Daily Arrivals Research Articles

“Ship-port-country” multi-dimensional research on the fine analysis of China's LNG trade

Liquefied Natural Gas (LNG) is a clean and low-carbon energy source that plays an important role in combating global warming. As the world's largest greenhouse gas (GHG) emitter, China has taken coal-to-gas as an important measure to reduce GHG emissions, and has become the world's largest LNG importer. Studying the patterns and characteristics of China's LNG trade is of great significance for ensuring energy supply security, formulating and adjusting energy strategies. However, the refined analysis of China's LNG trade is still inadequate, mainly due to the lack of research on the details of ship trajectories and port attributes. Therefore, a “ship-port-country” multidimensional LNG trade analysis method is proposed to improve the timeliness, multi-dimensionality and fineness of China's LNG trade analysis. The multidimensional characteristics of China's LNG trade are analyzed by studying the big data of nearly 3.5 billion automatic identification system (AIS) records in 2017. The study has found that (1) The arrival frequency of China's LNG trading ships varies greatly with time. For example, the most frequent daily arrivals are between 04:00 and 05:00 in the morning, while the least frequent arrivals are between 15:00 and 17:00 in the afternoon. (2) The Chinese LNG trade network consisting of 79 ports has been formed, of which NANTONG Port is the most important port. (3) The LNG trade volume calculated in this study has shown a strong correlation with the national LNG trade statistics and can be used to analyze LNG trade with higher spatial and temporal resolution. According to the characteristics of LNG trade at the three levels of ship, port and country, China can optimize its LNG trade policy from the following aspects: (1) Dynamically allocate port resources according to the arrival frequency of LNG tankers to reduce port congestion and waste of resources. (2) Expand core LNG trading ports such as NANTONG Port, ZHOUSHAN Port, TANGSHAN (JINGTANG) Port, XIUYU Port, DALIAN Port and LUSHUN Port. (3) Actively realize the diversification of LNG trading partners to reduce the dependence of LNG import sources on one country or region, and ensure energy security. The “ship-port-country” multi-level LNG trade analysis method proposed in this study provides a new research perspective for the analysis of the LNG trade pattern. This study reveals the multidimensional characteristics of China's LNG trade, which can provide decision-making support for China to formulate reasonable LNG trade policies and energy security policies, and is conducive to the stable and sustainable development of global LNG trade.

Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach

Background and objectiveEmergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown.MethodsWe extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric.ResultsCalendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant.ConclusionsOur study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables.

Modelling and Predicting Daily Arrival of Patients at the Accident and Emergency Department of the University of Cape Coast Hospital, Ghana

Background: Modelling and forecasting demand for future emergency healthcare services is increasingly gaining wide attention in the emergency healthcare industry worldwide. This aids hospital managers in looking into various options to appropriately plan and allocate available scarce resources for optimal and swift performance. Despite its importance, our knowledge of daily patient flow into the Accident and Emergency Department (AED) of the University of Cape Coast Hospital is incomprehensive, and even the model that best explains its movements remains unknown. Methods: Using daily periodicity of 517 time-series observations on daily patient arrivals sourced from the AED register over January 2020 through May 2021 the autoregressive integrated moving average (ARIMA) of the classical Box-Jenkins methods of time series analysis was used to analyse the data. Results: This study revealed twenty-five non-seasonal candidate models for the hospital AED and ARIMA (0, 1, 2) emerged as the best fitting model. The study results showed that the daily patient arrivals at the AED section of the University Hospital witnessed a 50% decline on average within the study period and a further 33% decline in the forecast region. The findings also revealed very high volatility in daily patient arrivals with an average of eight patient arrivals per day. Conclusion Non-seasonal ARIMA (0, 1, 2) was identified as the best model. Thus, for policy, intervention, and future research direction, it is recommended that steps are taken to investigate the highly volatile nature of patient arrivals as well as the steady downward trending of the daily patient flow at the Accident and Emergency Department of the University Hospital

Monitoring and forecasting COVID-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries

ABSTRACT The COVID-19 pandemic is greatly affecting the hospitality industry worldwide. Lodging demand is severely reduced as people's fear of coronavirus spreading risk in hotels. This research makes a timely contribution to the hospitality literature by proposing the mixed data sampling models (MIDAS) to monitor and forecast latest hotel occupancy rates with high-frequency big data sources, such as daily visitor arrivals and search query data. Quantitative evidence from Macau from January to July 2020 confirms that MIDAS models can measure the dynamic impacts of the COVID-19 pandemic on hotel occupancy and have a better prediction accuracy and explanation ability than competitive models. Industry practitioners can adopt this big data analytical framework to make daily or monthly updates of lodging demand, conduct scenario analysis, plan and trace the recovery schedule during and post COVID-19 phases. Finally, managerial implications and future work are highlighted.

The potential of a short tourist route on the example of a single street: tourist attractiveness of urban spaces

The subject of this research is tourist attractiveness of the walking rout of a section of a single street in Samara. The object of this research is such characteristics of the route as visual attractiveness, convenience and safety, atmosphere, and uniqueness. This article provides the analysis of urban space and environment on the example of a specific street in the city of Samara. The selected section directly connects the railway station, which is the place of daily arrival and transit tourists, and the Volga promenade – as one of the main symbols of the city and the center of attraction for locals and visitors. The goal of this research consists in the analysis of the existing conditions for the tourist route, which can be improved and extended for increasing the overall assessment of tourism infrastructure. The empirical framework of this research synthesizes the approaches towards interpretation of the psychogeography of the city and towards the analytics of individual experience in the urban environment. The field notes and information analysis allow examining the potential of a tourist route in this section, as well as making recommendations for increasing tourist attractiveness and development of tourism infrastructure of the city. Such pattern can be applied in the research of large and small cities for assessing the current situation and efficient planning of tourism infrastructure in the future. The author formulates the categories of tourist attractiveness that reflect physical, psychological and emotional characteristics of feasibility of urban environment for local tourism. The scientific novelty of this work consists in the development of categories for the analysis based on the key approaches of Paul Kidwell and Colin Ellard towards studying psychogeography of the city and architecture of the urban environment.

The prices of perishable food commodities in India: the impact of the lockdown

Onions, potatoes, and tomatoes constitute an important component of the Indian diet. The COVID-19 pandemic in 2020 forced the government to impose a lockdown from 25 March to 31 May. This paper uses granular data to assess the impact of the lockdown on the daily arrivals and wholesale prices of these commodities at three metropolitan markets. The impact was significant and negative on the quantity traded, and positive on prices, but the heterogeneity across commodities and markets was considerable.

Self-evolutionary sibling models to forecast railway arrivals using reservation data

Accurate forecasts of daily arrivals are of essential to allocate seat resources for transportation companies. Most studies addressed the issue from conventional time series aspects to retrieve historical arrival patterns and project future numbers. This study aims to utilize railway reservation records instead of arrival data to construct self-evolutionary advanced booking models and compare with three benchmarks. In addition, the proposed model involved the spirit of one prototype with multiple versions to pursue accuracy improvement. A family of eight sibling versions based on the curve similarity model, differentiating from the evaluation of similarities among booking curves, was established. The results showed that the constructed sibling versions perform differently with respect to individual data series. In other words, the way of similarity evaluation did affect the predictive performance. Although there was no single version outperforming the others, the selection based on the lowest validation errors was verified to be a good strategy to attain promising out-of-sample performance. Overall speaking, maintaining the family of sibling models for booking data with distinctive characteristics can achieve at least 4.5% and at most 23% improvement of accuracy if comparing with one specific version to all data series. In addition, the proposed sibling models can also outperform popular advanced booking benchmarks such as pick up, regression, and conventional curve similarity approach up to 36%, 32%, and 35%, respectively.

Forecasting Tourist Daily Arrivals With A Hybrid Sarima–Lstm Approach

Timely predicting tourist demand is extremely important for the tourism industry. However, due to limited availability of data, most of the relevant research studies have focused on data on a quarterly or monthly basis. In this article, we propose a novel hybrid approach, SARIMA + LSTM, that is, seasonal autoregressive integrated moving average (SARIMA) combined with long short-term memory (LSTM) to forecast daily tourist arrivals to Macau SAR, China. The LSTM model is a novel artificial intelligence nonlinear method which has been shown to have the capacity to learn the long-term dependencies existing in the time series. SARIMA + LSTM benefits from the predictive power of the SARIMA model and the ability of the LSTM to further reduce residuals. The results show that the SARIMA + LSTM forecast technique outperforms other methods.

The (Re)Allocation of Migrants during the Great Lockdown

This paper presents first-hand evidence of the impact of COVID-19 on the allocation of migrants. I use monthly data on the migrants in reception centres and on daily arrivals in Italy during the period from October 2017 to April 2020 combined with information on COVID-19-related-cases across Italian regions. I employ a difference-in-differences design, finding that the presence of migrants decreased approximately 1 percentage point more in regions highly exposed to the pandemic as compared to those that are less affected by COVID-19. Suggestive evidence also indicates that a larger presence of migrants is associated with high level of expenditure on security and migration, whilst no correlation is found between migration and crime. Taken together, these findings suggest that the unusual allocation of migrants due to the pandemic risks draining resources from functions that are key for the recovery phase to other, less strategic ones, such as security and migration.

Factors and outcomes associated with paediatric emergency department arrival patterns through the day.

Steadily increasing emergency department (ED) utilization has prompted efforts to increase resource allocation to meet demand. Little is known about the distribution and characteristics of patient arrivals by time of day. This study describes the variability and patterns of ED resource utilization related to patient, acuity, clinical, and disposition characteristics over a 24-hour period. Retrospective cross-sectional study of all visits to a tertiary children's hospital over a 1-year period. We use descriptive statistics to present ED visit details stratified by shift of arrival, and multivariable regression to explore the association between shift of presentation and hospital admission at index and 7-day return ED visits. Of 46,942 visits during the study period, 12% arrived overnight, 42% during the day, and 45% during the evening with variability in pattern of shift arrival by day of week. Overnight arrivals had a higher acuity (Canadian Triage and Acuity Scale [CTAS]) and different presenting complaints (more viral infection, less minor trauma) than day and evening arrivals, but similar ED length of stay. Shift of arrival was not associated with admission to hospital, but age, gender, socioeconomic status (SES), and day of week were. ED utilization patterns vary by shift of arrival. Though overnight arrivals represent a smaller proportion of total daily arrivals, their acuity is higher, and the spectrum of disease differs from day or evening arrivals. Understanding variations and patterns of ED utilization by shift of arrival and day of week may be helpful in tailoring resource allocation to more accurately and specifically meet demands.

Working with traumatized refugees on the Balkan route

AbstractIn the past several years Serbia has been in the center of the so‐called “Balkan route”, a pathway for refugees from the Middle East and Central Asia. In March 2016 the Balkan route was closed and this measure reduced the number of daily arrivals and prolonged the stay of refugees in Serbia. A brief overview of the current situation is given, together with some previous experiences in psychosocial programs for the displaced population during wars in former Yugoslavia and current psychosocial programs and interventions. Several short examples are presented to point out some aspects of those experiences that might have relevance to our understanding of the psychological trauma and possible ways of treatment.

Da Brindisi a Lampedusa: le migrazioni via mare nella cassa di risonanza della <i>popular music</i> italiana

From the landing of thousands of Albanese people on the Apulian coast in 1991 to the daily arrival of refugees on the Calabrian and Sicilian coasts since 2000, immigration by sea and the lifestories of the countless victims of this situation have become part of the ‘public awareness’ in Italy. To narrate the tragedies we are witnessing day by day, different communicational strategies have been adopted, depending on the respective media and on the political objectives pursued by individual ‘speakers’. The attention writers, film directors, and artists have payed to the recent migration movements towards Italy has gradually given a more profoundly human dimension to the phenomenon and has brought the public closer to the individual tragedy. Italian popular music in particular has actively contributed to establishing an artistic counter-discourse, which will be discussed in the present article. Examples of different musical genres will be analysed, covering the time frame in question, whereas the methodology pursued will be that of Imagology and that of Cultural Studies.

Data-driven analytics to support scheduling of multi-priority multi-class patients with wait time targets

Magnetic Resonance Image (MRI) uses powerful magnetic forces and radio frequencies to create detailed images of the organs and tissues within the body. In this paper, we first conduct descriptive analytics on MRI data of over 3.7 million patient records and determine the main factors affecting the waiting time and conduct predictive analytics to forecast the daily arrivals and the number of procedures performed at each hospital. It is the hospital’s goal to serve 90% of patients within their wait time targets. Therefore, we prescribe two simple scheduling policies based on a balance between the FIFO (First-In First-Out) and strict priority policies; namely, weight accumulation and priority promotion to improve the wait time management. Under the weight accumulation policy, patients from different priority levels start with varying initial weights, which then accumulates as a linear function of their waiting time. Under the priority promotion policy, a strict priority policy is applied to priority levels where patients are promoted to a higher priority level after waiting for a predetermined threshold of time. We evaluate the proposed policies against two performance measures: total exceeding time (the number of days by which patients exceed their wait target), and overflow proportion (the percentage of patients that exceed the wait target). To investigate the value of information, we schedule patients at different points of time from their day of arrival. The results show that hospitals can enhance their wait time management by delaying patient scheduling. We demonstrate that effective scheduling policies may result in significant reduction in patient waiting time without any costly capacity expansion.

Control and Abandonment: The Power of Surveillance on Refugees in Italy, During and After the Mare Nostrum Operation

AbstractMigrants' daily arrivals to Italy's southern coasts and continuous shipwrecks in the Mediterranean have captured international media attention, producing a fixation on the scene of landing and a deliberate marginalization of what happens to migrants and refugees after the moment of landing. This paper aims to refocus analytical attention on the lives of asylum seekers after landing in Europe, breaking through the institutional silence that is cast upon the infrastructure of the camp, the logic of assistance and the bureaucratic waiting zone asylum seekers are stuck in. By documenting political changes in European and national policies, the paper reflects on the forms of institutional control and abandonment refugees are subjected to once they land in Italy, and are housed in the governmental camps and extraordinary structures which arose at the time of the Mare Nostrum Operation where strict discipline, carelessness, uncertainty and confusion intertwine.

The Analytics of Bed Shortages: Coherent Metric, Prediction and Optimization

Bed shortages in hospitals usually have a negative impact on patient satisfaction and medical outcomes. In practice, healthcare managers often use bed occupancy rates (BOR) as a metric to understand bed utilization, which is insufficient in capturing the risk of bed shortages. We propose the bed shortage index (BSI) to capture more facets of bed shortage risk than traditional metrics such as the occupancy rate, the probability of shortages and expected shortages. The BSI is based on the well-known Aumann and Serrano (2008) riskiness index and it is calibrated to coincide with BOR when the daily arrivals in the hospital unit are Poisson distributed. Our metric can be tractably computed and does not require additional assumptions or approximations. As such, it can be consistently used across the descriptive, predictive and prescriptive analytical approaches. We also propose optimization models to plan for bed capacity via this metric. These models can be efficiently solved on a large scale via a sequence of linear optimization problems. The first maximizes total elective throughput while managing the metric under a specified threshold. The second determines the optimal scheduling policy by lexicographically minimizing the steady-state daily BSI for a given number of scheduled admissions. We validate these models using real data from a hospital and test them against data-driven simulations. We apply these models to study the real-world problem of long stayers, to predict the impact of transferring them to community hospitals, as a result of an aging population.

Modeling the critical care pathway for cardiothoracic surgery

The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increased demand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue was limited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type’s pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this technique with minor capacity reallocations resulted in more than 60% drop in cancellations.

A Two-Time-Scale Approach to Time-Varying Queues in Hospital Inpatient Flow Management

We analyze a time-varying Mperi/Geo2timeScale/N queueing system. The arrival process is periodic Poisson. The service time of a customer has components in different time scales: length of stay (LOS) in days and departure time (hdis) in hours. This queueing system has been used to study patient flows from the emergency department (ED) to hospital inpatient wards. In that setting, the LOS of a patient is simply the number of days she spends in a ward, and her departure time hdis is the discharge hour on the day of her discharge. We develop a new analytical framework that can perform exact analysis on this novel queueing system. This framework has two steps: first analyze the midnight customer count process and obtain its stationary distribution, then analyze the time-dependent customer count process to compute various performance measures. We also develop approximation tools that can significantly reduce the computational time. In particular, via Stein’s method, we derive explicit expressions to approximate the stationary distribution of the midnight count. We provide error bounds for these approximations and numerically demonstrate that they are remarkably accurate for systems with various sizes and load conditions. Our theoretical and numerical analysis have produced a number of insights that can be used to improve hospital inpatient flow management. We find that the LOS term affects the overnight wait caused by the mismatch between daily arrivals and discharges, whereas the hdis term affects the intraday wait caused by the nonsynchronization between the arrival and discharge time patterns. Thus, reducing LOS or increasing capacity can impact the daily average performance significantly; shifting the discharge timing to earlier times of a day can alleviate the peak congestion in the morning and mainly affects the time-dependent performance. The e-companion is available at https://doi.org/10.1287/opre.2016.1566 .

Optimization of hospital ward resources with patient relocation using Markov chain modeling

Overcrowding of hospital wards is a well-known and often revisited problem in the literature, yet it appears in many different variations. In this study, we present a mathematical model to solve the problem of ensuring sufficient beds to hospital wards by re-distributing beds that are already available to the hospital. Patient flow is modeled using a homogeneous continuous-time Markov chain and optimization is conducted using a local search heuristic. Our model accounts for patient relocation, which has not been done analytically in literature with similar scope. The study objective is to ensure that patient occupancy is reflected by our Markov chain model, and that a local optimum can be derived within a reasonable runtime.Using a Danish hospital as our case study, the Markov chain model is statistically found to reflect occupancy of hospital beds by patients as a function of how hospital beds are distributed. Furthermore, our heuristic is found to efficiently derive the optimal solution. Applying our model to the hospital case, we found that relocation of daily arrivals can be reduced by 11.7% by re-distributing beds that are already available to the hospital.

Young children in day and night care: negotiating and constructing belonging during daily arrivals

ABSTRACTThe paper aims at understanding the processes related to young children's belonging during daily arrivals at day and night care. Two aspects of a child's belonging are considered: membership and sense of belonging. Data were gathered by ethnographic observation of 8 children aged from 20 to 36 months in two Finnish day care centres offering day and night care. Arrival episodes taking place at different times of day were analysed qualitatively based on the children's actions and expressions. During these episodes, the children negotiated and constructed their belonging by interacting with adults, peers and material objects. These interactions took place within varying social and material surroundings and according to different daily routines that sometimes challenged the child's belonging. The results underline the importance of sensitive responsiveness by educators to young children's initiatives in supporting children's belonging during daily arrivals at day and night care.

An Analysis of the Relationship Between the Heat Index and Arrivals in the Emergency Department.

Background: Heatwaves are one of the most deadly weather-related events in the United States and account for more deaths annually than hurricanes, tornadoes, floods, and earthquakes combined. However, there are few statistically rigorous studies of the effect of heatwaves on emergency department (ED) arrivals. A better understanding of this relationship can help hospitals plan better and provide better care for patients during these types of events.Methods: A retrospective review of all ED patient arrivals that occurred from April 15 through August 15 for the years 2008 through 2013 was performed. Daily patient arrival data were combined with weather data (temperature and humidity) to examine the potential relationships between the heat index and ED arrivals as well as the length of time patients spend in the ED using generalized additive models. In particular the effect the 2012 heat wave that swept across the United States, and which was hypothesized to increase arrivals was examined.Results: While there was no relationship found between the heat index and arrivals on a single day, a non-linear relationship was found between the mean three-day heat index and the number of daily arrivals. As the mean three-day heat index initially increased, the number of arrivals significantly declined. However, as the heat index continued to increase, the number of arrivals increased. It was estimated that there was approximately a 2% increase in arrivals when the mean heat index for three days approached 100°F. This relationship was strongest for adults aged 18-64, as well as for patients arriving with lower acuity. Additionally, a positive relationship was noted between the mean three-day heat index and the length of stay (LOS) for patients in the ED, but no relationship was found for the time from which a patient was first seen to when a disposition decision was made. No significant relationship was found for the effect of the 2012 heat wave on ED arrivals, though it did have an effect on patient LOS.Conclusion: A single hot day has only a limited effect on ED arrivals, but continued hot weather has a cumulative effect. When the heat index is high (~90°F) for a number of days in a row, this curtails peoples activities, but if the heat index is very hot (~100°F) this likely results in an exacerbation of underlying conditions as well as heat-related events that drives an increase in ED arrivals. Periods of high heat also affects the length of stay of patients either by complicating care or by making it more difficult to discharge patients.