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
To increase overall passenger satisfaction, the comfort of railways was reconsidered from a passenger perspective. We twice conducted a web-based survey (February and November 2020) on passenger comfort during several representative travel phases (from planning to destination) and developed a draft evaluation index to estimate the overall comfort from the comfort of each travel phase. We confirm that this draft evaluation index created in the first survey shows 70 to 80% estimation accuracy against the second survey data affected by COVID-19, and that the overall comfort can be stably estimated from the comfort of each travel phase. © 2022 Ken-yusha Inc.. All rights reserved.
ABSTRACT
Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. © 2022 IEEE.
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
Spurred by fall in passenger numbers following the COVID-19 pandemic, railways today have made rapid progress in improving operational efficiency and labor saving measures. DX (digital transformation) technology, which has been applied to automatic train operation, inter alia, is therefore attracting attention. Another focal point for railways is the realization of global decarbonization, illustrated by "2050 Carbon Neutral" goal declared by the Suga Cabinet and "COP26." This paper introduces the outlines of three technologies related to these topics, namely, a "Method for evaluating wheel slide protection (WSP) performance by hybrid simulator," a "Method applying neural networks to detect abnormal noise during train operation," and "High efficiency of diesel electric railcars." © 2022 Ken-yusha Inc.. All rights reserved.
ABSTRACT
In light of the ongoing COVID-19 pandemic, it is important to analyse the ventilation system of an AC coach for safer as well as comfortable ride. In this study we have simulated the airflow, temperature distribution and velocity distribution inside the cabin, to find out the best layout for comfortable temperature as well as reduced chances of airborne infection. We have simulated various ventilation layouts of the 2 tier AC train coach of Indian Railways, to study the effect of the position of the inlet and outlet ports on the temperature and velocity distribution inside the cabin. CFD analysis was done using the Ansys Fluent solver employing the realizable k-ε model to solve the turbulence problem. Herein, a total of 12 layouts were simulated with 6 heated manikins sitting inside the cabin. The results of the study suggested that the temperature distribution inside the cabin changes significantly with a change in the inlet port position. Further, the layout with the above window and/or roof outlet has a relatively lower cabin temperature. This study forms the basis for further investigations to analyse the transmission of infection via cough droplets inside the cabin (unreported here). The results of this research are important for finding the optimum position of the inlet and outlet ports in AC coaches to enhance the overall thermal comfort and reduce infection transmission inside the cabin. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
It will soon be two years since the outbreak of the COVID-19 infection was first confirmed\ but the situation is not yet under control, and various measures are still required in various industries. In this report, based on the "Guidelines for Countermeasures against COVID-19 Infections in Railway Business" 0 and other relevant guidelines, we will introduce the measures that Tokyo Metro (hereinafter referred to as "the Company") has taken to ensure that passengers can use railroads with a sense of security and the measures that its employees have taken to prevent the spread of infections. (Information up to the end of October 2021). © 2022 Japan Railway Engineers' Association. All rights reserved.
ABSTRACT
The spread of new coronavirus infections (hereinafter referred to as COVID-19), which began in Japan at the end of 2019, has greatly affected railway business. There is an urgent need to reduce fixed costs while responding to changing needs for mobility in a post COVID-19 society. As such it is necessary to speed up research and development so that it can contribute to helping railway companies reduce fixed costs. This paper first introduces an overall picture of research and development related to the sophistication of automatic operation and its application to conventional railway lines. Secondly, it introduces ongoing research and development efforts related to the utilization of non-failsafe processors or a public communication network such as 5th Generation mobile communication systems (5G) for critical applications in the railway field. Finally, it presents the direction and policy of future research and development for equipment saving and cost reduction. © 2022 Ken-yusha Inc.. All rights reserved.
ABSTRACT
Atmospheric particulate matter (PM10) is one of the most important pollutants for human health, and road transport could be a major anthropogenic source of it. Several research studies have shown the impact of roads on the air quality in urban areas, but the relationship between road and rail networks and ambient PM10 concentrations has not been well studied, especially in suburban and rural landscapes. In this study, we examined the link between the spatial characteristics of each road type (motorway, primary road, secondary road, and railway) and the annual average PM10 concentration. We used the European 2931 air quality (AQ) station dataset, which is classified into urban, suburban, and rural landscapes. Our results show that in urban and rural landscapes, the spatial characteristics (the density of the road network and its distance from the AQ monitoring points) have a significant statistical relationship with PM10 concentrations. According to our findings from AQ monitoring sites within the urban landscape, there is a significant negative relationship between the annual average PM10 concentration and the density of the railway network. This result can be explained by the driving wind generated by railway trains (mainly electric trains). Among the road network types, all road types in the urban landscape, only motorways in the suburban landscape, and only residential roads in the rural landscape have a significant positive statistical relationship with the PM10 values at the AQ monitoring points. Our results show that in the suburban zones, which represent the rural–urban fringe, motorways have a strong influence on PM-related air pollution. In the suburban areas, the speed of vehicles changes frequently near motorways and intersections, so higher traffic-related PM10 emission levels can be expected in these areas. The findings of this study can be used to decrease transportation-related environmental conflicts related to the air quality in urban, urban–rural fringe, and rural (agricultural) landscapes.
ABSTRACT
Carbon neutrality is a necessary goal as a countermeasure against climate change. Therefore, it has become more important to promote further energy saving and the use of energy storage systems in railway systems. Notwithstanding, falls in passenger traffic due to COVID-19 have had a significant impact on railway management, and reducing infrastructure maintenance costs has become an urgent issue. This paper presents recent research and development on power supply systems, especially for decarbonizing the railways and reducing resources required for maintenance of overhead contact line systems. © 2022 Ken-yusha Inc.. 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
Sanitizing railway stations is a relevant issue especially due to the recent evolution of the Covid-19 pandemic. In this work, we propose a multi-robot approach to sanitize railway stations based on a distributed Deep Q-Learning technique. The framework relies on anonymous information from existing WiFi networks to localize passengers inside the station and to develop a map of possible risky areas to be sanitized. Starting from this map, a swarm of cleaning robots, each one endowed with a robot-specific convolutional neural network, learns how to on-line cooperate inside the station in order to maximize the sanitized area depending on the presence of the passengers. © 2021 Copyright for this paper by its authors.
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.