7 research outputs found
Understanding Human Mobility Flows from Aggregated Mobile Phone Data
In this paper we deal with the study of travel flows and patterns of people
in large populated areas. Information about the movements of people is
extracted from coarse-grained aggregated cellular network data without tracking
mobile devices individually. Mobile phone data are provided by the Italian
telecommunication company TIM and consist of density profiles (i.e. the spatial
distribution) of people in a given area at various instants of time. By
computing a suitable approximation of the Wasserstein distance between two
consecutive density profiles, we are able to extract the main directions
followed by people, i.e. to understand how the mass of people distribute in
space and time. The main applications of the proposed technique are the
monitoring of daily flows of commuters, the organization of large events, and,
more in general, the traffic management and control.Comment: 6 pages, 14 figure
The Impact of Biases in the Crowdsourced Trajectories on the Output of Data Mining Processes
The emergence of the Geoweb has provided an unprecedented capacity for generating and sharing digital content by professional and non- professional participants in the form of crowdsourcing projects, such as OpenStreetMap (OSM) or Wikimapia. Despite the success of such projects, the impacts of the inherent biases within the ‘crowd’ and/or the ‘crowdsourced’ data it produces are not well explored. In this paper we examine the impact of biased trajectory data on the output of spatio-temporal data mining process. To do so, an experiment was conducted. The biases are intentionally added to the input data; i.e. the input trajectories were divided into two sets of training and control datasets but not randomly (as opposed to the data mining procedures). They are divided by time of day and week, weather conditions, contributors’ gender and spatial and temporal density of trajectory in 1km grids. The accuracy of the predictive models are then measured (both for training and control data) and biases gradually moderated to see how the accuracy of the very same model is changing with respect to the biased input data. We show that the same data mining technique yields different results in terms of the nature of the clusters and identified attributes
A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes
The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns
Understanding Mass Transfer Directions via Data-Driven Models with Application to Mobile Phone Data
The aim of this paper is to solve an inverse problem which regards a mass
moving in a bounded domain. We assume that the mass moves following an unknown
velocity field and that the evolution of the mass density can be described by
partial differential equations (PDEs), which is also unknown. The input data of
the problems are given by some snapshots of the mass distribution at certain
times, while the sought output is the velocity field that drives the mass along
its displacement. To this aim, we put in place an algorithm based on the
combination of two methods: first, we use the Dynamic Mode Decomposition to
create a mathematical model describing the mass transfer; second, we use the
notion of Wasserstein distance (also known as earth mover's distance) to
reconstruct the underlying velocity field that is responsible for the
displacement. Finally, we consider a real-life application: the algorithm is
employed to study the travel flows of people in large populated areas using, as
input data, density profiles (i.e. the spatial distribution) of people in given
areas at different time instances. This kind of data are provided by the
Italian telecommunication company TIM and are derived by mobile phone usage.Comment: 19 pages, 10 figure
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data
The unprecedented urbanization in China has dramatically changed the urban
spatial structure of cities. With the proliferation of individual-level
geospatial big data, previous studies have widely used the network abstraction
model to reveal the underlying urban spatial structure. However, the
construction of network abstraction models primarily focuses on the topology of
the road network without considering individual travel flows along with the
road networks. Individual travel flows reflect the urban dynamics, which can
further help understand the underlying spatial structure. This study therefore
aims to reveal the intra-urban spatial structure by integrating the road
network abstraction model and individual travel flows. To achieve this goal, we
1) quantify the spatial interaction relatedness of road segments based on the
Word2Vec model using large volumes of taxi trip data, then 2) characterize the
road abstraction network model according to the identified spatial interaction
relatedness, and 3) implement a community detection algorithm to reveal
sub-regions of a city. Our results reveal three levels of hierarchical spatial
structures in the Wuhan metropolitan area. This study provides a data-driven
approach to the investigation of urban spatial structure via identifying
traffic interaction patterns on the road network, offering insights to urban
planning practice and transportation management