Mobile User Indoor-Outdoor Detection through Physical Daily Activities

An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications

Identifying climate-related failures in railway infrastructure using machine learning

Climate change impacts pose challenges to a dependable operation of railway infrastructure assets, thus necessitating understanding and mitigating its effects. This study proposes a machine learning framework to distinguish between climatic and non-climatic failures in railway infrastructure. The maintenance data of turnout assets from Sweden's railway were collected and integrated with asset design, geographical and meteorological parameters. Various machine learning algorithms were employed to classify failures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study identified minimum-temperature and quantity of snow and rain prior to the event as the most influential factors. The 24-hour time horizon prior to failure emerged as the most effective time window for the classification. The practical implications and applications include enhancement of maintenance and renewal process, supporting more effective resource allocation, and implementing climate adaptation measures towards resilience railway infrastructure management

Climate Change Impact Assessment on Railway Maintenance

Modern societies have become more and more complex, interconnected, and heavily dependent ontransport infrastructure. Moreover, most transport infrastructures were conceptualized, designed and built withoutanticipating the future variations of climate change. Climate changes have a negative impact on the railway systemand related costs. Increased temperatures, precipitation, sea levels, and frequency of extremely adverse weatherevents such as floods, heatwaves, and heavy snowfall pose major risks and consequences for railway infrastructureassets, operations and maintenance. Approximately, 5 to 10% of total failures and 60% of delays of trains are dueto various climate change impacts of railway infrastructure in northern Europe. In Sweden, weather-related failureswere responsible for 50% of train delays in switches and crossings (S&C).The paper explores a pathway toward climate resilience in transport networks and assess the climate change impactson railway infrastructure by integrating transport infrastructure health information with meteorological, satellite,and expert knowledge. The paper provides recommendations considering adaptation options to ensure an effectiveand efficient railway transport operation and maintenance.ISBN for host publication: 978-981-18-5183-4Robust infrastructure – Adapting railway maintenance to climate change (CliMaint

A Survey on Underground Pipelines and Railway Infrastructure at Cross-Sections

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures are critical for society and industry resulting in direct and indirect costs for all the related stakeholders. Pipeline failures are complex processes, which are affected by many factors, both static (e.g., pipe material, size, age, and soil type) and dynamic (e.g., traffic load, pressure zone changes, and environmental impacts). These failures have serious impacts on public due to safety, disruption of traffic, inconvenience to society, environmental impacts and shortage of resources. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within transportation infrastructure. The aim of this study is to identify failure modes and consequences related to the crossing of pipelines in railway corridors. Expert opinion have been collected through two set of questionnaires which have been distributed to the 291 municipalities in the whole Sweden. The failure analysis revealed that pipe deformation has higher impact followed by pipe rupture at cross-section with railway infrastructure. For underground pipeline under railway infrastructure, aging and external load gets higher ranks among different potential failure causes to the pipeline.ISBN för värdpublikation: 978-981-11-2724-3PipeXrai

Combined RAMS and LCC analysis in railway and road transport infrastructures

Life-cycle cost (LCC) analysis is an assessment technique used to evaluate costs incurred during the life-cycle of"br" a system to help in long term decision making. In railway and road transport infrastructures, costs are subject to"br" numerous uncertainties associated to the operation and maintenance phase. By integrating in the LCC the"br" stochastic nature of failure using Reliability, Maintainability, Availability and Safety (RAMS) analyses,"br" maintenance costs can be more reliably estimated. This paper presents an innovative approach for a combined"br" RAMS&LCC methodology for linear transport infrastructures which has been developed under the H2020"br" project INFRALERT. Results of the application of such methodology in two real use cases are shown, one for"br" rail and another one for road. The use cases show how the approach is implemented in practice