Modeling Motion Relations for Moving Objects on Road Networks

Hornsby, Kathleen; King, Kraig

doi:10.1007/s10707-007-0039-7

Cited by 16 publications

(1 citation statement)

References 45 publications

(43 reference statements)

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“…The generation of large volumes of tracking data is currently being enthusiastically welcomed in the field of GIScience. Ever smaller and cheaper localization devices allow the recording of the spatio‐temporal footprint of, for example, cars (Stewart Hornsby and King 2008), motorcycle couriers (Turner 2009), pedestrians (van der Spek 2008), shopping carts (Larson et al. 2005), as well as wild (Cagnacci et al.…”

Section: State Of the Artmentioning

confidence: 99%

How fast is a cow? Cross‐Scale Analysis of Movement Data

Laube

Purves

2011

Transactions in GIS

111

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Data representing the trajectories of moving point objects are becoming increasingly ubiquitous in GIScience, and are the focus of much methodological research aimed at extracting patterns and meaning describing the underlying phenomena. However, current research within GIScience in this area has largely ignored issues related to scale and granularity – in other words how much are the patterns that we see a function of the size of the looking glass that we apply? In this article we investigate the implications of varying the temporal scale at which three movement parameters, speed, sinuosity and turning angle are derived, and explore the relationship between this temporal scale and uncertainty in the individual data points making up a trajectory. A very rich dataset, representing the movement of 10 cows over some two days every 0.25 s is investigated. Our cross‐scale analysis shows firstly, that movement parameters for all 10 cows are broadly similar over a range of scales when the data are segmented to remove quasi‐static subtrajectories. However, by exploring realistic values of GPS uncertainty using Monte Carlo Simulation, it becomes apparent that fine scale measurement of all movement parameters is masked by uncertainties, and that we can only make meaningful statements about movement when we take these uncertainties into account.

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Section: State Of the Artmentioning

confidence: 99%

How fast is a cow? Cross‐Scale Analysis of Movement Data

Laube

Purves

2011

Transactions in GIS

111

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A Fuzzy Spatio-temporal-Based Approach for Activity Recognition

Yaouanc

Poli

2012

Lecture Notes in Computer Science

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Abstract. Over the last decade, there has been a significant deployment of systems dedicated to surveillance. These systems make use of real-time sensors that generate continuous streams of data. Despite their success in many cases, the increased number of sensors leads to a cognitive overload for the operator in charge of their analysis. However, the context and the application requires an ability to react in real-time. The research presented in this paper introduces a spatio-temporal-based approach the objective of which is to provide a qualitative interpretation of the behavior of an entity (e.g., a human or vehicle). The process is formally supported by a fuzzy logic-based approach, and designed in order to be as generic as possible.

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Managing sensor traffic data and forecasting unusual behaviour propagation

et al. 2010

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Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. This scenario presents several research challenges, in spatio-temporal data management and data analysis. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and data management strategies to query these data. The first aspect is geared towards supporting pattern matching. This leads to a model to study and predict unusual traffic behavior along an urban road network. The second aspect deals with spatio-temporal database issues, taking into account information 280 Geoinformatica (2010) 14:279-305 produced by the model. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city.

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Modeling Motion Relations for Moving Objects on Road Networks

Cited by 16 publications

References 45 publications

How fast is a cow? Cross‐Scale Analysis of Movement Data

How fast is a cow? Cross‐Scale Analysis of Movement Data

A Fuzzy Spatio-temporal-Based Approach for Activity Recognition

Managing sensor traffic data and forecasting unusual behaviour propagation

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