Real Usage Pattern Research Articles

Market demands vs. scientific realities: A comparative analysis in the context of BIM-based and user-centred lighting control

Real utilisation patterns often only become apparent when a building is put into operation. Since planning is therefore based on several assumptions, the potential of building technology is often limited. Post-occupancy evaluations offer the potential to adapt operation to real usage patterns and draw conclusions for future planning, thereby improving performance. Although solution approaches are available in the literature, this potential is currently hardly being utilised. A survey in the construction industry is therefore used to compare market requirements with existing solution concepts. The results emphasise the relevance of auto-commissioning, contractual agreements on performance evaluation and the coupling of digital twins with post-occupancy evaluations. A stronger anchoring of the control industry in BIM is proving to be necessary for better cross-disciplinary collaboration between all parties involved. Even though the focus of this study is on lighting, the most important findings are in principle transferable to other trades.

Cycle Characterization of SiO-Based Lithium-Ion-Batteries Using Real Load Profiles

Understanding the cyclic behavior of Lithium-Ion Batteries (LIBs) is crucial for optimizing their performance and extending their operational lifespan. This work presents a study on the cycle characterization of silicon-oxide-based (SiOx) cells, focusing on the impact of real load profiles and state-of-charge (SoC) ranges while varying the SiOx content in the cells. Various load profiles representing real usage patterns obtained from an industrial partner were applied to SiO-based pouch cells. These load profiles are represented over different SoC ranges to explore the effect of varying levels of charge/discharge on battery aging. The aging characteristics of the batteries are evaluated by monitoring capacity fade, state-of-health (SoH), and capacity end-point-slippage. The experimental results demonstrate that the different SiOx content of the investigated cells and the SoC range significantly influence the cycle behavior of the cells. The resulting capacity loss was affected especially by the anode overhang effect. Cycling under high SoC conditions accelerates capacity fade and leads to higher SoH loss. The findings also indicate that SiO-based cells exhibited higher aging than traditional graphite-based cells. The capacity fade rate increased at higher SiOx content.

A data-driven dynamic repositioning model in bicycle-sharing systems

The new generation of bicycle-sharing is an O2O (online-to-offline) platform service that enables the users to access the bicycle with a smartphone App. This paper proposes a dynamic repositioning model with predicted demand, where the repositioning time interval is fixed. A data-driven Neural Network (NN) approach is introduced to forecast the bicycle-sharing demand. The repositioning objective function at each time interval is defined to simultaneously minimize the operator cost and penalty cost. In addition to the normal constraints in static repositioning problem, flow conservation, inventory-balance and travel time constraints are taken into account. Due to the non-deterministic polynomial-time hard (NP-hard) nature of this model, a hybrid metaheuristic approach of Adaptive Genetic Algorithm (AGA) and Granular Tabu Search (GTS) algorithm is applied to calculate the solution. Based on predicted demand, the initial repositioning plan is made by AGA statically at the beginning of study horizon, which ensures the global optimization of the first solution. As time goes on, repositioning plan is checked and updated according to the real-usage patterns using GTS algorithm, which has the advantage of high-performance local-search within a short computing time. Numerical analysis is conducted using the real cases. The simulation results reveal that the proposed methodology can effectively model the dynamic repositioning problem in response to real-time bicycle-sharing usage. The proposed methodology can be a value-added tool in enhancing the feasibility and sustainability of bicycle-sharing program.

Agent-based modelling of the spatial pattern of leisure visitation in forests: A case study in Wallonia, south Belgium

The leisure use of forests is a fast growing sector in European tourism. The difficulties in monitoring real usage pattern have hindered communications in tourism, conservation and public health in forested areas. In this study, we present an agent-based model to approximate forest visit patterns at the regional level, based on the visitor, residential and forest attributes. The model was then adapted locally for Wallonia, south Belgium, to predict the daily distribution of forest visits and visitor flows. The results suggest high visit rates in urban forests. In highly attractive forests that are distant from major cities, the visit rates may greatly respond to the visitor-level and environmental changes. Future empirical investigations are encouraged to build a cross-contextual understanding on visitors' decision-making mechanisms and to identify how these mechanisms may be influenced by environmental factors operating at different spatio-temporal levels.

Reducing Energy Waste through Eco-Aware Everyday Things

Society wastes much more energy than it should. This produces tons of unnecessary CO2emissions. This is partly due to the inadequate use of electrical devices given the intangible and invisible nature of energy. This misuse of devices and energy unawareness is particularly relevant in public spaces (offices, schools, hospitals and so on), where people use electrical appliances, but they do not directly pay the invoice to energy providers. Embedding intelligence within public, shared appliances, transforming them into Eco-aware things, is valuable to reduce a proportion of the unnecessarily consumed energy. To this end, we present a twofold approach for better energy efficiency in public spaces: (1) informing persuasively to concerned users about the misuse of electronic appliances; (2) Customizing the operating mode of this everyday electrical appliances as a function of their real usage pattern. To back this approach, a capsule-based coffee machine placed in a research laboratory has been augmented. This device is able to continuously collect its usage pattern to offer feedback to coffee consumers about the energy wasting and also, to intelligently adapt its operation to reduce wasted energy. To this aim, several machine learning approaches are compared and evaluated to forecast the next-day device usage.

LiveLabs

We believe that, for successful adoption of novel mobile technologies and applications, it is important to be able to test them under real usage patterns, and with real users. To implement this vision, we present our initial effort in building LiveLabs, a large-scale mobile testbed for in-situ experimentation. LiveLabs is unique in two aspects. First, LiveLabs operates on a scale much larger than most research testbeds it is being deployed in four different public spaces in Singapore (a university campus, a shopping mall, an airport and a leisure resort), and is expected to have a pool of over 30,000 opt-in participants. Second, LiveLabs not only instruments smartphones and the infrastructure to gather deep individual and collective context, but also provides a unique experimentation platform that automates many aspects of behavioral experimentation, such as subject selection and context-triggered delivery of interventions. We briefly describe some of the research challenges associated with building such a large-scale deep-context collection testbed, as well as the current status of LiveLabs. We then share our perspectives on the challenges of setting up and operating such testbeds, with the expectation that our experiences will prove useful to other researchers looking to build similar testbeds elsewhere.

Toward Universal Design in Public Transportation Systems: An Analysis of Low‐Floor Bus Passenger Behavior with Video Observations

AbstractThis study aims to investigate the relationship between ergonomic comfort and the provision of accessibility features for mobility‐impaired persons in the use of public transportation systems in Korea. To deal with mobility issues in a public transportation environment, we chose the low‐floor bus as our research subject because it is the representative of a barrier‐free design solution. We collected data via video observation, which can overcome the limitations of traditional accessibility‐focused studies and laboratory studies by providing data on real usage patterns. We developed a framework to analyze the gathered data which includes user, space, tools, activities, and context. As a result, we observed existing difficulties of mobility‐impaired persons in terms of moving and supporting their bodies on the bus, despite the application of accessibility features. Furthermore, the design only for accessibility of mobility‐disabled persons can cause unexpected predicaments for the passengers who do not have physical handicaps. Drawing on these findings, we concluded that the concept of universal design in public transportation environments must be introduced in Korea. This study suggests video observation as a useful methodology for collecting data in dynamic environments. Additionally, our study is expected to contribute to how the concept of universal design can be implemented, and stimulate issues for ergonomic research based on our behavior pattern analysis.