High-frequency forecasting from mobile devices' bigdata: An application to tourism destinations' crowdedness

Purpose: This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution;Second, this paper elaborates on those estimates' usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons. Design/methodology/approach: This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models. Findings: The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naive I model). Practical implications: A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors' experience and tourism stakeholders' decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures. Originality/value: High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week. Plain Language Summary: This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors' experience and public and private decision-making. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Encaged and Enraged: A Study of How Level of Aggression Relates to Perceived Crowdedness, Risk, and Boredom

Social distancing policies have been practiced in different regions around the world to minimize the number of cases of COVID-19. After an outbreak in mid-July 2020, the Hong Kong government adopted a series of adminis-trative measures and strongly encouraged residents to stay at home. This lockdown period provided an oppor-tunity to study variations in levels of aggression when people spend more time than usual in an overcrowded liv-ing environment. A total of 185 Hong Kong residents were recruited for this study. Their perceptions of the crowdedness of their living space, aggression level (measured using the BPAQ-SF), proneness to boredom (meas-ured by the BFS-SF), and perceptions of risk regarding COVID-19 were collected via online questionnaires. Perceived crowdedness, proneness to boredom, and perceptions of susceptibility to COVID-19 were found to signi-ficantly predict the variance of different types of aggression in a regression model. In a mediation analysis, anger acted as a mediator of the relationship between proneness to boredom and different types of aggression. Parti-cipants' perceptions of their susceptibility to COVID-19 suggested an underlying worry about the contagious-ness of the virus, which was in turn associated with feelings of uncertainty and a rise in aggression level. © 2023, Universitaet Bielefeld. All rights reserved.

Bus Crowdedness Sensing System Based on Carbon Dioxide Concentration

Crowdedness sensing of buses is playing an important role in the disease control of COVID-19 and bus resource scheduling. This research analyzes the relationship between carbon dioxide concentration, bus environment and the number of passengers by linear regression. Our prototype system collects the data of bus environment and carbon dioxide concentration to estimate the number of passengers in real time. By collecting the sensing data from a shuttle bus of university campus, we experimentally evaluate the feasibility and sensing performance of the crowdedness estimation model. © 2022 Owner/Author.

A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection

Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learningbased object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform. © 2022 The Institute of Electronics, Information and Communication Engineers.

Relationships between Green Space Attendance, Perceived Crowdedness, Perceived Beauty and Prosocial Behavior in Time of Health Crisis.

An emergent body of evidence shows the impact of exposure to nature on prosocial attitudes and interpersonal relationships. This study examines relationships between green space (GS) attendance, perceived beauty of the space, perceived crowdedness of the space, and prosocial behavior. A cross-sectional study with snowball sampling was conducted in April 2020. All participants (N = 1206) responded to an online survey that included a French version of the social value orientation slider measure (used as a proxy for prosocial behavior), questions about the lockdown, and their GS attendance. After retaining only participants who had visited a GS at least once since the beginning of their lockdown (N = 610), multiple linear regressions showed that social orientation scores demonstrated associations with the interaction between GS attendance and perceived crowdedness of the GS, suggesting that attending low crowded GS is linked to increasing prosociality. These results provide insight into the roles that GS can have during a health crisis and suggest some practical implications.

The Influence of Social Crowdedness in Retail Stores on Consumers’ Shopping Satisfaction in the COVID-19 Era: A Focus on the Moderating Effects of Happiness and Time Perspective

Purpose – The construct of social crowdedness has been debated whether it impacts consumer shopping satisfaction positively or negatively. However, as the COVID-19 has prolonged, people fear places full of people due to possible contamination and so they avoid such areas. Therefore, this study expects that social crowded-ness has a negative effect. However, this study also predicts that consumers’ individual values could moderate the negative effect of social crowdedness. Design/Methodology/Approach – This study recruited an experimental study design for the research purpose above. Through two experiments, this study examined the effect of social crowdedness on shopping satisfaction and the moderating effect of happiness perspective (study 1) and time perspective (study 2). Findings – The study found that people in a high social crowdedness condition showed lower shopping satis-faction. Furthermore, this negative reaction in a high social crowdedness condition was moderated depending on their dominant perspective. Research Implications – Those with calm happiness and present-fatalistic perspective showed even greater shopping satisfaction in low social crowdedness conditions. In contrast, those with exciting happiness and present-hedonic time perspectives showed comparably higher satisfaction in a high crowdedness condition. The results are expected to provide critical strategic implications to retail managers. © 2022 International Academy of Global Business and Trade. All rights reserved.

Emergency department crowdedness and emergency department cardiac arrest occurrence: an observational study in the COVID-19 pandemic

Emergency department (ED) crowdedness is a global phenomenon that can lead to many adverse effects. The relationship of crowdedness and emergency department cardiac arrest (EDCA) occurrence is still debated. The COVID-19 pandemic precipitated a change in the patient volume of the ED and the crowdedness of the ED varied with the epidemic in a continuous period. Different degrees of crowdedness provided us with an opportunity to study the relationship between crowdedness and EDCA occurrence. Our aim of this study was to determine the relationship between EDCA occurrence and prognosis and ED crowdedness.This was a longitudinal study conducted in a tertiary teaching hospital. The study period was from October 1, 2019, to September 30, 2020, and was divided into three periods according to daily patient volume and crowdedness. All nontraumatic and adult EDCA patients during the study period were included, and out-of-hospital cardiac arrest (OHCA) patients and patients with do-not-resuscitate orders were excluded.During the study period, a total of 126 EDCA patients were included. The ratio of EDCA events to daily patient volume was compared among these 3 periods, and there was no significant difference (P2: p = 0.109, P3: p = 0.761, P1 as reference). No significant difference in the prognosis of EDCA patients was found among the 3 periods, regardless of the return of spontaneous circulation (ROSC) (p = 0.437) or survival rates (p = 0.838). In conclusion, there was no obvious correlation between ED crowdedness and EDCA occurrence. The prognosis of EDCA patients was not significantly associated with crowdedness. The metrics of ED overcrowding is unknown and may need further study to develop a generally accepted standard or index. [ FROM AUTHOR] Copyright of Signa Vitae is the property of Pharmamed Mado Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Risk Status of Waiting Areas for Airborne Infection Control in Delhi Hospitals.

Background Hospital waiting areas are overlooked from the airborne infection control viewpoint as they are not classified as critical for infection control. This is the area where undiagnosed and potentially infected patients gather with susceptible and vulnerable patients, and there is no mechanism to segregate the two, especially when the potentially infected visitors/patients themselves are unaware of the infection or may be asymptomatic. It is important to know whether hospitals in Delhi, a populated, low-resource setting having community transmission/occurrence of airborne diseases such as tuberculosis, consider waiting areas as critical. Hence, this study aims to determine whether hospitals in Delhi consider waiting areas as critical areas from the airborne infection control viewpoint. Methodology The Right to Information Act, 2005, was used to request information from 11 hospitals included in this study. Results After compiling the results, it was found that five out of the 11 hospitals did not consider waiting areas as critical from the infection spread point of view. Two of the 11 hospitals acknowledged the criticality of waiting areas but did not include the same in the list of critical areas. Only three out of the 11 considered waiting areas as critical and included these in the list of critical areas in a hospital. Conclusions This study provided evidence that most hospitals in Delhi do not include waiting areas in the list of critical areas in a hospital.

The effects of crowdedness and safety measures on restaurant patronage choices and perceptions in the COVID-19 pandemic.

This paper investigates the effects of crowdedness and in-restaurant safety measures on consumers' restaurant patronage choices (eat-in vs. order takeaway vs. not patronize) and their perceptions during the COVID-19 pandemic. In an online experiment with 593 US consumers and 591 Australian consumers, we assess the effects of three levels of crowdedness (low vs. medium vs. high crowdedness) and four types of in-restaurant safety measures (none vs. partition vs. increasing distances between tables vs. not using in-between tables) by showing participants an image of the restaurant setting. Results show that US consumers are more sensitive to crowdedness, whereas Australian consumers are more sensitive to different types of safety measures, which greatly influence their patronage choices. In general, safety measures featuring social distancing are preferred over partitions, and there is no preferential difference between the measure of increasing distances between tables and the measure of not using in-between tables.