404 research outputs found
T-PickSeer: Visual Analysis of Taxi Pick-up Point Selection Behavior
Taxi drivers often take much time to navigate the streets to look for
passengers, which leads to high vacancy rates and wasted resources. Empty taxi
cruising remains a big concern for taxi companies. Analyzing the pick-up point
selection behavior can solve this problem effectively, providing suggestions
for taxi management and dispatch. Many studies have been devoted to analyzing
and recommending hot-spot regions of pick-up points, which can make it easier
for drivers to pick up passengers. However, the selection of pick-up points is
complex and affected by multiple factors, such as convenience and traffic
management. Most existing approaches cannot produce satisfactory results in
real-world applications because of the changing travel demands and the lack of
interpretability. In this paper, we introduce a visual analytics system,
T-PickSeer, for taxi company analysts to better explore and understand the
pick-up point selection behavior of passengers. We explore massive taxi GPS
data and employ an overview-to-detail approach to enable effective analysis of
pick-up point selection. Our system provides coordinated views to compare
different regularities and characteristics in different regions. Also, our
system assists in identifying potential pick-up points and checking the
performance of each pick-up point. Three case studies based on a real-world
dataset and interviews with experts have demonstrated the effectiveness of our
system.Comment: 10 pages, 10 figures; The 10th China Visualization and Visual
Analytics Conferenc
The Possibility of Big Data Spatio-Temporal Analytics for Understanding Human Behavior and Their Spatial Patterns in Urban Area
13301甲第4630号博士(学術)金沢大学博士論文本文Ful
Trajectory data mining: A review of methods and applications
The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used
Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks
Intra-urban origin-destination (OD) network communities evolve throughout the
day, indicating changing groups of closely connected regions. Under this
variation, groups of regions with high consistency of community affiliation
characterize the temporally stable structure of the evolution process, aiding
in comprehending urban dynamics. However, how to quantify this consistency and
identify these groups are open questions. In this study, we introduce the
consensus OD network to quantify the consistency of community affiliation among
regions. Furthermore, the temporally stable community decomposition method is
proposed to identify groups of regions with high internal and low external
consistency (named "stable groups"), where each group consists of temporally
stable cores and attaching peripheries. Wuhan taxi data is used to verify our
methods. On the hourly time scale, eleven stable groups containing 82.9% of
regions are identified. This high percentage suggests that dynamic communities
can be well organized via cores. Moreover, stable groups are spatially closed
and more likely to distribute within a single district and separated by water
bodies. Cores exhibit higher POI entropy and more healthcare and shopping
services than peripheries. Our methods and empirical findings contribute to
some practical issues, such as urban area division, polycentric evaluation and
construction, and infectious disease control
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