Train Station Research Articles

Real-world estimation taps into basic numeric abilities.

Accurately estimating and assessing real-world quantities (e.g., how long it will take to get to the train station; the calorie content of a meal) is a central skill for adaptive cognition. To date, theoretical and empirical work on the mental resources recruited by real-world estimation has focused primarily on the role of domain knowledge (e.g., knowledge of the metric and distributional properties of objects in a domain). Here we examined the role of basic numeric abilities - specifically, symbolic-number mapping - in real-world estimation. In Experiment 1 ( ) and Experiment 2 ( ), participants first completed a country-population estimation task (a task domain commonly used to study real-world estimation) and then completed a number-line task (an approach commonly used to measure symbolic-number mapping). In both experiments, participants with better performance in the number-line task made more accurate estimates in the estimation task. Moreover, Experiment 2 showed that performance in the number-line task predicts estimation accuracy independently of domain knowledge. Further, in Experiment 2 the association between estimation accuracy and symbolic-number mapping did not depend on whether the number-line task involved small numbers (up to 1000) or large numbers that matched the range of the numbers in the estimation task (up to 100,000,000). Our results show for the first time that basic numeric abilities contribute to the estimation of real-world quantities. We discuss implications for theories of real-world estimation and for interventions aiming to improve people's ability to estimate real-world quantities.

Deep learning-based video surveillance system for suspicious activity detection

The need for reliable video surveillance systems to detect and prevent suspicious activities has become more important with the increase in crime and security threats. This paper proposes a real-time video surveillance system based on the Long-term Recurrent Convolutional Network (LRCN) model, which can automatically detect and alert the authority about suspicious activities, such as fighting, accidents, and robbery. Our system comprises two main components: LRCN-based activity recognition and real-time alert generation. We evaluated the performance of the proposed system on a custom dataset compiled from two publicly available datasets and achieved state-of-the-art results in terms of accuracy, precision, and recall. Our results demonstrate the effectiveness and scalability of the LRCN-based video surveillance system for real-time suspicious activity detection. We believe that our proposed system can be deployed in various public places, such as airports, train stations, and shopping malls, to enhance the security and safety of the public.

An Approach for Measuring Spatial Accessibility to Services/Facilities by Urban Community Aged Population

Abstract. Population aging is a global trend and a world-wide challenge to many governments. The high-quality life of the elderly cannot be separated from the support of basic services and facilities. Therefore, it is necessary to conduct analysis and research on the accessibility of urban community services and facilities. It is worth noting that the research in this article is based on master thesis (Bai, 2013). Drawing on the data, methods, and conclusions from the master's thesis, in this paper, an approach is designed for measuring spatial accessibility to services/facilities by a spatially dispersed ageing population in urban areas. The local government area (LGA) of Monash in the Melbourne metropolitan area has been selected the case study area. This study combines GIS-based spatial analytical procedures and two-step floating catchment area (2SFCA) method, in conjunction with mesh block level 2011 population data, service/facility data and transportation network data, to measure and map spatial variations in potential accessibility to services and facilities deemed essential to the aged population. Service facilities considered in the study include bus stops, train stations, bank facilities, shopping centres, post offices, churches, parks, public libraries, community centres, pharmacies, GP clinics and hospitals in Monash LGA. The research results can be used to optimize the spatial planning and layout of public facilities for ageing population.

Playing with Space to Deal with the Contradictions of Customer Sovereignty: An Ethnography of Service Workers’ Spatial Tactics in Train Stations

While the existing literature on service work allows us to understand how customer sovereignty policies constrain service work by transforming servicescapes, we need a more agential approach to how service workers use space as a resource to deal with the tensions resulting from the promotion of customer sovereignty. This article draws on de Certeau’s thinking to fill this gap by looking at how workers play with space constraints and opportunities and deploy spatial tactics to walk a fine line with their customers. Through an ethnographic study of service work in train stations, this article offers a fine-grained empirical account of the spatial tactics used by workers in their daily work. We show how they use space to cope with the tensions in their daily interactions with customers, and how spatial tactics constitute micro-practices of resistance to customer sovereignty policies.

Commuter exposure to smoke and particulate matter air pollution in Auckland during the 2019 New Zealand International Convention Centre fire

ABSTRACT Human mobility typically exhibits temporal and spatial predictability. However, during hazardous events, roadways, footpaths and public transport networks can be disrupted by detours, closures and congestion. Urban fires, exemplified by the October 2019 New Zealand International Convention Centre (NZICC) fire in Auckland, New Zealand, are on the rise, posing threats to life, property and mobility. In this study, we employ geospatial analyses to investigate the impact of the NZICC fire on human mobility, encompassing both driving and walking. We generate predicted surfaces of particulate matter (PM2.5 and PM10) from seven local fixed air monitoring stations to estimate network-based exposure and inhalation dosage during travel. High levels of air pollution during the fire exceeded baseline concentrations, surpassing National Environmental Standards for Air Quality (NESAQ) and World Health Organization (WHO) limits for both PM2.5 and PM10. Private car commuters faced delays and congestion due to road closures, prolonging smoke exposure. Pedestrians, including those accessing essential public transportation infrastructure, experienced unavoidable exposure to smoke along the fastest routes from NZICC to places like Britomart train station and the Downtown Ferry. We recommend increasing the availability and dissemination of air pollutant monitoring data to enhance public awareness of the health risks associated with smoke exposure.

Polycyclic aromatic hydrocarbons (PAHs) in black crusts on stone monuments in Milan: detection, quantification, distributions, and source assessment

In the field of conservation of cultural heritage, one must always consider the environmental conditions in which the works of art are located and the level of atmospheric pollution to which they are exposed, especially in the case of monuments stored outdoors. The present study is focused on the detection and the quantification of polycyclic aromatic hydrocarbons (PAHs) in black crust samples from the Monumental Cemetery of Milan (Italy), and the assessment of their sources through the analysis of the distributions of the different compounds in the samples, together with the use of diagnostic ratios. Six black crust samples taken from funerary monuments were analyzed. Fourteen polycyclic aromatic hydrocarbons were identified (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, indeno[1,2,3-cd]pyrene) by high-performance liquid chromatography with a diode-array detector (HPLC–DAD), with a total concentration from 0.72 to 3.81 μg/g (mean of 1.87 μg/g). The known carcinogenic benzo[a]pyrene accounted for 5–10% of the total polycyclic aromatic hydrocarbons in the samples analyzed, with concentrations up to 0.20 μg/g. Moreover, the study of the distribution and diagnostic ratios allowed us to confirm that anthropogenic sources such as traffic and the proximity of the train station are the major causes of the degradation of the monuments contained in this Cemetery.

Mathematical modelling of the train station of the heart: the atrio-ventricular node.

Mathematical modelling of the train station of the heart: the atrio-ventricular node.

Sustainable Business Models for Innovative Urban Mobility Services

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm.

Spatial Analysis with Detailed Indoor Building Models for Emergency Services

This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations.

Bystanders’ willingness to assist using automated external defibrillators during cardiac arrest

The “bystander effect,” in which the presence of others inhibits rescue actions, has not been specifically examined in the context of cardiac arrest; understanding this effect in relation to rescue with automated external defibrillators (AEDs) is important. This study aims to identify the presence of others as a factor inhibiting rescue actions using an AED, from a social psychology perspective. We collected data through a web-based questionnaire involving registered residents in all 47 prefectures of Japan. The participants were presented with hypothetical scenarios of witnessing cardiac arrest events at train stations, under sparse or crowded conditions, and with or without the presence of competent parties (e.g., station staff or security guards). Their willingness to intervene was assessed across three levels of rescue behavior: (1) running and calling for help, (2) retrieving an AED, and (3) using an AED. This study found evidence of the bystander effect, indicating that the presence of competent others reduced behavioral interventions by bystanders during out-of-hospital cardiac arrest (OHCA) events. Moreover, the perceived presence of competent parties at the scene of a cardiac arrest reduced bystanders’ willingness to initiate rescue under certain circumstances. While many bystanders were willing to initiate rescue efforts in response to calls for help, they resisted rescues involving an AED. This study observes that a bystander effect occurs among bystanders witnessing OHCA, explores the inhibiting effects of identifying competent parties on the initiation of rescue efforts, and suggests that there are significant invisible barriers to using AEDs in rescuing patients with OHCA.

The optimal decision of service provider considering extra waiting area value-added services – pooled or dedicated?

Service providers often provide chargeable extra waiting area value-added services (EWS) to enhance customers’ waiting experiences, e.g., in VIP and business lounges at airports, train stations, luxury brand stores, etc., to improve profitability. The impact of EWS on customers’ value perception is subject to their heterogeneous sensitivities and also depends on the reference effect due to EWS’ observable status. This paper investigates the optimal level of EWS and price decisions for a service provider facing heterogeneous customers, namely the EWS-preferring (E-type) customers and ordinary (O-type) customers, considering the observable status (observable vs. unobservable) of EWS using pooled or dedicated capacities in operations through M/M/1 queue models. The optimal price and level are solved and the optimal profit is calculated under each scenario. It is found that customers’ heterogeneous sensitivities play an important role in the price and intensity decisions. Offering observable EWS to leverage the reference effect in customers’ value perception can lead to a higher profit. Furthermore, using pooled capacity in operations with EWS is more profitable when service costs are higher. These results offer significant managerial implications and provide practical guidelines for service providers regarding the intensity of EWS, service price, and whether EWS should be observable to customers through pooled or dedicated capacities.

Is it time for a European Convention against Racial Profiling?

Four years after the murder of George Floyd and the global Black Lives Matter protests, racial profiling not only remains prevalent in law enforcement practices in- and outside Europe, but takes on new forms in the wake of technological developments enabling large scale data analysis and the use of algorithmic risk profiles. A few years ago, the European Court of Human Rights (ECtHR) first dealt explicitly with the issue of racial profiling in the twin cases of Basu v. Germany and Muhammad v. Switzerland. More recently, the ECtHR held in Wa Baile c. Suisse that Switzerland had violated the prohibition of discrimination by subjecting the applicant to racial profiling at Zürich train station, as part of a police action to apprehend persons without a valid residence permit. 1 While these judgments, especially in Wa Baile, go some way towards securing international State accountability for racial profiling, they also illustrate the difficulties of fighting such profiling as a form of structural discrimination under the ECHR. Meanwhile other human rights bodies, at the European and international levels, have both acknowledged the structural nature of racial profiling and published numerous policy guidelines and recommendations for positive State action to ensure non-discriminatory law enforcement. Yet, to date all of these instruments remain at the level of soft law. Despite the ECtHR's recent efforts, European human rights law thus does not impose legal obligations on States to actively prevent and combat racial profiling. This column signals this gap, and considers some of the pros and cons of adopting a European Convention against Racial Profiling.

Analytical Study on Reducing the Heating Effects of Daylight and Improving Natural Lighting Performance at Erzincan Train Station in the Context of Climate Change

Climate change is an environmental issue that is rapidly escalating due to the effects of global warming. The increase in carbon emissions, along with various human activities such as industrial processes, land use changes, and the reckless consumption of natural resources, are among the primary causes of global warming. Various architectural interventions are being implemented in historical buildings to mitigate the impact of global warming and its consequence, climate change. The study aims to protect the historical building from the harmful effects of climate change by reducing the heating effects of direct sunlight during the summer and enhancing the efficiency of natural lighting throughout the day. In this context, the Erzincan Train Station is located in Erzincan in Eastern Anatolia. Erzincan is one of the most affected by the climate change crisis has been selected for the study. The plans of the Erzincan Train Station were digitized using AutoCAD software and subsequently modeled in three dimensions using Revit software. Daylight analysis was conducted on the created model using the Insight plugin in Revit software. The analysis determined that recommendations should be made for the building's south facade. Based on the climate data of Erzincan province and the conducted analyses, the light shelf system was designed and implemented due to its applicability, shading, and lighting advantages among daylighting systems. The effects of the proposed system were examined using spatial daylight autonomy (sDA) and annual sunlight exposure (ASE) daylight metrics according to LEED v4 EQc7 standards. The study found that the light shelf system resulted in a 5% reduction in ASE values building-wide and a 6% reduction in the average values of specific areas while increasing sDA values. In this context, it has been observed that areas experiencing a decrease in direct sunlight exposure (ASE values) also show an increase in sufficient daylight exposure (sDA values), which could contribute to improving the building's energy performance.

THE NEW PEDESTRIAN FOOTBRIDGE AT LUXEMBOURG RAILWAY STATION

AbstractThe new pedestrian footbridge at Luxembourg Train Station is an integral component of the revamped platform canopies. Its unique undulating wave design, symbolic of motion, is mirrored in the canopies' roofs, creating asynchronous waves between platforms. As the footbridge aligns with the wave, it gracefully encompasses the translucent tube of the new structure.An integrated staircase fits beneath this architectural wave, with an elevator opposite for easy platform access. This translucent prism stands in stark contrast to the sweeping curves of the roofs and the footbridge itself.Prioritizing user comfort and safety, a translucent design was chosen, eliminating blind spots. The 100‐meter footbridge is constructed with an orthotropic steel deck, offering substantial spans with minimal height. The roof's central section serves to reduce sun exposure and conceal electrical wiring.The assembly process involved crane work during railway track closures over consecutive weekends, using a CC3800 crane with a maximum capacity of 650 tons

Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.

Techno-Economic Analysis of Solar-Wind Powered EV Charging Stations at Train Station Parking Lots

Techno-Economic Analysis of Solar-Wind Powered EV Charging Stations at Train Station Parking Lots

Impacts of COVID-19 on the premiums of proximity to railway stations: An in-depth analysis using passenger flows

As a result of the coronavirus (COVID-19) pandemic, work and leisure patterns have changed significantly, as have commuting and travel behavior. This has led to changes in the degree of dependence and demand for mass transportation. This paper proposes that these changes decrease people's demand to live near public transportation services and cause the decline of the positive externality created by public transit as measured by house prices. This paper explores the train stations of Taiwan's two largest cities (Taipei and Kaohsiung) by adopting various hedonic price models to estimate the COVID-19 effect on the premiums of proximity to rail stops. We then analyze whether the changes in premiums are affected by the passenger flow at the station. This paper uses four variables to measure passenger flows: the number of total passengers on services, the net number of passengers (the number leaving the local area minus the number of arriving at the local area), and the expected numbers of commuters and non-commuters. The empirical results of this paper show that after the COVID-19 outbreak, the monetary value of the proximity of railway stations decreased significantly. However, the COVID-19 effect on different stations is heterogeneous. The post-COVID-19 decrease in the positive externality associated with proximity to a rail station is significantly affected by passenger flows. The changes in the positive externality of stations found in this study suggest the public has reduced their dependence on stations due to changes in commuting behavior. We urge the government to pay attention to whether the commuting flows and the public's demand for public transportation have shifted and the use of vehicles and road transportation has risen.

A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne’s zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.

The linguistic landscape of Kotabaru Malang Train Station: language representation in public space

The linguistic landscape of Kotabaru Malang Train Station: language representation in public space

A robust optimisation approach of train timetabling for freight transportation using high-speed railway

To ensure reliable services for high-speed freight rail transport, we address the issue of robust freight train timetabling by incorporating buffer times. Unlike passenger trains with prescribed timetables, high-speed freight train timetables need to be scheduled from the beginning and integrated into existing passenger schedules. To achieve reliable high-speed rail freight services, we introduce robust parameters that determine the buffer times for train operations and station stops. Using these, we develop an integer linear robust model aimed at maximizing timetable robustness, considering given control parameters, while minimizing travel and deviation times. We employ an integer Benders decomposition algorithm to solve this model efficiently. Our robust optimization method is validated through experiments using data from the Chengdu-Chongqing high-speed railway, demonstrating the efficacy and efficiency of our model and algorithm, especially in balancing efficiency and robustness.