212 research outputs found
Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory
The exploitation of high volume of geolocalized data from social sport
tracking applications of outdoor activities can be useful for natural resource
planning and to understand the human mobility patterns during leisure
activities. This geolocalized data represents the selection of hike activities
according to subjective and objective factors such as personal goals, personal
abilities, trail conditions or weather conditions. In our approach, human
mobility patterns are analysed from trajectories which are generated by hikers.
We propose the generation of the trail network identifying special points in
the overlap of trajectories. Trail crossings and trailheads define our network
and shape topological features. We analyse the trail network of Balearic
Islands, as a case of study, using complex weighted network theory. The
analysis is divided into the four seasons of the year to observe the impact of
weather conditions on the network topology. The number of visited places does
not decrease despite the large difference in the number of samples of the two
seasons with larger and lower activity. It is in summer season where it is
produced the most significant variation in the frequency and localization of
activities from inland regions to coastal areas. Finally, we compare our model
with other related studies where the network possesses a different purpose. One
finding of our approach is the detection of regions with relevant importance
where landscape interventions can be applied in function of the communities.Comment: 20 pages, 9 figures, accepte
A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces
Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more âsmartâ, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participantsâ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.This work has been supported by the Cost Action TU1306, called CYBERPARKS:
Fostering knowledge about the relationship between Information and Communication Technologies and Public
Spaces supported by strategies to improve their use and attractiveness, the Spanish Ministry of Economy
and Competitiveness under the ESPHIA project (ref. TIN2014-56042-JIN) and the TARSIUS project (ref.
TIN2015-71564-C4-4-R), and the Basque Country Department of Education under the BLUE project (ref.
PI-2016-0010). The authors would also like to thank the staff of UbiSive s.r.l. for the support in developing
the application
Reconstructing human activities via coupling mobile phone data with location-based social networks
In the era of big data, the ubiquity of location-aware portable devices
provides an unprecedented opportunity to understand inhabitants' behavior and
their interactions with the built environments. Among the widely used data
resources, mobile phone data is the one passively collected and has the largest
coverage in the population. However, mobile operators cannot pinpoint one user
within meters, leading to the difficulties in activity inference. To that end,
we propose a data analysis framework to identify user's activity via coupling
the mobile phone data with location-based social networks (LBSN) data. The two
datasets are integrated into a Bayesian inference module, considering people's
circadian rhythms in both time and space. Specifically, the framework considers
the pattern of arrival time to each type of facility and the spatial
distribution of facilities. The former can be observed from the LBSN Data and
the latter is provided by the points of interest (POIs) dataset. Taking
Shanghai as an example, we reconstruct the activity chains of 1,000,000 active
mobile phone users and analyze the temporal and spatial characteristics of each
activity type. We assess the results with some official surveys and a
real-world check-in dataset collected in Shanghai, indicating that the proposed
method can capture and analyze human activities effectively. Next, we cluster
users' inferred activity chains with a topic model to understand the behavior
of different groups of users. This data analysis framework provides an example
of reconstructing and understanding the activity of the population at an urban
scale with big data fusion
Does Twinning Vehicular Networks Enhance Their Performance in Dense Areas?
This paper investigates the potential of Digital Twins (DTs) to enhance
network performance in densely populated urban areas, specifically focusing on
vehicular networks. The study comprises two phases. In Phase I, we utilize
traffic data and AI clustering to identify critical locations, particularly in
crowded urban areas with high accident rates. In Phase II, we evaluate the
advantages of twinning vehicular networks through three deployment scenarios:
edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis
demonstrates that twinning significantly reduces network delays, with virtual
twins outperforming physical networks. Virtual twins maintain low delays even
with increased vehicle density, such as 15.05 seconds for 300 vehicles.
Moreover, they exhibit faster computational speeds, with cloud-based twins
being 1.7 times faster than edge twins in certain scenarios. These findings
provide insights for efficient vehicular communication and underscore the
potential of virtual twins in enhancing vehicular networks in crowded areas
while emphasizing the importance of considering real-world factors when making
deployment decisions.Comment: 6 pages, 8 figures, 2tables, conference pape
Temporal convolutional networks for multi-person activity recognition using a 2D LIDAR
Motion trajectories contain rich information about human activities. We propose to use a 2D LIDAR to perform multiple people activity recognition simultaneously by classifying their trajectories. We clustered raw LIDAR data and classified the clusters into human and non-human classes in order to recognize humans in a scenario. For the clusters of humans, we implemented the Kalman Filter to track their trajectories which are further segmented and labelled with corresponding activities. We introduced spatial transformation and Gaussian noise for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition (HAR). Finally, we built two neural networks including a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to classify trajectory samples into 15 activity classes collected from a kitchen. The proposed TCN achieved the best result of 99.49% in overall accuracy. In comparison, the TCN is slightly superior to the LSTM network. Both the TCN and the LSTM network outperform hidden Markov Model (HMM), dynamic time warping (DTW), and support vector machine (SVM) with a wide margin. Our approach achieves a higher activity recognition accuracy than the related work
Recommended from our members
Crowdsourced Data Mining for Urban Activity: A Review of Data Sources, Applications and Methods
The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.This research is funded by a scholarship from the China Scholarship Counci
Identifying and understanding road-constrained areas of interest (AOIs) through spatiotemporal taxi GPS data: A case study in New York City
Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications.
Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained AOIs in terms of their spatiotemporal distribution and contextual characteristics. Our analysis captures many important locations, including but not limited to primary transit hubs, famous cultural venues, open spaces, and some other tourist attractions, prominent landmarks, and commercial centres. Moreover, we respectively analyse these AOIs in terms of their dynamics and contexts by performing further clustering analysis, formulating five temporal clusters delineating the dynamic evolution of the AOIs and four contextual clusters representing their salient contextual characteristics
- âŠ