Crowd Levels Research Articles

Exploring the Tourist Perceptions of Destination Components at the Sigiriya World Heritage Site: A Tripadvisor-Based Study

ABSTRACT This study explores tourists’ perceptions of the Sigiriya UNESCO World Heritage Site in Sri Lanka. The study has used TripAdvisor reviews and analysed using content analysis employing the 5’A’s framework for destinations (Attractions, Accessibility, Amenities, Activities, Accommodation). The results indicate that tourists perceive the attraction based on scenery, historical significance, features, cost, and crowd levels. Their perception highlighted the availability of accommodation, facilities and amenities. The activities including rock climbing, garden visits, photography, and artefact observation along with the accessibility considerations, such as fitness requirements, were also highlighted in their perceptions. The study revealed a key finding that ‘authentic experience’ is a crucial component of heritage tourism. Accordingly, this study proposes expanding 5’A’s framework incorporating ‘authentic experiences’ to understand tourists’ perceptions and their recommendation behaviour. Practical implications include preserving the site’s natural and historical value, improving accessibility and amenities, and offering authentic experiences in heritage destinations to enhance tourists’ perceptions

Management Policy in Urban Rail Transit System: Trade-Off between Social Distancing and Service Efficiency Using Simulation in the Post-Epidemic Era

The past COVID-19 pandemic introduced the world to the necessity of dealing with the trade-off between minimizing probability of contagion, and providing people with services they need. This trade-off stipulates that a large person-to-person distance will reduce contagion probability, but will render service inefficient, and vice versa. This work focuses on the urban rail transit (URT) hub, as an example of a busy passenger area, from which we can derive an optimal preparedness policy to use during the pandemic time of any coronaviruses. We use simulation methodology, based on the classical social force model, to represent behaviors and characteristics of pedestrians. Passenger flow movement process is a mechanism we explore to figure out how the epidemic management policy and pedestrian psychological-related behaviors interact with the URT system. The systems’ complexity regarding contagion-prevention distances are tested over a few scenarios: before/after the outbreak, and for different person-to-person distances demonstrating different crowd levels. A case study of Xinjiekou Station, Nanjing URT, China, enables assessment of passenger management policy with person-to-person distances of 0.5 m, 1.0 m and 2.0 m. Multi-scenario performance illustrates the trade-off in dynamic between the efficiency of pedestrians’ walking behaviors and the distancing needs for preventing coronaviruses transmission. The results show that queuing length with social distancing of 1.0 m and 2.0 m is increased by 4.17% and 21.22%. The average delays in boarding are 14.1 s and 22.5 s for 1.0 m and 2.0 m, which leads to 15.29% and 22.39% increases, respectively, in comparison with ordinary social distancing of about 0.5 m.

Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands.

After the outbreak of COVID-19 pandemic, crowding has been highlighted as a risk factor for contracting acute respiratory infections (ARIs) such as COVID-19, which has affected the demand for public transport. Although several countries, including the Netherlands, have implemented differential fare systems for peak and off-peak travel to reduce crowding during the rush hours, the problem of overcrowding on trains has remained prevalent and is expected to cause more disutility than even before the pandemic. A stated choice experiment in the Netherlands is conducted to understand the extent to which people can be motivated to change their departure time to avoid crowded trains during rush hours by offering them real-time information on on-board crowding levels and a discount on the train fare. To gain further insights into how travelers respond to crowding and capture unobserved heterogeneity in the data, latent class models have been estimated. Unlike the previous studies, the respondents were segregated into two groups before the start of the choice experiment based on their indicated preference to schedule a delay earlier or later than their desired departure. To study the change in travel behavior during the pandemic, the context of different vaccination stages was also provided in the choice experiment. Background information collected in the experiment was broadly categorized as socio-demographic, travel and work-related factors, and attitudes towards health and COVID-19. It was found that the coefficients obtained for the main attributes which were presented in the choice experiment (on-board crowd levels, scheduled delay and discount offered on full fare) were found statistically significant, and in line with previous research. It was concluded that when most of the people are vaccinated in the Netherlands, the travelers become less averse to on-board crowding. The research also indicates that certain groups of respondents, such as those who are highly crowd averse, and are not students, can be motivated to change their departure time if real-time crowding information was provided. Other groups of respondents who were found to value fare discounts can also be motivated to change their departure by similar incentives.

Eliciting Confidence for Improving Crowdsourced Audio Annotations

In this work we explore confidence elicitation methods for crowdsourcing "soft" labels, e.g., probability estimates, to reduce the annotation costs for domains with ambiguous data. Machine learning research has shown that such "soft" labels are more informative and can reduce the data requirements when training supervised machine learning models. By reducing the number of required labels, we can reduce the costs of slow annotation processes such as audio annotation. In our experiments we evaluated three confidence elicitation methods: 1) "No Confidence" elicitation, 2) "Simple Confidence" elicitation, and 3) "Betting" mechanism for confidence elicitation, at both individual (i.e., per participant) and aggregate (i.e., crowd) levels. In addition, we evaluated the interaction between confidence elicitation methods, annotation types (binary, probability, and z-score derived probability), and "soft" versus "hard" (i.e., binarized) aggregate labels. Our results show that both confidence elicitation mechanisms result in higher annotation quality than the "No Confidence" mechanism for binary annotations at both participant and recording levels. In addition, when aggregating labels at the recording level, results indicate that we can achieve comparable results to those with 10-participant aggregate annotations using fewer annotators if we aggregate "soft" labels instead of "hard" labels. These results suggest that for binary audio annotation using a confidence elicitation mechanism and aggregating continuous labels we can obtain higher annotation quality, more informative labels, with quality differences more pronounced with fewer participants. Finally, we propose a way of integrating these confidence elicitation methods into a two-stage, multi-label annotation pipeline.

Utilization of E-WoM strategy and WhatsApp group texting in building trust and shopper intention to conventional business sectors during the coronavirus pandemic

Indonesia is a country with the largest female population in Asia, 51% of whom are housewives. The consumer behavior of housewives in Indonesia is mostly like shopping at traditional markets, unfortunately, since the COVID-19 outbreak, they cannot shop as often as possible to the market for reasons. safety and temporary closure of markets to reduce crowd levels in traditional markets, as a result of which traders and consumers cannot interact and transact. There are social media activists in Bandung city who try to both traders and consumers of traditional markets by utilizing chat group Apps such as Whatsapp group for promoting and trading traditional market. Opinion leaders are used as positive spreaders of traditional market products, this business is free for traders, payments are made directly to merchants via bank transfers and use of delivery services by couriers who are victims of COVID-19 dismissal, sales traffic is quite high but no one has tested the impact increase in consumer behavior and product trust.

E Word of Mouth and Structural Geography Predicting the Consumer Behavior in Traditional Market During Covid-19 Pandemic

Indonesia is a country with the largest female population in Asia, 51% of whom are housewives. The consumer behavior of housewives in Indonesia is mostly like shopping at traditional markets, unfortunately since the Covid 19 outbreak they cannot shop as often as possible to the market for reasons. safety and temporary closure of markets to reduce crowd levels in traditional markets, as a result of which traders and consumers cannot interact and transact. There are social media activists in Bandung city who try to both traders and consumers of traditional markets by utilizing chat group Apps such as Whatsapp group for promote and trade traditional market. Opinion leaders are used as positive spreaders of traditional market products, this business is free for traders, payments are made directly to merchants via bank transfers and use of delivery services by couriers who are victims of Covid 19 dismissal, sales traffic is quite high but no one has tested the impact increase in consumer behavior and product trust. Additionally, the study has also considered the role of structural geography for the prediction of the consumer behavior. This study aims to answer this problem, with 300 respondents who are consumers in the chat group taken as a sample, the questionnaire is given and processed using PLS, the results show that as many respondents feel their consumer behavior changes shopping to traditional markets and the 75.61% increases trust in traditional market products due to referral from the chat group. The results of the study revealed that the structural geography also influences the consumer behaviors. © 2021 RIGEO. All Rights Reserved.

Crowd management COVID-19

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration into their regular routines reducing the risk of infection. In this context, this paper presents a solution for crowd management. The aim is to monitor and manage crowd levels in interior places or point-of-interests (POI), particularly shopping centers or stores. The solution is based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user. In this sense, it recommends places near the user location with the least estimated crowd. The recommendation algorithm uses a top-K approach and behavioral game theory to predict the user’s choice and estimate the crowd level for the requested POI. To evaluate the efficiency of this technological intervention in terms of the potential number of contacts of possible COVID-19 infections and the recommendation quality, we have developed an agent-based model (ABM). The adoption level of new technologies can be related to the end-user experience and trust in such technologies. As the end-user follows a recommendation that leads to uncrowded places, both the end-user experience and trust increased. We study and model this process using the OCEAN model of personality. The results from the studied scenarios showed that the proposed solution is widely adopted by the agents, as the trust factor increased from 0.5 (initial set value) to 0.76. In terms of crowd level, these are effectively managed and reduced on average by 40%. The mobility contacts were reduced by 40%, decreasing the risk of COVID-19 infection. An APP has been designed to support the described crowd management and contact tracing functionality. This APP is available on GitHub.

Multimodal shortest hyperpaths considering crowding in transit

In rush hours, the onboard crowd level within transit vehicles is a problem of any large city. At the expense of time savings, some users will avoid riding crowded lines if they consider that transit vehicles do not have enough personal space. This paper presents a hypergraph model and an algorithm to find multimodal shortest hyperpaths considering the user constraints on the sequence of boarded modes and their preferences of onboard crowding levels. It is assumed that transit inter-arrivals are random. A penalty for riding transit vehicles is defined to model how the users perceive the onboard crowd levels. This penalty depends on the onboard crowd levels and the seating capacity of the vehicles. A state-automaton is used to model the user constraints on the sequence of boarded modes. To find the shortest hyperpaths, the user selects their origin, destination, the maximum number of modal transfers, and the onboard crowd level threshold.

Mobile Crowdsourcing Technology Acceptance and Engagement in Crisis Management

The purpose of this study is to examine the determinants of the intention for the continuous acceptance and use of mobile crowdsourcing to participate in refugee crisis management. A questionnaire was developed to collect data from 389 Syrian refugees in Jordan's Za'atari camp. Smart PLS was used to analyze the data. The findings indicated that the individual and crowd performance expectancy, the social influence, and perceived risks on the individual and crowd levels have a significant influence on the intention for the continuous acceptance and engagement in mobile crowdsourcing to participate in refugee crisis management. In addition, the results revealed that cultural values of masculinity, power distance, and long-term orientation have no effect on the intention. At the same time, cultural values of collectivism and uncertainty avoidance have a significant effect.

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed.

Monitoring a Crowd's Affective State: Status Quo and Future Outlook

Crowd monitoring plays an integral role in crowd management processes and continues to receive growing interest across different public and commercial service sectors. In recent years, synergistic advances in different domains, such as computing, sensing, IoT, drones, and AI/ML, have empowered monitoring architectures with various enhancements in identifying patterns in crowd dynamics. However, efforts to examine affect in crowd monitoring considerations have been lacking in the current literature. In this work, we address this void by reviewing the recent advances and enabling technologies in affective sensing at both the individual and crowd levels. We also remark on deployment considerations in affective sensing architectures and key outstanding challenges.

Ethics of Public Use of AI and Big Data: The Case of Amsterdam’s Crowdedness Project

Smart information systems (Big Data and artificial intelligence) are used by governments to improve mobility, reduce over-crowdedness in hotspots, and provide more effective management of crowds. I looked at how Amsterdam municipality is using smart information systems (SIS) in their DrukteRadar Project to identify, report, and tackle issues surrounding crowdedness levels in the city.SIS are becoming popular amongst governmental officials to automate activities more effectively. SIS provide the opportunity to improve mobility, increase economic growth, reduce energy outputs, improve management decisions, respond to disasters quicker, and improve citizens’ quality of life. They offer governments the possibility of improving services, while reducing costs. The use and implementation of SIS is becoming widespread and governments are observing the benefits posed by SIS, particularly in relation to urban management.80% of Europe’s population will live in cities by 2020 and governments face a huge strain on resources and infrastructure. The use of SIS is being pioneered to help governments meet these needs and to provide a sustainable future for urban citizens. Ethical issues in this context can include that data may not be accurate, faithful or representative of a city and its citizens, which may cause bias, prejudice and harm to a population, by leading to unfair service provision. ICT companies’ involvement in governmental SIS projects may also lead technological lock-in and dependency on corporations. Instantaneous and ubiquitous retrieval and analysis of data may infringe upon citizens’ privacy and may lead vulnerabilities of malicious hacking, stolen data and a city’s security.To uncover if these issues correlate with the experience of those working in the field, I interviewed the Project Owner of Amsterdam’s DrukteRadar project (translated as crowdedness project). This project implements SIS to anticipate and prevent overcrowding in Amsterdam, and was created in response to growing pressures on the city’s amenities. The DrukteRadar Project collates a wide array of datasets to predict crowd levels and potential problem hotspots, visualised through a digital dashboard. The project aims to improve municipality management, provide help to tourists planning their trips, and assisting citizens’ navigation through the city.Through my discussions with the Project Owner of the DrukteRadar, I uncovered two additional issues not found in the literature: access to SIS and data ownership. The DrukteRadar team is concerned about access to SIS to promote fairness, equality, and provision of services amongst citizens. It aims to make its dashboard user-friendly and available to as many people as possible to promote inclusion. Data ownership is a concern for the project – who owns the data and what can be done with it. The DrukteRadar Project ensures they have data sovereignty, so that they do not become technologically locked-in to relationships with private organisations.The Project Owner was aware that inaccurate of data may lead to discriminatory recommendations and harmful consequences. The DrukteRadar Project tries to minimise their algorithmic inaccuracy through extensive monitoring; secure technical infrastructure; and stakeholder review sessions. Another interesting finding was identifying how projects ensure privacy protection of its citizens. The DrukteRadar ensures that data is not traceable to individuals and the use of datasets follow privacy-by-design principles. The project also has strong security protocols, cyber-security measures, anonymization techniques, and repeated vulnerability tests. Overall, my report was able to evaluate how ethical issues found within the SIS literature correlate to those identified, and tackled, in practice. as well as highlighting the two additional concerns not explicitly mentioned in the literature.

Low Income Community as Crowd Worker for Crowdsourcing: Issues, Challenges and Future Direction

World are now facing flood of low income community responding to current global issue regarding employment. Considering the advantages of crowdsourcing, it has been identified as one potential solution to the issue. Responding to that, the concept has been implemented in many developing countries that have high rate of low income community such as India, Nepal and Philippines. Through crowdsourcing, business and individual can achieve greater and higher productivity in faster and shorter period. Crowdsourcing offer flexibility in terms of time, locations and durations of doing tasks and the most important is crowdsourcing will give additional income to complement existing income that will help to uplift one’s lifestyle hence reduce the rate of low income community. However, focusing on low income community as crowd worker for crowdsourcing require depth understanding on varies factors such as crowd education levels, skills, attitudes, language and etcetera. Visibly, there are numerous issues and challenges in the implementation process. Thus, this study aims to discuss the issues and challenges when dealing with low income community as crowd worker. This study presents seven vital factors that need to be taken into deliberation when dealing with low income community. The results and discussion from this study can be used as a road map to prepare an action plan for a successful crowdsourcing implementation for low income community.

Experiments and Models for Decision Fusion by Humans in Inference Networks

With the advent of the Internet of Things (IoT) and a rapid deployment of smart devices and wireless sensor networks (WSNs), humans interact extensively with machine data. These human decision makers use sensors that provide information through a sociotechnical network. The sensors can be other human users or they can be IoT devices. The decision makers themselves are also part of the network, and there is a need to understand how they will behave. In this paper, the decision fusion behavior of humans is analyzed on the basis of behavioral experiments. The data collected from these experiments demonstrate that people perform decision fusion in a stochastic manner dependent on various factors, unlike machines that perform this task in a deterministic manner. A Bayesian hierarchical model is developed to characterize the observed stochastic human behavior. This hierarchical model captures the differences observed in people at individual, crowd, and population levels. The implications of such a model on designing large-scale inference systems are presented by developing optimal decision fusion trees with both human and machine agents.

Assessing the Effects of Crowding Perception and Gender Among Southeast Asia Pilgrims in Mina, Saudi Arabia

This study gives a better understanding of the effect of crowding on pilgrims at the Hajj. It explores the levels of perceived crowding of Southeastern Asian pilgrims and investigates the effects of gender on their perceived crowding. Questionnaire in both Bahasa Melayu and English languages were administered for data collections from randomly selected pilgrims, (n=128), from the Southeast Asia Hajj group at Mina for three days (10th, 11th and 12th of Dhul-Hijjah). Results revealed that pilgrims witness high levels of crowd perception within their stay at Mina. In addition also, differences between the overall perceived crowding, perceived human crowding and perceived spatial crowding among pilgrims were revealed. Perceived human crowding was found as an indicator to the overall perceived crowding at Mina. Future research could be carried out to assess the effects of other factors such as education and cultural backgrounds and these findings can help authorities in charge of formulating policies manage crowd levels and perception.

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

AbstractHow to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.

Statistical analysis of passenger-crowding in bus transport network of Harbin

Passenger flow data is indispensable but rare in the study of public transport networks. In this study, we focus on the passenger-crowding characteristics of the bus transport network of Harbin (BTN-H) based on passenger flow investigation. The three frequency histograms for all the uplinks and downlinks in Harbin are presented, including passengers on the bus at each section, crowding coefficients, and position parameters of crowded sections. The differences in crowding position are analyzed on each route. The distributions of degree and crowding degree (in directed space L) follow an exponential law. The new finding indicates that there are many stations with few crowded sections and a few stations with many crowded sections. The distributions of path length and crowded length (in directed space P) are presented based on the minimum transfer times, and it is found that they can be fitted by a composite Gaussian function and a Gaussian function, respectively. The stations and paths can be divided into three crowd levels. We conclude that BTN-H is crowded from a network-based perspective.

Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning

Estimating the crowd density of public territories, such as scenic spots, is of great importance for ensuring population safety and social stability. Due to problems in scenic spots such as illumination change, camera angle change, and pedestrian occlusion, current methods are unable to make accurate estimations. To deal with these problems, an ensemble learning (EL) method using support vector regression (SVR) is proposed in this study for crowd density estimation (CDE). The method first uses human head width as a reference to separate the foreground into multiple levels of blocks. Then it adopts the first-level SVR model to roughly predict the three features extracted from image blocks, including D-SIFT, ULBP, and GIST, and the prediction results are used as new features for the second-level SVR model for fine prediction. The prediction results of all image blocks are added for density estimation according to the crowd levels predefined for different scenes of scenic spots. Experimental results demonstrate that the proposed method can achieve a classification rate over 85% for multiple scenes of scenic spots, and it is an effective CDE method with strong adaptability.

The Influence of Personal Factors on Hajj Crowd Perception among African Pilgrim Group in Mina

Objective: This paper examines the effects of personal factors (expectation, control, sociability and mood) on perceived crowding levels among African group of pilgrims. Methods/Statistical Analysis: Data was collected via self-administered questionnaire from 156 African pilgrims to Hajj. The data was analyzed using SPSS for descriptive analysis and AMOS for Structural Equation Modeling (SEM). Internal consistency of the developed research instrument, Kaiser-Meyer- Olkin measure of sampling adequacy and exploratory factor analysis revealed that the research instrument was suitable. Findings: Among the African pilgrim group measurement model, control and sociability had the highest correlation while expectation and perceived level of crowding had the lowest correlation. Furthermore, control personal factor had the least insignificant impact on perceived level of crowding. Expectation and mood personal factors were both significant factors in this study. Applications/Improvements: Policy formation on managing crowd levels and perception will invariably be strategic via incorporating the findings of this paper. Future research will be extended to assess the effects of other factors such as physical factors Hajj pilgrims.

An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders

A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.