel vertical drop airflow (VDA) system is proposed to mitigate the effect of droplet transmission in a high-speed train cabin. The droplet transmission route and droplet fate are investigated using three-dimensional fluid dynamics simulations, performed employing the Eu-lerian–Lagrangian model. Additionally, a porous model is adopted to simulate the effect of close-fitting masks. The results indicate that 120 s after coughing, the decrease in the droplet number in the VDA system is 72.1% of that observed in the conventional system. Moreover, the VDA system effectively suppresses droplet transmission because the maximum droplet travel distances of the VDA systems are 49.9% to 67.0% of those of the conventional systems. Furthermore, the effect of reducing droplet transmission by wearing a close-fitting mask is confirmed in all systems. Thus, the decrease in both droplet number and droplet transmission area in train cabins validate that the proposed VDA system has an effective airflow design to prevent droplet infection. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
The COVID-19 has led to people’s increased concern about health issues. In this paper, we investigate the needs of users traveling by high-speed rail in the post-pandemic era and optimize the design of high-speed rail seats, and evaluate the feasibility. Methodology: Using INPD combined with AHP and QFD to guide the design of high-speed railway seats, we use INPD as the main line of research and SET factor analysis to find the product opportunity gaps;using questionnaires and user interviews to research different high-speed railway travelers and derive various needs of users for high-speed railway seats;AHP was used to calculate and prioritize the target user requirements, and then QFD was used to determine the weights of each design requirement point. Conclusion: This paper aims to provide design ideas and future development trends for the design of high-speed railway seats in the post-pandemic era by using INPD, AHP and QFD methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The purpose of this paper is to examine the transformation of Indonesian Railways Public Transport Information Services Through Social Media in the Covid-19 Pandemic Era. The existence of social media makes it easier for people to know the rules that need to be met when traveling. The social media platform used in writing is an twitter account. The analysis of this research uses 4 Twitter social media accounts from KAI Airport, KAI Commuter, Kereta Api Kita, and Kereta Api Indonesia. The method used his Qualitative Data Analysis Software and analysis through Nvivo. From the results of the research of the four accounts, KAI Airport's monthly tweets are accounts that are often used by users in finding information in the era of globalization of Covid-19, while KAI Commuters is a search for information through the hashtag #rekancommuters, and the word that often appears in the four accounts is #rekancommuters. via word cloud. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic highlights the pursuit of business continuity efforts in a variety of companies that adapt their theoretical plans to the ever-changing environment of today's society. Business continuity generally increases the resilience of businesses to disruptions, interruptions, or loss of ability to meet their key objectives. Business continuity is therefore important in fulfilling the agreed tasks and outlined strategies. Theoretically, most railway companies have a business continuity plan, however, get hardly ever implemented. The main reason behind that is the fact the railway participation in the transport system, has a very specific role in providing transport services. The continuity of railway activities fulfils a specific task, providing transport services, which is one of the basic functions of the state.The application of participatory continuity can also be used in air transport. © 2021 IEEE.
ABSTRACT
Site visits or field trips have been a tool utilized by construction engineering and management educators to engage students in active learning, assist traditional lessons, and attain stronger and deeper student learning experiences. Nevertheless, site visits present major logistical and accessibility challenges, which reduces the number of students that have access to the benefits of such a technique. The limitations for site visits have further broadened recently, as COVID-19 public health concerns has forced educators to move to online course delivery quickly and the majority of site visits have been canceled. This study presents the development of VR-OnSite-a web-based virtual environment that offers a social field trip experience for large groups of students that resembles real-world jobsite conditions. A pilot study was conducted with sixteen students that followed a guided site visit within the context of a railway transportation project. The use of VR-OnSite resulted in statistically significant increase in student perceived knowledge in railway transportation and high engagement during the virtual site visit. © 2022 ASCE.
ABSTRACT
With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m-2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m-1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54%under this epidemic. IEEE
ABSTRACT
Passenger flow at a new high-speed railway station presents significant uncertainty during COVID-19, which brings a huge challenge to the daily management and operation of the station. To detect the future development trend of demand and reduce the impact of its fluctuation on the daily operation of the station, three classical forecast methods are applied to predict the passenger flow in and out of the station during workdays in this paper. Furthermore, the performance of these methods is compared by conducting a case study of Huairou South Station. The results show that the ARIMA model (autoregressive integrated moving average model) shows better performance than the neural network model and Bass model (Bass diffusion model). Finally, a revised ARIMA model is introduced to predict the passenger flow of the National Day. © 2021 IEEE.
ABSTRACT
The evaluation of perceived safeness and risk by persons is particularly valuable for safety and security handling. Each individual is influenced by others’ opinions on safety and security and the Internet embodies the place where these opinions are mostly pursued, acquired, and estimated. From this point of view, social networks play a considerable influential role. For this reason, Opinion Mining and Sentiment Analysis have found noteworthy uses in a variety of situations and one of the most interesting is embodied by public safety and security. The goal of this work is to examine the perception of safety and risk within the railway stations of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the railway stations of London Victoria and Waterloo and the railway stations of Rome Termini and Tiburtina were considered, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents present in Twitter. © 2021 IEEE.
ABSTRACT
Based on a stated preference survey, we comprehensively analyze the travel psychology of residents and the advantages and disadvantages of rail transit and conventional buses, travel time, travel cost, travel security, and vehicle comfort and investigate the relationship between the relevant influencing factors and the transition probability from rail transit to buses. A stochastic utility theory is introduced to describe the transfer behavior pertaining to travel modes, and a binary Logit model for diversion transfer is constructed. The decision tree is also used to predict the diversion transfer. Then, based on the large amount of travel willingness data obtained through the stated preference survey, a maximum likelihood estimation method is used to calibrate the parameters of the binary Logit model. The performance of the binary Logit proves to be better than that of the decision tree. Results show that the travel time most notably affects the passenger flow transfer, followed by the vehicle comfort. Finally, Guangzhou Rail Transit Line 3 is considered an example, and the diversion route planning and design are performed according to the constructed diversion transfer probability model to verify the effectiveness and practicability of the model. The research provides an effective theoretical basis and technical reference for other cities to perform rail traffic diversion planning. Based on these results, the following suggestions can be made: (1) the organization of public transportation routes, delivery volume, and travel speed outside should be improved;(2) undertaking combined operation of bus and rail transportation and integrated development is preferred;(3) the transportation management should focus on the comprehensive function development and hardware support of public transportation stations. The convenience and comfort of rail transit are closely related to the facilities and functions of the stations and their connections, which should be highly valued.