Data Mining - Concepts and Techniques.

Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes.

A Hybrid Prediction Model for Moving Objects

Recently, the phenomenal advent of photo-sharing services, such as Flickr and Panoramio, have led to volumous community-contributed photos with text tags, timestamps, and geographic references on the Internet. The photos, together with their time- and geo-references, become the digital footprints of photo takers and implicitly document their spatiotemporal movements. This study aims to leverage the wealth of these enriched online photos to analyze people’s travel patterns at the local level of a tour destination. Specifically, we focus our analysis on two aspects: (1) tourist movement patterns in relation to the regions of attractions (RoA), and (2) topological characteristics of travel routes by different tourists. To do so, we first build a statistically reliable database of travel paths from a noisy pool of community-contributed geotagged photos on the Internet. We then investigate the tourist traffic flow among different RoAs by exploiting the Markov chain model. Finally, the topological characteristics of travel routes are analyzed by performing a sequence clustering on tour routes. Testings on four major cities demonstrate promising results of the proposed system.

Mining Travel Patterns from Geotagged Photos

Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern `one object is leading others', which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.

https://www.geo.uzh.ch/~plaube/pubs/anderssonEtal_GeoI08_preVersion.pdf

Reporting Leaders and Followers among Trajectories of Moving Point Objects

With the recent progress of spatial information technologies and mobile computing technologies, spatio-temporal databases which store information on moving objects including vehicles and mobile users have gained a lot of research in- terests. In this paper, we propose an algorithm to extract mobility statistics from indexed spatio-temporal datasets for the interactive analysis of huge collections of moving object trajectories. We focus on a mobility statistics value called the Markov transition probability, which is based on a cell-based organization of a target space and the Markov chain model. The pro- posed algorithm efficiently computes the specified Markov transition probabilities with the help of a spatial index R-tree. We reduce the statistics computation task to a kind of constraint satisfaction problem that uses a spatial index, and utilize internal

/pdf/extracting-mobility-statistics-from-indexed-spatio-temporal-1dzd04ie0h.pdf

Extracting Mobility Statistics from Indexed Spatio-Temporal Datasets

Rapid development and ongoing research activities on mobile devices, digital cartography, and global positioning systems (GPSs) have brought us a new type of software service - location-based services for moving objects (such as people with mobile devices and vehicles with car navigation systems). Realization of location-based services requires new technologies to provide appropriate neighborhood information to moving objects. A general approach to providing neighborhood information to moving objects is to retrieve objects in the neighborhood of a moving object with a spatial query that uses the traditional Euclidean distance. However, if we know the destination and the estimated route of a moving object, we would be able to provide more appropriate information to the object. Based on this idea, we have developed adaptive spatial query generation models that take the trajectory of a moving object into consideration to retrieve desired information. In this paper, we describe the design and implementation of the neighborhood information retrieval system based on the models and evaluate its effectiveness with experiments.

Implementation and evaluation of an adaptive neighborhood information retrieval system for mobile users

あらまし 近年,地図情報の電子化や位置計測技術の発展,携帯端末などの普及を受けて,携帯端末をもって 移動する利用者やナビゲーション機器を備えた自動車などの移動オブジェクトに対し,移動状況に即した周辺情 報の提供を行う手法の開発が求められている.一般に,移動オブジェクトに周辺情報を提供する場合,移動体の 現在位置を中心として決められた範囲を検索をすることが考えられる.しかし,これまで通ってきた経路やこれ から通る予定経路が明らかであれば,それらの情報を利用してより適切な情報が提供できる余地がある.そこで 我々の研究グループでは,移動経路や移動速度などを考慮して移動オブジェクトに対して情報を提供するための 空間データベース検索モデルの開発を行い,提案した検索モデルに基づく移動オブジェクトに対する周辺情報提 供モデルを,商用の GISソフトウェア上で実装した.本論文はこのシステムの設計について述べ,実験をもとに その有用性について評価する. キーワード 移動オブジェクト,周辺情報提供,空間データベース,空間問合せ,楕円体距離

Yuichi Tsukamoto

Papers

Extracting Mobility Statistics from Indexed Spatio-Temporal Datasets

Implementation and evaluation of an adaptive neighborhood information retrieval system for mobile users

Development and Evaluation of a Spatial Database Retrieval System to Provide Neighborhood Information to Moving Objects