Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data

Andrienko, Gennady; Andrienko, Natalia; Fuchs, Georg; Wood, Jo

doi:10.1109/tvcg.2016.2616404

Cited by 97 publications

(82 citation statements)

References 49 publications

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“…To understand the spatiotemporal patterns and long-term trends of mass-mobility behaviors, Andrienko et al [132] developed a general procedure to analyze spatial-flow situations based on temporal dynamics, where the spatial abstraction aggregates Origin-destination (OD) flows that share a common origin or destination by the ranges of the direction and distance. This approach reduces the data dimensionality and enables a visual representation of flows via diagram maps, thereby avoiding line crossings, which are inevitable in flow maps with links denoted by lines.…”

Section: Difference Diagramsmentioning

confidence: 99%

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

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Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.First, we should fully understand the conceptual distinction between "multidimensional" and "multivariate". Generally, "multidimensional" is used to describe the dimensionality of independent variables in physical space and concentrates on expressing spatial and temporal concepts, while "multivariate" is used for attribute-count description that indicates the attribute amount involved in the data. In fact, trajectory data are typically enormous, high-dimensional, spatiotemporal vector attribute data. We are interested not only in comprehending dimensional information for spatiotemporal-element depictions, but also in the visualization research with attribute-feature expression. Starting in Section 2, we present relevant visualization techniques and methods that function with multisource trajectory data. The chosen examples are for illustration and the list is not exhaustive-we only focus on small but representative choices. Universal Multivariate VisualizationThe trajectory visualization reflects the changing patterns of time-varying geolocations and corresponding attribute information, including basic-dimensional information and the attached thematic variables of space-time objects. However, these variables usually do not exist independently but are combined with each other, and the trajectory data of different sources, scales, or types are plotted in a hybrid manner. We separately list visualization techniques that integrate multiple attributes. As a powerful and portable carrier that encodes abundant information in methods of visual cha...

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Section: Difference Diagramsmentioning

confidence: 99%

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

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“…Such display is free from occlusion, but the space distortion complicates the perception, and the overall spatial pattern of flows is broken into multiple locationspecific patterns. Recently, it has been proposed to aggregate OD data in a way that not only reduces the data dimensionality for efficient interactive analysis but also enables visual representation by means of diagrams rather than intersecting flow lines [7]. The diagrams are positioned at the places of trip origins (Fig.…”

Section: Linking Origins To Destinationsmentioning

confidence: 99%

“…Aggregated outgoing (left) and incoming (right) car trips to/from different directions and distance ranges are represented by diagrams with segment widths proportional to the flow magnitudes. [7] interactively select particular flows for viewing and comparing their variations over time, which are represented on linear and circular histograms [59]. Comparison of two density maps, e.g., corresponding to different time intervals or different types of moving objects, can be supported by subtracting one map from another and encoding positive and negative differences by shades of two color hues [48].…”

Section: E Collective Movement Over a Territorymentioning

confidence: 99%

“…The status of docking stations over space and time is further visualized in a grid view, and patterns of the bicycle hire program revealed insight into how different populations use the bicycle hires. The spatiotemporal patterns of bicycle trips over a long time period were also investigated using aggregation of OD data by trip directions and distance ranges and clustering of spatial situations from different time intervals [7].…”

Section: A Use Of Transportmentioning

confidence: 99%

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Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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112

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This is the accepted version of the paper.This version of the publication may differ from the final published version. City Research Online  Abstract-Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions including social transportation, recommender systems, and policy implications. Permanent repository link

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“…Here, the work lead by G. Andrienko [22] developed methods to visualize general OD patterns using diagram maps rather than flow maps. The methods were applied to two case studies that illustrated the ability of the proposed data visualizations to reduce dimensionality using spatial and temporal abstraction.…”

Section: Trajectory Data Applicationsmentioning

confidence: 99%

Probe Vehicle Based Trajectory Data Visualization and Applications

Petrone¹,

Franz²

2018

IJT

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Background and MotivationUnderstanding highway travel demand patterns is critical for transportation network analysis and planning. Traditionally, analysis of travel demand patterns relies on transportation surveys. While such surveys offer valuable information on general travel behaviors, the need for dynamic, continuous, high resolution traveler data has driven the development of cutting edge data collection methods based on Global Positioning System (GPS) information. The initial advancement along these lines was the utilization of vehicle probe data that uses GPS information to measure the travel times and speeds on pre-defined road segments called traffic message channels (TMCs). While traditional probe data has transformed how transportation professionals monitor and analyze the roadway network, it did not fully utilize the individual trip level spatio-temporal capabilities of GPS data. To meet this need, several transportation data providers and way-finding service providers have developed technologies to passively collect anonymous traveler data based on timestamped location information, which will be referred to in this paper as trajectory data. Upon processing trajectory data, information on trip origins and destinations (OD), departure times, route choices, and travel times can be extracted to understand travel behavior patterns and the related transportation network performance.As will be described in the literature review section, there has been tremendous progress made on utilizing trajectory data for traffic network monitoring and analysis with cutting edge data visualizations. However, a majority of these applications have been

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Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data

Cited by 97 publications

References 49 publications

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Probe Vehicle Based Trajectory Data Visualization and Applications

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