Intrinsic group behaviour: dependence of pedestrian dyad dynamics on principal social and personal features

Being determined by human social behaviour, pedestrian group dynamics depends on "intrinsic properties" of the group such as the purpose of the pedestrians, their personal relation, their gender, age, and body size. In this work we quantitatively study the dynamical properties of pedestrian dyads by analysing a large data set of automatically tracked pedestrian trajectories in an unconstrained "ecological" setting (a shopping mall), whose relational group properties have been coded by three different human observers. We observed that females walk slower and closer than males, that workers walk faster, at a larger distance and more abreast than leisure oriented people, and that inter group relation has a strong effect on group structure, with couples walking very close and abreast, colleagues walking at a larger distance, and friends walking more abreast than family members. Pedestrian height (obtained automatically through our tracking system) influences velocity and abreast distance, both growing functions of the average group height. Results regarding pedestrian age show as expected that elderly people walk slowly, while active age adults walk at the maximum velocity. Groups with children have a strong tendency to walk in a non abreast formation, with a large distance (despite a low abreast distance). A cross-analysis of the interplay between these intrinsic features, taking in account also the effect of extrinsic crowd density, confirms these major effects but reveals also a richer structure. An interesting and unexpected result, for example, is that the velocity of groups with children {\it increases} with density, at least in the low-medium density range found under normal conditions in shopping malls. Children also appear to behave differently according to the gender of the parent

Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information

Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/testing. The proposed system was found to outperform a baseline and a previous state of the art in an offline, human-judged evaluation

Social group behaviour of triads. Dependence on purpose and gender

We analysed a set of uninstructed pedestrian trajectories automatically tracked in a public area, and we asked a human coder to assess their group relationships. For those pedestrians who belong to the groups, we asked the coder to identify their apparent purpose of visit to the tracking area and apparent gender. We studied the quantitative dependence of the group dynamics on such properties in the case of triads (three people groups) and compared them to the two pedestrian group case (dyads), studied in a previous work. We found that the group velocity strongly depends on relation and gender for both triads and dyads, while the influence of these properties on spatial structure of groups is less clear in the triadic case. We discussed the relevance of these results to the modelling of pedestrian and crowd dynamics, and examined the possibility of the future works on this subject

Characterization of soil microflora on a successional glacier foreland in the high Arctic on Ellesmere Island, Nunavut, Canada using phospholipid fatty acid analysis

We investigated soil microbial biomass and community structure along a primary successional gradient after deglaciation in the high Arctic, at Ellesmere Island, Nunavut, Canada(80°50\u27N, 82°45\u27W). Soil samples were collected from five glacial moraines(M1 to M5) with different developmental periods. Time since the recession of glaciers at M1, M3, and M5 was estimated to be 300, 9000, and over 17000 years, respectively. Soil samples from five points in each moraine were subjected to phospholipid fatty acid(PLFA) analysis. Total PLFA content(an index of microbial biomass) in M1 was significantly lower than those in older moraines(M2-M5), whereas the content remained at an almost constant level from M2 to M5. Significant differences in PLFA composition(an index of microbial community structure) were also observed between M1 and older moraines(M2-M5); the proportion of straight chain saturated fatty acids in M1 was higher than those in older moraines(M2-M5), whereas those of branched fatty acids and unsaturated fatty acids in M1 were lower than those in older moraines(M2-M5). These results suggest that changes of microflora occurred in the early phase of succession after deglaciation and became stable thereafter. Microbial biomass had a positive correlation with soil carbon and nitrogen contents over the successional chronosequence, suggesting that development of soil microflora was affected in part by organic matter accumulation

Thermodynamics of a gas of pedestrians: theory and experiment

In this paper, we perform an experiment on the interaction of pedestrians in a chaotic environment and investigate the possibility to study its results using a thermodynamic model. In contrast to simple single-file unidirectional scenarios, where only distance and time are relevant to adjust walking speed, bidirectional cases are much more complex since pedestrians can perform evading manoeuvres to avoid collisions. To better understand collision avoidance in a bidimensional environment we designed a set of experiments where people need to move chaotically for the whole time. Trajectories of moving pedestrians were obtained by tracking their head position, but a method to obtain body orientation failed, thus limiting reliable information on average quantities, i.e. average density and speed. By analysing those data, we showed that equations for thermodynamic processes can be used to describe pedestrian dynamics from medium densities or a state where interaction distances are very small. To allow combining low density cognitive aspects of collision avoidance with semi-random motion at medium densities we also developed a microscopic simulation model inspired by physics. Results show that, after calibrations, the simulation model allows to reproduce the fundamental diagram of different studies despite the very simple rules implemented. This shows that describing the statistical nature of specific crowds requires a relatively small set of rules and research should focus on cognitive/psychological aspects which are essential for understanding crowds of people