66,409 research outputs found
Inflow process of pedestrians to a confined space
To better design safe and comfortable urban spaces, understanding the nature
of human crowd movement is important. However, precise interactions among
pedestrians are difficult to measure in the presence of their complex
decision-making processes and many related factors. While extensive studies on
pedestrian flow through bottlenecks and corridors have been conducted, the
dominant mode of interaction in these scenarios may not be relevant in
different scenarios. Here, we attempt to decipher the factors that affect human
reactions to other individuals from a different perspective. We conducted
experiments employing the inflow process in which pedestrians successively
enter a confined area (like an elevator) and look for a temporary position. In
this process, pedestrians have a wider range of options regarding their motion
than in the classical scenarios; therefore, other factors might become
relevant. The preference of location is visualized by pedestrian density
profiles obtained from recorded pedestrian trajectories. Non-trivial patterns
of space acquisition, e.g., an apparent preference for positions near corners,
were observed. This indicates the relevance of psychological and anticipative
factors beyond the private sphere, which have not been deeply discussed so far
in the literature on pedestrian dynamics. From the results, four major factors,
which we call flow avoidance, distance cost, angle cost, and boundary
preference, were suggested. We confirmed that a description of decision-making
based on these factors can give a rise to realistic preference patterns, using
a simple mathematical model. Our findings provide new perspectives and a
baseline for considering the optimization of design and safety in crowded
public areas and public transport carriers.Comment: 23 pages, 6 figure
A large-scale real-life crowd steering experiment via arrow-like stimuli
We introduce "Moving Light": an unprecedented real-life crowd steering
experiment that involved about 140.000 participants among the visitors of the
Glow 2017 Light Festival (Eindhoven, NL). Moving Light targets one outstanding
question of paramount societal and technological importance: "can we seamlessly
and systematically influence routing decisions in pedestrian crowds?"
Establishing effective crowd steering methods is extremely relevant in the
context of crowd management, e.g. when it comes to keeping floor usage within
safety limits (e.g. during public events with high attendance) or at designated
comfort levels (e.g. in leisure areas). In the Moving Light setup, visitors
walking in a corridor face a choice between two symmetric exits defined by a
large central obstacle. Stimuli, such as arrows, alternate at random and
perturb the symmetry of the environment to bias choices. While visitors move in
the experiment, they are tracked with high space and time resolution, such that
the efficiency of each stimulus at steering individual routing decisions can be
accurately evaluated a posteriori. In this contribution, we first describe the
measurement concept in the Moving Light experiment and then we investigate
quantitatively the steering capability of arrow indications.Comment: 8 page
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Crowdsourced Live Streaming over the Cloud
Empowered by today's rich tools for media generation and distribution, and
the convenient Internet access, crowdsourced streaming generalizes the
single-source streaming paradigm by including massive contributors for a video
channel. It calls a joint optimization along the path from crowdsourcers,
through streaming servers, to the end-users to minimize the overall latency.
The dynamics of the video sources, together with the globalized request demands
and the high computation demand from each sourcer, make crowdsourced live
streaming challenging even with powerful support from modern cloud computing.
In this paper, we present a generic framework that facilitates a cost-effective
cloud service for crowdsourced live streaming. Through adaptively leasing, the
cloud servers can be provisioned in a fine granularity to accommodate
geo-distributed video crowdsourcers. We present an optimal solution to deal
with service migration among cloud instances of diverse lease prices. It also
addresses the location impact to the streaming quality. To understand the
performance of the proposed strategies in the realworld, we have built a
prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our
extensive experiments demonstrate that the effectiveness of our solution in
terms of deployment cost and streaming quality
Agent-based pedestrian modelling
When the focus of interest in geographical systems is at the very fine scale, at the level of
streets and buildings for example, movement becomes central to simulations of how spatial
activities are used and develop. Recent advances in computing power and the acquisition of
fine scale digital data now mean that we are able to attempt to understand and predict such
phenomena with the focus in spatial modelling changing to dynamic simulations of the
individual and collective behaviour of individual decision-making at such scales. In this
Chapter, we develop ideas about how such phenomena can be modelled showing first how
randomness and geometry are all important to local movement and how ordered spatial
structures emerge from such actions. We focus on developing these ideas for pedestrians
showing how random walks constrained by geometry but aided by what agents can see,
determine how individuals respond to locational patterns. We illustrate these ideas with three
types of example: first for local scale street scenes where congestion and flocking is all
important, second for coarser scale shopping centres such as malls where economic
preference interferes much more with local geometry, and finally for semi-organised street
festivals where management and control by police and related authorities is integral to the
way crowds move
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