A visual analytics framework for spatio-temporal analysis and modelling

Andrienko, Natalia; Andrienko, Gennady

doi:10.1007/s10618-012-0285-7

Cited by 56 publications

(54 citation statements)

References 26 publications

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“…One work [7] proposes a visual analytics framework to support representing spatio-temporal patterns of traffic flows and relationships between flow characteristics by formal models. Such models can, obviously, be used for forecasting, but the transition from 'assess' to 'forecast' and 'develop options' is not supported.…”

Section: Visual Analytics Methods For Analysis Of Movement Datamentioning

confidence: 99%

“…2, we applied partition-based clustering (k-means) to the time series of flow characteristics, i.e., the links have been clustered by the similarity of the associated time series of the flow intensities and mean speeds. Colors have been assigned to the resulting clusters in such a way that similar colors correspond to similar clusters [7]. These colors have been used to paint the flow symbols on the map.…”

Section: Spatio-temporal Aggregation Of Movement Datamentioning

confidence: 99%

“…The visual analytics support to the 'assess' task includes techniques for exploratory analysis of movement data and interactive tools for derivation of formal models representing the spatio-temporal variation of traffic flows and the relationships between the traffic intensities and mean speeds [2] [7]. The process of model building is supported by an interactive visual interface illustrated in Fig.…”

Section: Assessmentioning

confidence: 99%

“…They allowed us to spot fundamental patterns of traffic flow dynamics [8] (briefly presented in the next section) that are common for different areas and spatial scales. The next step was development of interactive visual interfaces for representing these patterns by mathematical models [7]. After obtaining such models, a logical next step in our research was to try to use them for traffic forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Andrienko

Rinzivillo

2016

Machine Learning and Knowledge Discovery in Databases

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. Permanent repository linkWe propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.

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Section: Visual Analytics Methods For Analysis Of Movement Datamentioning

confidence: 99%

Section: Spatio-temporal Aggregation Of Movement Datamentioning

confidence: 99%

Section: Assessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Andrienko

Rinzivillo

2016

Machine Learning and Knowledge Discovery in Databases

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“…To solve the two challenges listed above, we divide our study into three steps, based on the visual analytics framework provided by (Andrienko, 2013). First, we define the specific movement events which relate to our analysis problem; for example, we may extract low speeds occurring along a movement track using an extraction method to find the stop events and stop areas.…”

Section: Introductionmentioning

confidence: 99%

Visualization of Spatio-Temporal Relations in Movement Event Using Multi-View

Zheng

Fang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multiscale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatiotemporal relations of movement events from taxi trajectory data.

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A survey of evaluation methods for personal route and destination prediction from mobility traces

Stegmann

Žliobaitė

Tolvanen

et al. 2017

WIREs Data Min & Knowl

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Personal mobility data can nowadays be easily collected by personal mobile phones and used for analytical modeling. To assist in such an analysis, a variety of computational approaches have been developed. The goal is to extract mobility patterns in order to provide traveling assistance, information, recommendations or on‐demand services. While various computational techniques are being developed, research literature on destination and route prediction lacks consistency in evaluation methods for such approaches. This study presents a review and categorization of evaluation criteria and terminology used in assessing the performance of such methods. The review is complemented by experimental analysis of selected evaluation criteria, to highlight the nuances existing between the evaluation measures. The experimental study uses previously unpublished mobility data of 15 users collected over a period of 6 months in Helsinki metropolitan area in Finland. The article is primarily intended for researchers developing approaches for personalized mobility analysis, as well as a guideline for practitioners to select criteria when assessing and selecting between computational approaches. Our main recommendation is to consider user‐specific accuracy measures in addition to averaged aggregates, as well as to take into consideration that for many users accuracy does not saturate fast and the performance keeps evolving over time. Therefore, we recommend using time‐weighted measures. WIREs Data Mining Knowl Discov 2018, 8:e1237. doi: 10.1002/widm.1237 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Society and Culture Application Areas > Industry Specific Applications

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A visual analytics framework for spatio-temporal analysis and modelling

Abstract: This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link

Cited by 56 publications

References 26 publications

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Visualization of Spatio-Temporal Relations in Movement Event Using Multi-View

A survey of evaluation methods for personal route and destination prediction from mobility traces

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