Search Results (28)

The reflectance-based vicarious calibration approach uses measurements at well-understood test sites to provide top-of-atmosphere reference reflectance values suitable for inter-calibration approaches and does not require coincident views. The challenge is that results from such data may suffer from high variability from day to day. Data from high-quality sensors, such as the imaging spectrometers on the International Space Station (ISS) platform, provide an opportunity to use improved fine spectral information about the test sites with various sun/sensor geometries and site surface and atmospheric conditions to improve the test sites’ characterization. The results here are based on data from the DLR Earth Sensing Imaging Spectrometer (DESIS) instrument installed on the ISS since 2018 combined with output from the Radiometric Calibration Network (RadCalNet) site at Railroad Valley Playa (RRV) to decouple the effects of sun/sensor geometry from the RadCalNet predictions. The approach here uses the precessing orbit of the ISS to allow similar sensor view zenith angles at varying sun angles over short periods that limit the impact of any sensor changes and highlight the bi-directional effects of the surface reflectance and atmospheric conditions. DESIS data collected at (i) similar solar angles but varying view angles, (ii) similar sensor angles and varying solar angles, and (iii) similar scatter angles are compared. The DESIS results indicate that the top-of-atmosphere reflectance spectra for RRV at similar solar zenith angles but with varying sensor viewing angles provide more consistent data than those with varying solar zenith but with similar sensor viewing angles. In addition, comparisons of reflectance spectra of the site performed in terms of the sensor view scatter angle show good agreement, indicating that a directional reflectance correction would be straightforward and could offer a significant improvement in the use of RadCalNet data. The work shows that observations from imaging spectroscopy data from DESIS, and eventually Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and the climate-quality sensor CLARREO Pathfinder (CPF), provide the opportunity for the development of a model-based, SI-traceable prediction of at-sensor radiance over the RRV site that would serve as the basis for similar site characterizations with error budgets valid for arbitrary view and illumination angles. Full article

At the time of its construction, which started in 1913, the architectural design of Moscow’s Kazan Station was considered by many to be out of step with the avant-garde creative energies that pervaded fin-de-siècle Russian culture. The same opinion applied to the artworks that were installed to decorate the station’s interior. In the decades since, art historians have generally shared the judgments levied by those who complained about the station’s supposed deficits in the 1910s. The purpose of this article is to show that, while the designs and décor of Kazan Station were indeed anachronistic—especially considering the high-tech purposes and functions of the industrial-era railroad station—the anachronism, far from reflecting a lack of awareness or innovative ability, resulted from conscious decisions on the part of Alexei Shchusev as architect, Alexandre Benois as the individual who selected artists to work on the station, and the artists themselves, including Nikolai Roerich and Pavel Kuznetsov, namely, those who built and decorated the station deliberately concealed the station’s inherently modernist and utilitarian nature behind a backward-looking, past-oriented façade, both to fulfill their mission of commemorating old Russia’s imperial expansion and subjugation of the East and to assuage the social and cultural anxieties often stirred up in the late 1800s and early 1900s by the construction of infrastructural assets such as railroad stations. Full article

This review aims to systematically evaluate existing literature on reducing suicides along railroads, with specific focus on effectiveness, limitations, and research gaps in the current evidence base. Database searches were conducted in PubMed, PsycInfo, Scopus, Embase, and CINAHL covering studies published until 30 November 2024. After screening 623 studies and their references, 51 studies were included; 26 empirically assessed rail-related prevention interventions and 25 provided relevant qualitative insights. Physical barriers like removal of grade crossings, addition of fencing, and platform screen doors (PSDs) showed significant promise. Full-height PSDs eliminated all suicides and half-height PSDs significantly reduced suicide incidence. Fencing was found to be effective but raised concerns about feasibility and must be part of a comprehensive approach to mitigate potential displacement. Safe media reporting was linked to decreased suicides and a reduced risk of contagion, and CCTV monitoring and suicide pits also showed potential but had limited research. Other strategies showed mixed evidence and required additional evaluation. Some studies, particularly on physical barriers, showed possible displacement effects to other stations, highlighting the need for studies larger in geographic and temporal scope. Our findings support certain prevention interventions, but generalizability is limited by scope of research and methodological concerns. Overall, our findings highlight the need for broader, long-term studies to confirm efficacy and establish comprehensive, scalable approaches for policy implementation. Full article

The purpose of this study is to solve the problem of low load factor and profit margin in the point-to-point transportation of international freight trains through the assembly transportation organization mode. A bi-objective location-routing optimization model is constructed to optimize problems, such as the location of the assembly center, route of freight assembly, frequency of international freight trains, and number of formations. The objectives are to minimize the total comprehensive cost and maximize the average satisfaction of the shippers. Considering the impact of blockchain technology, the proportion of customs clearance time reduction after blockchain implementation, the proportion of customs clearance fee reduction after blockchain implementation, and the cost of blockchain technology are introduced into the model. The case study is based on railroad transportation data for 2022. In this case, 43 stations in the Indo-China Peninsula are selected as origin stations, and two Chinese stations are designated terminal stations. An improved NSGA-II algorithm (ANSGAII-OD) is proposed to resolve the location-routing optimization model. This algorithm is based on opposition-based learning and its dominant strength. The case study indicates that assembly transportation is advantageous compared with direct transportation. Moreover, the comprehensive cost is reduced by 19.77%. Furthermore, blockchain technology can effectively reduce costs and improve transportation efficiency. After the implementation of blockchain technology, the comprehensive cost is reduced by 8.10%, whereas the average satisfaction of shippers is increased by 10.35%. Full article

This paper deals with the optimization of railcar flow assignment among multiple marshalling stations oriented to making a stage plan. We aimed to minimize the total dwell time of the on-time and delayed allocated railcar flow at the station within a stage plan. Considering the limitations of the disassembly and assembly capacity of marshalling stations, the limitation of the transfer operation capacity between marshalling stations, the connection time limit of inbound and outbound trains, and the limitation of trains with different full workloads, we constructed a multi-marshalling station railcar flow assignment optimization model. Furthermore, for comparison, we built a model that only satisfied the train equivalent length limitation and a model that only satisfied the train hauling weight limitation. Finally, we designed experimental scenarios and conducted comparative analyses of the proposed model and a model that considered only a single full workload limitation. The results show that the railcar flow assignment scheme that considers different full workload limitations shortens the dwell time of railcar flow at the station, reduces the number of undispatched outbound trains within the time range of the stage plan, and effectively improves the terminal operation efficiency within the stage plan. Thus, the proposed scheme can achieve an effective railcar flow assignment within a multi-marshalling station railroad terminal while minimizing the dwell time. Full article

This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf contamination, which can cause partial loss of braking and lead to undesired events such as station overruns. Better understanding the adhesion-reducing impact of leaf contamination significantly improves railway engineering practices to counter their effects on train braking and traction. This experimental study evaluates the reduction in traction and braking forces (collectively called “adhesion”) as a function of leaf volume, using two leaf species that commonly grow along U.S. railroad tracks. The test methods rely on the chosen leaves’ transpiration characteristics while ensuring the result’s reproducibility. Leaves were symmetrically positioned on the wheel surface, centered around the mid-rib area within the wear band, and taped on the edges far from the wear band. The critical test parameters (i.e., wheel load, wheel velocity, and percentage creepage) are kept constant among the tests. At the same time, leaf volume is reduced from a maximum amount that covers the entire wheel surface (100% coverage) to no leaves (0%). The latter is used as the baseline. The percentage creepage is kept constant at an exaggerated amount of 2% to accelerate the test time. The results indicate a nonlinear relationship between leaf volume and the loss of braking. Even a small amount of leaf contamination causes a significant reduction in adhesion by as much as 50% compared with no contamination (i.e., baseline). Increasing leaf volume results in contact saturation, beyond which adhesion is not reduced. The minimum adhesion observed in this study is 20% of the maximum adhesion that occurs when no leaf contamination is present. Full article

Bangkok, the capital of Thailand, grapples with several urban challenges, including traffic accidents, congestion, and underdeveloped sidewalks. These issues stem from the escalating use of private cars and motorcycles driven by rapid urbanization, accompanying economic growth, and the unique street geometry of the city. Furthermore, despite the development of railroads, issues persist with the poor walking environment for pedestrian access to stations, posing a challenge to the promotion of safe and environmentally friendly public transportation. This study evaluated streets’ spatial performance, focusing on the performance of soi, the intricate network of narrow urban pathways in Bangkok. Leveraging an AI model capturing pedestrian value, this study subsequently assessed accessibility from typical residential areas to public transportation stations. The findings shed light on the challenges faced by sois in Bangkok. Based on the findings, practical implications were discussed and provided to inform urban designers, with the aim of promoting public transportation usage and encouraging a more sustainable urban form. Full article

In the extensive framework of the historiography on the relationship between railroads and cities that began in the second half of the 19th century, a new phase of thought and action emerged from the 1980s, driven by a transport-oriented development approach and the node–place model within the paradigms of New Urbanism and sustainable urbanism, respectively. This highlights the need to integrate railway planning and urban and territorial planning to take advantage of the development potential of the urban areas surrounding railway stations. In this context, a subject rarely analyzed in Spain is the urban areas around commuter railway stations and the opportunity they represent to rethink growth and development on an urban and metropolitan scale. This study explores and shows the roles that municipalities with commuter stations and their surrounding areas play and could play as a basis for leading an urban and territorial strategy based on a polycentric model in favor of more sustainable mobility and development in the Madrid urban region. Full article

As the primary solution to the issue of high passenger traffic in urban areas, rail transit has a significant impact on the structural form of cities and regional development. Additionally, it has varying degrees of a premium effect on land value around stations. Current research on the factors influencing the premium effect of rail transit station areas mainly focuses on the macro level of the station area circle, with more attention given to the premium caused by distance and functional differences. Most research objects are typically urban center lines or stations. However, this study focuses on the core area of the station and concentrates on the impact of the construction of integrated station–city facilities on the choice of pedestrian routes and the enhancement of pedestrian accessibility. It also explores whether this enhancement is associated with the premium benefits of ancillary commercial development. To achieve this goal, this paper integrates models from several related studies to conduct a comprehensive assessment. Firstly, it uses a spatial panel econometric model to improve the classical characteristic price method model. It then combines the ideas and models of the cost–benefit analysis method, taking the Odakyu Odawara Line of the Japanese suburban railroad as an example. This analysis explores the mechanism and factors influencing the rent premium of commercial facilities in the suburban rail station area and systematically assesses the combined station–city facilities. The study evaluates the social benefits (enhanced walkability) and economic value (premium value added from commercial facilities) of the combined station–city facilities systematically. The results of the study show that (1) the premium benefits of suburban railroad station area commercial facilities are significantly related to the type of station–city combination facilities, combination mode, and walking time and weakly related to the location factor. Additionally, (2) the results of the cost–benefit valuation analysis based on the Ebina Station verify that a reasonable design of station–city combination facilities can effectively enhance the proximity of commercial facilities to the station and improve the walking accessibility, thus promoting the premium benefits. The study demonstrates that a reasonable design of the combined station and city facilities can effectively enhance the proximity of commercial facilities to the station and improve pedestrian accessibility, promoting premium benefits, which can quickly feed the construction cost of the station and achieve positive revenue in the short term. Therefore, the results of the study provide a quantitative reference for the planning and design of suburban stations. Full article

Current railroad customs clearance systems are problematic in that the movement of trains is occasionally restricted for extended periods during inspections to verify cargo integrity at customs clearance. Consequently, significant human and material resources are consumed to obtain customs clearance to the destination, considering different processes exist for cross-border trade. Therefore, we developed a cross-border blockchain-based non-stop customs clearance (NSCC) system to address these delays and reduce resource consumption for cross-border trains. The integrity, stability, and traceability of blockchain technology are used to construct a stable and reliable customs clearance system to address these problems. The proposed method connects diverse trade and customs clearance agreements in a single blockchain network, which ensures integrity and minimal resource consumption, and includes railroads, freight vehicles, and transit stations in addition to the current customs clearance system. The integrity and confidentiality of customs clearance data are protected using sequence diagrams and the blockchain to strengthen the resilience of the NSCC process against attacks; the blockchain-based NSCC system structurally verifies the attack resilience based on matching sequences. The results confirm that the blockchain-based NSCC system is time- and cost-efficient compared with the current customs clearance system and offers improved attack resilience. Full article

Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is so complex that its maintenance is challenging and expensive. Therefore, using artificial intelligence for railway safety has attracted many researchers. This paper examines artificial intelligence applications for railway safety, mainly focusing on deep learning approaches. This paper first introduces deep learning methods widely used for railway safety. Then, we investigated and classified earlier studies into four representative application areas: (1) railway infrastructure (catenary, surface, components, and geometry), (2) train body and bogie (door, wheel, suspension, bearing, etc.), (3) operation (railway detection, railroad trespassing, wind risk, train running safety, etc.), and (4) station (air quality control, accident prevention, etc.). We present fundamental problems and popular approaches for each application area. Finally, based on the literature reviews, we discuss the opportunities and challenges of artificial intelligence for railway safety. Full article

The single-baseline solution (SBS) model has been widely adopted by the existing global navigation satellite system (GNSS) deformation monitoring systems due to its theoretical simplicity and ease of implementation. However, the SBS model neglects the mathematical correlation between baselines, and the accuracy and reliability can be degraded for baselines with long length, large height difference or frequent satellite signal occlusion. When monitoring large-area ground settlement or long-spanned linear objects such as bridges and railroads, multiple reference stations are frequently utilized, which can be exploited to improve the monitoring performance. Therefore, this paper evaluates the multi-baseline solution (MBS) model, and constrained-MBS (CMBS) model that has a prior constraint of the spatial-correlated tropospheric delay. The reliability and validity of the MBS model are verified using GPS/BDS datasets from ground settlement deformation monitoring with a baseline length of about 20 km and a height difference of about 200 m. Numerical results show that, compared with the SBS model, the MBS model can reduce the positioning standard deviation (STD) and root-mean-squared (RMS) errors by up to (47.4/51.3/66.2%) and (56.9/60.4/58.4%) in the north/east/up components, respectively. Moreover, the combined GPS/BDS positioning performance for the MBS model outperforms the GPS-only and BDS-only positioning models, with an average accuracy improvement of about 13.8 and 25.8%, with the highest accuracy improvement of about 41.6 and 43.8%, respectively. With the additional tropospheric delay constraint, the CMBS model improves the monitoring precision in the up direction by about 45.0%. Full article

This study provides a systematic method for determining the proper rent fee of a port railway station with a litigation case study of Busan port container terminal. The Port Rail Station Operating Company (PRSOC) leases and operates the port railway station of the port authority (PA). The PA wants to receive a rent fee with the goal of recovering the investment cost, and the PRSOC wants to pay rent to the extent that it can generate an appropriate profit. In order to reasonably estimate the rent fee, this study attempted using a method of estimating the capacity-based demand of the rail station. A recent discussion of terminal rentals concerns what to rent. That is, will only the infrastructure be leased, or will the substructure and the superstructure be leased? Will the infrastructure, superstructure, and equipment be leased? Rail station capacity encounters a bottleneck when measuring railroad track capacity, i.e. RMGC capacity. In other words, arbitrary demand estimation leads to operating losses for PRSOC and may also cause losses to investors because investment costs cannot be recovered. In this study, data such as investment cost, operating cost, and sales required for the construction of the rail station were collected from the Ministry of Ocean and Fishery, PA, and PRSOC. Based on these data, a proper rent fee is proposed by analyzing the cash flow on the premise of operation for the next 30 years. This study adopts the discounted cashflow (DCF) method because DCF allows an objective and consistent comparison of rent fee levels from the PA and PRSOC perspectives. Using DCF, this study finds that the proper rent fee from the perspective of the PA is USD 397,045, while the PRSOC’s proper rent fee is USD 355,801. Thus, it is reasonable to set the standard for determining the proper rent fee by calculating and comparing the rent fee level from the perspective of PA’s investment cost recovery and PRSOC’s operating balance maintenance perspective. This study suggests that the DCF method should be applied to standardize the rent fee calculation system for the port railway station. Full article

The integration of the cultural/creative and tourism industries is one way to present different types of regional cultural heritage to the world. This study examined the coupling of intangible cultural heritage and cultural tourism development in Hunan, seeking a pattern for the development of the former under the latter. The study included analyzing the coupling between tourism resources and ICH and exploring modes of revitalizing or developing ICH in the context of cultural tourism. We used methods of GIS spatial technology, including nearest neighbor indexing, kernel density estimation, and queuing analysis, to estimate the kernel density of a buffer of points (high-speed railway stations, and 5A and 4A scenic spots) and lines (high-speed railways and highways). The results show (1) the overall spatial aggregation of Hunan’s ICH resources; (2) how tourist attractions drive ICH resources, as illustrated by the coupling between the spatial distribution of ≥4A attractions and ICH; and (3) the pattern of high-speed road and railroad network development, featuring region-wide guidance for the development of Hunan’s ICH resources. This research not only provides development patterns and concepts for the study of intangible cultural heritage and tourism development planning in Hunan, but also provides useful references for the combination of the two in other regions. Full article

Detecting railroad station anomalies is a critical task prior to segmentation and making optimization decisions for each cluster. Three types of anomalies (local clustered, axis paralleled, and surrounded by normal instances) caused by the specialty of railroad operations bring the existing methods non-trivial challenges in detecting them accurately and efficiently. To tackle this limitation of existing methods, this paper proposes a novel anomaly detection method named Huffman Anomaly Detection Forest (HuffForest) to detect station anomalies, which leverages Huffman encoding to measure abnormalities in certain railroad scenarios with high accuracy. The proposed method establishes a Huffman forest by constructing trees from the perspective of data points and subsequently computes anomaly scores of instances considering both local and global information. A sampling-based version is also developed to improve scalability for large datasets. Taking advantage of the encoding mechanism, the proposed method can effectively recognize the underlying patterns of railroad stations and detect outliers in various complicated scenarios where the conventional methods are not reliable. Experiment results on both synthesized and public benchmarks are demonstrated to show the advances of the proposed method compared to the state-of-the-art isolation forest (iForest) and local outlier factor (LOF) methods on detection accuracy with an acceptable computational complexity. Full article