Visual analytics for movement behavior in traffic and transportation (original) (raw)
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Anais do Congresso Brasileiro de Automática 2020, 2020
The control and monitoring of public transport buses considering the Global Positioning System (GPS) produce a data tsunami for the creation of indicators related to public transportation. The conventional techniques of data analysis for this type of information require programming effort, execution of algorithms with high processing in extensive databases to get at the end the production of the idealized statistical and visualization reports. In this process, the search for new analyzes or visualizations may require a restart of the process, a control of the versioning of the developed programs and repetitive high processing algorithm. This form of acting hinder the cognitive process, the analysis capability and the inference of relevant information. In this context, this paper proposes a methodology based on Visual Analytics to infer passenger demand based on the trajectory of conventional buses for planning new routes served by electric buses at the State University of Campinas -...
A Visual Analytics Framework for Large Transportation Datasets
The advancement of sensor technologies makes it possible to collect large amounts of dynamic urban data. On the other hand, how to store, process, and analyze collected urban data to make them useful becomes a new challenge. To address this issue, this paper proposes a visual analytics framework, which is applied to transportation data to manage and extract information for urban studies. More specifically, the proposed framework has three components: (1) a geographic information system (GIS) based pipeline providing basic data processing functions; (2) a spatial network analysis that is integrated into the pipeline for extracting spatial structure of urban movement; (3) interactive operations allowing the user to explore and view the output data sets at different levels of details. Taking Singapore as a case study area, we use a sample data set from the automatic smart card fare collection system as an input to our prototype tool. The result shows the feasibility of proposed framework and analysis method. To summarize, our work shows the potential of geospatial based visual analytics tools in using 'big' data for urban analysis.
2011
In this paper, we present an interactive visual analytics system, Triple Perspective Visual Trajectory Analytics (TripVista), for exploring and analyzing complex traffic trajectory data. The users are equipped with a carefully designed interface to inspect data interactively from three perspectives (spatial, temporal and multi-dimensional views). While most previous works, in both visualization and transportation research, focused on the macro aspects of traffic flows, we develop visualization methods to investigate and analyze microscopic traffic patterns and abnormal behaviors. In the spatial view of our system, traffic trajectories with various presentation styles are directly interactive with user brushing, together with convenient pattern exploration and selection through ring-style sliders. Improved ThemeRiver, embedded with glyphs indicating directional information, and multiple scatterplots with time as horizontal axes illustrate temporal information of the traffic flows. Our system also harnesses the power of parallel coordinates to visualize the multi-dimensional aspects of the traffic trajectory data. The above three view components are linked closely and interactively to provide access to multiple perspectives for users. Experiments show that our system is capable of effectively finding both regular and abnormal traffic flow patterns.
Visual Analytics for Characterizing Mobility Aspects of Urban Context
Springer eBooks, 2021
Visual analytics science develops principles and methods for efficient human-computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.
GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 2011
In previous work, we have proposed a tool for Spatiotemporal Pattern Query. It matches individual moving object trajectories against a given movement pattern. For example, it can be used to find the situations of Missed Approach in ATC data (Air Traffic Control systems, used for tracking the movement of aircrafts), where the landing of the aircraft was interrupted for some reason. This tool expresses the pattern as a set of predicates that must be fulfilled in a certain temporal order. It is implemented as a Plugin to the Secondo DBMS system. Although the tool is generic and flexible, domain expertise is required to formulate and tune queries. The user has to decide the set of predicates, their arguments, and the temporal constraints that best describe the pattern. This paper demonstrates a novel solution where a Visual Analytics system, V-Analytics, is used in integration with this query tool to help a human analyst explore such patterns. The demonstration is based on a real ATC data set.
Visual Analytics Methods for Movement Data
Mobility, Data Mining and Privacy, 2008
The chapter considers the use of interactive visual techniques for detection of various patterns and relationships in movement data. While a variety of techniques and tools for visualisation of movement data exist, very few of them are applicable to massive data. Moreover, movement data have quite a complex structure, which necessitates the use of multiple heterogeneous displays showing different aspects of the data. Linking between the displays is essential; however, traditional techniques for display linking are not scalable to large data collections. The chapter outlines a roadmap to the creation of visualisation-centred tools for analysis of massive movement data. Besides visual representation of data, the tools involve data aggregation and other transformations. All the suggested visualisation techniques display data in an aggregated form and are therefore scalable. There are open issues that require further research. One of them is how several displays with differently aggregated data can be effectively linked while individual data items are not available in the computer memory. Another problem is visualisation of discovered patterns for the purposes of knowledge synthesis and communication.
Visualization of Urban Mobility Data from Intelligent Transportation Systems
Sensors, 2019
Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings.
Algorithmic and Visual Analysis of Spatiotemporal Stops in Movement Data
2016
The extensive use of geographic positioning devices leads to the generation of large amounts of movement data, collected and stored in digital repositories. Such collections of movement data enable domain experts and scientists to analyze and discover interesting movement patterns. Analyzing the occurrence of stops (stops refer to full halts or very slow speed) in transportation systems is an im-portant challenge in movement data analysis. This analysis can be used to better understand traffic congestion problems and find cor-responding solutions. We propose an efficient system to analyze stop occurrences. It consists of two major parts that we describe in this paper. The first one deals with our algorithmic solutions. We propose an efficient clustering algorithm to partition the stops into groups. The main goal is to detect strongly connected com-ponents of stops, where two stops belong to the same component if they are close enough. The idea is that each component will be an isola...
BVis: urban traffic visual analysis based on bus sparse trajectories
Journal of Visualization, 2018
Public urban transport network has the characteristic of wide coverage, while buses have the features of stable running routes and static parking places, which is helpful to study urban traffic and bus station congestion patterns. Unlike the common GPS trajectory data, our data includes the pertinent records of the buses arrival and departure from the relevant bus stations due to data compression. In this paper, a visual analysis system called BVis is presented to analyze the urban traffic applying the large-scale real sparse buses dataset. This system covers the four modules of bus data visualization, first, the sparse trajectory data cleaning and mapping, second, the global traffic states and section traffic patterns analysis of roads, third, the bus station congestion patterns analysis using the station parking time, finally, an importance analysis of bus stations in the complex public transport network. Furthermore, an enhanced node importance evaluation algorithm is presented, which combines the dynamic properties of the bus station, such as traffic volume of station and station parking time. Using the real bus GPS dataset, three cases are described to demonstrate the performance and effectiveness of the system.