Intrinsic group behaviour II: On the dependence of triad spatial dynamics on social and personal features; and on the effect of social interaction on small group dynamics

Social aspects of collision avoidance: a detailed analysis of two-person groups and individual pedestrians

Pedestrian groups are commonly found in crowds but research on their social aspects is comparatively lacking. To fill that void in literature, we study the dynamics of collision avoidance between pedestrian groups (in particular dyads) and individual pedestrians in an ecological environment, focusing in particular on (i) how such avoidance depends on the group's social relation (e.g. colleagues, couples, friends or families) and (ii) its intensity of social interaction (indicated by conversation, gaze exchange, gestures etc). By analyzing relative collision avoidance in the "center of mass" frame, we were able to quantify how much groups and individuals avoid each other with respect to the aforementioned properties of the group. A mathematical representation using a potential energy function is proposed to model avoidance and it is shown to provide a fair approximation to the empirical observations. We also studied the probability that the individuals disrupt the group by "passing through it" (termed as intrusion). We analyzed the dependence of the parameters of the avoidance model and of the probability of intrusion on groups' social relation and intensity of interaction. We confirmed that the stronger social bonding or interaction intensity is, the more prominent collision avoidance turns out. We also confirmed that the probability of intrusion is a decreasing function of interaction intensity and strength of social bonding. Our results suggest that such variability should be accounted for in models and crowd management in general. Namely, public spaces with strongly bonded groups (e.g. a family-oriented amusement park) may require a different approach compared to public spaces with loosely bonded groups (e.g. a business-oriented trade fair).

Modeling of crowd groups with an extended social field model

A social field model is extended by adding supports of simulations of crowd group related dynamics such as group cohesion, back-tracking, etc. A computational implementation combining heterogeneous computing and multi-threading technologies is developed to integrate the proposed extension into a heterogeneous computing framework. Hence modeling of group related dynamics can be accomplished in a very efficient manner. In the section of numerical experiments, the extended model is firstly validated with a set of practical data. Results show that the simulated evacuation time matches the practical one quite well. Then a study of fundamental diagrams is expanded with considerations of crowd groups. It is shown that the impact of crowd groups mainly happens in the regime of low densities and would become insignificant in the regime of high densities.

A new approach for social group detection based on spatio-temporal interpersonal distance measurement

Visual-based social group detection aims to cluster pedestrians in crowd scenes according to social interactions and spatio-temporal position relations by using surveillance video data. It is a basic technique for crowd behaviour analysis and group-based activity understanding. According to the theory of proxemics study, the interpersonal relationship between individuals determines the scope of their self-space, while the spatial distance can reflect the closeness degree of their interpersonal relationship. In this paper, we proposed a new unsupervised approach to address the issues of interaction recognition and social group detection in public spaces, which remits the need to intensely label time-consuming training data. First, based on pedestrians' spatio-temporal trajectories, the interpersonal distances among individuals were measured from static and dynamic perspectives. Combined with proxemics' theory, a social interaction recognition scheme was designed to judge whether there is a social interaction between pedestrians. On this basis, the pedestrians are clustered to identify if they form a social group. Extensive experiments on our pedestrian dataset “SCU-VSD-Social” annotated with multi-group labels demonstrated that the proposed method has outstanding performance in both accuracy and complexity.

Association of the nutritional risk index for Japanese hemodialysis with mortality and dietary nutritional intake in patients undergoing hemodialysis during long-term hospitalization

AIM

The nutritional risk index for Japanese hemodialysis (NRI-JH) is a nutritional screening tool for predicting mortality in patients undergoing hemodialysis; however, its utility in patients undergoing hemodialysis during long-term hospitalization who have a high risk of protein-energy wasting, is unclear.

METHODS

This retrospective study assessed hospitalized patients undergoing hemodialysis during long-term care at a single hospital. The NRI-JH was calculated using body mass index, serum albumin level, total cholesterol level, and serum creatinine level. The patients were categorized into three risk groups-low, medium, and high. Dietary energy and protein intake were evaluated by dietitians. The association of NRI-JH risk with nutritional intake and mortality were examined.

RESULTS

In total, 133 patients were analyzed. The NRI-JH risk was low in 24%, medium in 26%, and high in 50% of the patients. The patients in the high-risk group were older and had lower energy and protein intakes than those in the low- and medium-risk groups. High-risk patients showed shorter survival times than low- and medium-risk patients, and a high NRI-JH risk was associated with a high mortality rate (hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.08-4.77; p < 0.05). The association weakened when protein intake and C-reactive protein level were added as covariates (HR, 2.01; 95% CI, 0.95-4.28, p = 0.07).

CONCLUSIONS

High NRI-JH risk was associated with low dietary nutritional intake and poor survival in patients undergoing hemodialysis during long-term hospitalization. Nutritional status evaluation and nutritional interventions may improve prognosis in this population.

How people with disabilities influence crowd dynamics of pedestrian movement through bottlenecks

Despite considerable research efforts, most controlled empirical studies on crowd movement usually rely on homogeneous crowds, i.e., research participants are typically young adults without disabilities. Consequently, relatively little is known about pedestrian movement in more diverse and heterogeneous crowd conditions, e.g., when persons with reduced mobility are present. This gap may be particularly relevant at bottlenecks, along the path of a moving crowd, that limit the capacity of pedestrian flow. Here, we present results from 12 studies in which participants (total N = 252) with and without visible disabilities moved together in a crowd. In each study, groups of participants walked together in a hallway with a bottleneck at the end. The point of speed adoption, distances between neighbours, and behavioural activities were analysed. We found (1) that participants with disabilities reduced their speed further away from the bottleneck than participants without disabilities; (2) participants without disabilities stayed closer to neighbors with disabilities than to neighbors without disabilities; and (3) participants interacted and communicated with each other to organise in front of the bottleneck. These results underline the importance of studying representative and heterogeneous samples in crowd dynamics. We also argue that more interdisciplinary research is needed to better understand the dynamics of interactions between neighbors in a crowd. A more nuanced understanding of pedestrian dynamics holds the promise of improving the validity of simulation tools such as movement and evacuation models.

Analytical approach to solve the problem of aircraft passenger boarding during the coronavirus pandemic

The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase boarding time. The passenger behavior is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers are travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and low risk of virus transmission. Our results show that the consideration of groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario.