Revealing Underlying Structure and Behaviour from Movement Data

Sester, Monika; Feuerhake, Udo; Kuntzsch, Colin; Zhang, Lijuan

doi:10.1007/s13218-012-0180-9

Cited by 15 publications

(6 citation statements)

References 6 publications

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“…the land use for hospitals or commercial districts. The analysis of trajectories in the traffic context has been conducted with respect to movement behaviour (Sester et al, 2012) or travel mode (Bohte and Maat, 2009). This is done with different foci, e.g.…”

Section: Related Workmentioning

confidence: 99%

Identification of Similarities and Prediction of Unknown Features in an Urban Street Network

Feuerhake

Wage

Sester

et al. 2018

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

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<p><strong>Abstract.</strong> Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.</p>

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Section: Related Workmentioning

confidence: 99%

Identification of Similarities and Prediction of Unknown Features in an Urban Street Network

Feuerhake

Wage

Sester

et al. 2018

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

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“…Segmentation methods were also developed in MOVE for other types of trajectories. Sester et al [ 104 ] present a method to link segments to human movement behaviour. Their segmentation is based on identifying important places from trajectories (most frequently visited places for the longest time).…”

Section: Reviewmentioning

confidence: 99%

Analysis and visualisation of movement: an interdisciplinary review

et al. 2015

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The processes that cause and influence movement are one of the main points of enquiry in movement ecology. However, ecology is not the only discipline interested in movement: a number of information sciences are specialising in analysis and visualisation of movement data. The recent explosion in availability and complexity of movement data has resulted in a call in ecology for new appropriate methods that would be able to take full advantage of the increasingly complex and growing data volume. One way in which this could be done is to form interdisciplinary collaborations between ecologists and experts from information sciences that analyse movement. In this paper we present an overview of new movement analysis and visualisation methodologies resulting from such an interdisciplinary research network: the European COST Action “MOVE - Knowledge Discovery from Moving Objects” (http://www.move-cost.info). This international network evolved over four years and brought together some 140 researchers from different disciplines: those that collect movement data (out of which the movement ecology was the largest represented group) and those that specialise in developing methods for analysis and visualisation of such data (represented in MOVE by computational geometry, geographic information science, visualisation and visual analytics). We present MOVE achievements and at the same time put them in ecological context by exploring relevant ecological themes to which MOVE studies do or potentially could contribute.

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“…Segmenting trajectories according to spatiotemporal criteria (e.g. Buchin et al, 2010) can reveal meaningful locations and behaviour of the moving object(s) along the trajectories (Sester et al, 2012).…”

Section: Approach For Stop Events Usage For Junction Discoverymentioning

confidence: 99%

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Zourlidou

Sester

2016

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

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ABSTRACT:The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same locationthus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

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Revealing Underlying Structure and Behaviour from Movement Data

Cited by 15 publications

References 6 publications

Identification of Similarities and Prediction of Unknown Features in an Urban Street Network

Identification of Similarities and Prediction of Unknown Features in an Urban Street Network

Analysis and visualisation of movement: an interdisciplinary review

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

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