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Special Issue "Discovery and Prediction of Moving Objects in Databases using GIS-based Tools"

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (28 February 2018) | Viewed by 53511

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Special Issue Editors

Prof. Dr. Sabine Timpf

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Guest Editor

Geoinformatics group, University of Augsburg, 86159 Augsburg, Germany
Interests: GIS; navigation; modelling moving objects; pedestrian mobility and public transport

Dr. Jean Damascène Mazimpaka
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Guest Editor

Geoinformatics group, University of Augsburg, 86159 Augsburg, Germany
Interests: movement analytics; data mining; crowd sourced geodata; spatial databases; transportation

Special Issue Information

Dear Colleagues,

Recent advances in sensor and mobile technologies have led to the availability of massive amounts of data, representing the movement of entities, such as people, vehicles, and animals. These data, referred to as movement data, reflect the characteristics of a moving object and the movement context. The extraction of useful information hidden in movement data can support movement understanding and prediction. While important research work has been done regarding the representation, management and analysis of movement data, the discovery and prediction of movement of entities and other related aspects is not well explored. Advanced methods and tools that take into account different issues associated with movement data such as a large volume, uncertainty and heterogeneity are urgently needed. Different application domains such as transportation, urban planning and ecology can benefit from such methods and tools for understanding different phenomena, monitoring their evolution, and controlling their effects.

This Special Issue seeks original research contributions in frameworks, methods and good case studies of tackling key research challenges in discovering and predicting various aspects related to moving objects. Sample topics covered include:

Knowledge discovery from movement data
Prediction of locations or trajectories of moving objects
Prediction of changes in movement context
Discovery of characteristics of moving objects (e.g., activities, transportation mode, etc.)
Machine learning methods for movement prediction
Data integration for movement understanding and prediction
Visualization of movement patterns

Prof. Dr. Sabine Timpf
Mr. Jean Damascène Mazimpaka
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Knowledge discovery
Movement prediction
Machine learning
Prediction methods
Data integration
Movement patterns

Published Papers (13 papers)

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Research

Open AccessFeature PaperArticle

Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches

and

ISPRS Int. J. Geo-Inf. 2018, 7(5), 166; https://doi.org/10.3390/ijgi7050166 - 27 Apr 2018

Cited by 7 | Viewed by 3743

Abstract

For next place prediction, machine learning methods which incorporate contextual data are frequently used. However, previous studies often do not allow deriving generalizable methodological recommendations, since they use different datasets, methods for discretizing space, scales of prediction, prediction algorithms, and context data, and therefore lack comparability. Additionally, the cold start problem for new users is an issue. In this study, we predict next places based on one trajectory dataset but with systematically varying prediction algorithms, methods for space discretization, scales of prediction (based on a novel hierarchical approach), and incorporated context data. This allows to evaluate the relative influence of these factors on the overall prediction accuracy. Moreover, in order to tackle the cold start problem prevalent in recommender and prediction systems, we test the effect of training the predictor on all users instead of each individual one. We find that the prediction accuracy shows a varying dependency on the method of space discretization and the incorporated contextual factors at different spatial scales. Moreover, our user-independent approach reaches a prediction accuracy of around 75%, and is therefore an alternative to existing user-specific models. This research provides valuable insights into the individual and combinatory effects of model parameters and algorithms on the next place prediction accuracy. The results presented in this paper can be used to determine the influence of various contextual factors and to help researchers building more accurate prediction models. It is also a starting point for future work creating a comprehensive framework to guide the building of prediction models. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

An Efficient Shortest Path Routing Algorithm for Directed Indoor Environments

Sultan Alamri

ISPRS Int. J. Geo-Inf. 2018, 7(4), 133; https://doi.org/10.3390/ijgi7040133 - 26 Mar 2018

Cited by 8 | Viewed by 3479

Abstract

Routing systems for outdoor space have become the focus of many research works. Such routing systems are based on spatial road networks where moving objects (such as cars) are affected by the directed roads and the movement of traffic, which may include traffic jams. Indoor routing, on the other hand, must take into account the features of indoor space such as walls and rooms. In this paper, we take indoor routing in a new direction whereby we consider the features that a building has in common with outdoor spaces. Inside some buildings, there may be directed floors where moving objects must move in a certain direction through directed corridors in order to reach a certain location. For example, on train platforms or in museums, movement in the corridors may be directed. In these directed floor spaces, a routing system enabling a visitor to take the shortest path to a certain location is essential. Therefore, this work proposes a new approach for buildings with directed indoor spaces, where each room can be affected by the density of the moving objects. The proposed system obtains the shortest path between objects or rooms taking into consideration the directed indoor space and the capacity of the objects to move within each room/cell. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data

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ISPRS Int. J. Geo-Inf. 2018, 7(2), 78; https://doi.org/10.3390/ijgi7020078 - 24 Feb 2018

Cited by 12 | Viewed by 3911

Abstract

We present a methodology for distinguishing between three types of animal movement behavior (foraging, resting, and walking) based on high-frequency tracking data. For each animal we quantify an individual movement path. A movement path is a temporal sequence consisting of the steps through space taken by an animal. By selecting a set of appropriate movement parameters, we develop a method to assess movement behavioral states, reflected by changes in the movement parameters. The two fundamental tasks of our study are segmentation and clustering. By segmentation, we mean the partitioning of the trajectory into segments, which are homogeneous in terms of their movement parameters. By clustering, we mean grouping similar segments together according to their estimated movement parameters. The proposed method is evaluated using field observations (done by humans) of movement behavior. We found that on average, our method agreed with the observational data (ground truth) at a level of 80.75% ± 5.9% (SE). Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

Rouzbeh Forouzandeh Jonaghani

Sepehr Honarparvar

and

Navid Khademi

ISPRS Int. J. Geo-Inf. 2018, 7(1), 35; https://doi.org/10.3390/ijgi7010035 - 22 Jan 2018

Viewed by 3273

Abstract

In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc.), the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

A Hybrid Approach Combining the Multi-Temporal Scale Spatio-Temporal Network with the Continuous Triangular Model for Exploring Dynamic Interactions in Movement Data: A Case Study of Football

Pengdong Zhang

Jasper Beernaerts

and

Nico Van de Weghe

ISPRS Int. J. Geo-Inf. 2018, 7(1), 31; https://doi.org/10.3390/ijgi7010031 - 20 Jan 2018

Cited by 4 | Viewed by 4087

Abstract

Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN) and the continuous triangular model (CTM) for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC) is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness) are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1) exploring the interaction intensities between any two individuals; (2) exploring the interaction intensities among multiple individuals, and (3) exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data

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ISPRS Int. J. Geo-Inf. 2018, 7(1), 25; https://doi.org/10.3390/ijgi7010025 - 12 Jan 2018

Cited by 71 | Viewed by 6005

Abstract

Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source–destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Multilevel Visualization of Travelogue Trajectory Data

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ISPRS Int. J. Geo-Inf. 2018, 7(1), 12; https://doi.org/10.3390/ijgi7010012 - 03 Jan 2018

Cited by 10 | Viewed by 3954

Abstract

User-generated travelogues can generate much geographic data, containing abundant semantic and geographic information that reflects people’s movement patterns. The tourist movement patterns in travelogues can help others when planning trips, or understanding how people travel within certain regions. The trajectory data in travelogues might include tourist attractions, restaurants and other locations. In addition, all travelogues generate a trajectory, which has a large volume. The variety and volume of trajectory data make it very hard to directly find patterns contained within them. Moreover, existing work about movement patterns has only explored the simple semantic information, without considering using visualization to find hidden information. We propose a multilevel visual analytical method to help find movement patterns in travelogues. The data characteristic of a single travelogue are different from multiple travelogues. When exploring a single travelogue, the individual movement patterns comprise our main concern, like semantic information. While looking at many travelogues, we focus more on the patterns of population movement. In addition, when choosing the levels for multilevel aggregation, we apply an adaptive method. By combining the multilevel visualization in a single travelogue and multiple travelogues, we can better explore the movement patterns in travelogues. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

GIS-Based Evaluation of Spatial Interactions by Geographic Disproportionality of Industrial Diversity

Jemyung Lee

and

Satoshi Hoshino

ISPRS Int. J. Geo-Inf. 2017, 6(11), 352; https://doi.org/10.3390/ijgi6110352 - 08 Nov 2017

Cited by 2 | Viewed by 4179

Abstract

Diversity of regional industry is regarded as a key factor for regional development, as it has a positive relationship with economic stability, which attracts population. This paper focuses on how the spatial imbalance of industrial diversity contributes to the population change caused by inter-regional migration. This paper introduces a spatial interaction model for the Geographic Information System (GIS)-based simulation of the spatial interactions to evaluate the demographic attraction force. The proposed model adopts the notions of gravity, entropy, and virtual work. An industrial classification by profit level is introduced and its diversity is quantified with the entropy of information theory. The introduced model is applied to the cases of 207 regions in South Korea. Spatial interactions are simulated with an optimized model and their resultant forces, the demographic attraction forces, are compared with observed net migration for verification. The results show that the evaluated attraction forces from industrial diversity have a very significant, positive, and moderate relationship with net migration, while other conventional factors of industry, population, economy, and the job market do not. This paper concludes that the geographical quality of industrial diversity has positive and significant effects on population change by migration. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Detection of Moving Ships in Sequences of Remote Sensing Images

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ISPRS Int. J. Geo-Inf. 2017, 6(11), 334; https://doi.org/10.3390/ijgi6110334 - 01 Nov 2017

Cited by 6 | Viewed by 3063

Abstract

High-speed agile remote sensing satellites have the ability to capture multiple sequences of images. However, the frame rate is lower and the baseline between each image is much longer than normal image sequences. As a result, the edges and shadows in each image in the sequence vary considerably. Therefore, more requirements are placed on the target detection algorithm. Aiming at the characteristics of multi-view image sequences, we propose an approach to detect moving ships on the water surface. Based on marker controlled watershed segmentation, we use the extracted foreground and background images to segment moving ships, and we obtain the complete shape and texture information of the ships. The inter-frame difference algorithm is applied to extract the foreground object information, while Otsu’s algorithm is used to extract the image background. The foreground and background information is fused to solve the problem of interference with object detection caused by long imaging baseline. The experimental results show that the proposed method is effective for moving ship detection. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Simplifying GPS Trajectory Data with Enhanced Spatial-Temporal Constraints

Haizhong Qian

and

Yongmei Lu

ISPRS Int. J. Geo-Inf. 2017, 6(11), 329; https://doi.org/10.3390/ijgi6110329 - 30 Oct 2017

Cited by 15 | Viewed by 4560

Abstract

Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may be used to simplify GPS trajectories. However, traditional algorithms focus on geometric characteristics of linear features. Trajectory data may record information beyond location. Examples include time and elevation, and inferred information such as speed, transportation mode, and activities. Effective trajectory simplification should preserve these characteristics in addition to location and orientation of spatial-temporal movement. This paper proposes an Enhanced Douglas–Peucker (EDP) algorithm that implements a set of Enhanced Spatial-Temporal Constraints (ESTC) when simplifying trajectory data. These constraints ensure that the essential properties of a trajectory be preserved through preserving critical points. Further, this study argues that speed profile can uniquely identify a trajectory and thus it can be used to evaluate the effectiveness of a trajectory simplification. The proposed ESTC-EDP simplification method is applied to two examples of GPS trajectory. The results of trajectory simplification are reported and compared with that from traditional DP algorithm. The effectiveness of simplification is evaluated. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

An Luo

Shenghua Chen

and

Bin Xv

ISPRS Int. J. Geo-Inf. 2017, 6(11), 327; https://doi.org/10.3390/ijgi6110327 - 30 Oct 2017

Cited by 28 | Viewed by 6252

Abstract

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data. Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

Development of a Safety Index to Identify Differences in Safety Performance by Postal Delivery Motorcyclists Based either in Different Regional Post Offices or within the Same Regional Office

Junghee Jo

and

Hyunsuk Kim

ISPRS Int. J. Geo-Inf. 2017, 6(11), 324; https://doi.org/10.3390/ijgi6110324 - 27 Oct 2017

Cited by 4 | Viewed by 3575

Abstract

Postal motorcyclists who regularly conduct deliveries are particularly vulnerable to road accidents since they are exposed to traffic throughout their work day. To reduce accident rates, safety officers in each of the local delivery offices alert postmen of any hazardous conditions that may be conducive to accidents. Although some commercial postal organizations already use tracking technologies (e.g., GPS), Korea Post currently has no systematic way to collect their postmen’s driving behavior except by referring to each postman’s manually recorded daily mileage. In light of this, we developed a safety index (SI) for quantifying and analyzing individual postal motorcyclists’ safety performance based on their driving behavior and work environment. Each postman’s work environment varies from post office to post office and postman to postman depending on delivery conditions. After creating a GPS based system that can be installed on personal digital assistants (PDAs) that are already used by postmen throughout their shifts, we conducted two phases of field tests during a two-year period involving postmen working in different demographic areas. Using the collected field data, we validated our developed SI and analyzed whether there were any differences in the safety performance among postal motorcyclists working in different regional post offices or within the same regional post office. We found that the safety performance of postal motorcyclists working in different regional delivery offices varied depending on the regional characteristics of the local delivery office (e.g., densely distributed delivery points vs. loosely distributed delivery points). We also found that the safety performance of postal motorcyclists working in the same regional post office varied depending on the specific circumstances of each delivery area (e.g., short commuting routes of the postman responsible for downtown vs. long commuting routes of the postman responsible for a suburb). Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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Open AccessArticle

The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks

HaRim Jung

and

Ung-Mo Kim

ISPRS Int. J. Geo-Inf. 2017, 6(11), 322; https://doi.org/10.3390/ijgi6110322 - 26 Oct 2017

Cited by 2 | Viewed by 2695

Abstract

This paper addresses the problem of processing range monitoring queries, each of which continuously retrieves moving objects that are currently located within a given query range. In particular, this paper focuses on processing range monitoring queries in the road network, where movements of the objects are constrained by a predefined set of paths. One of the most important challenges of processing range monitoring queries is how to minimize the wireless communication cost and the server computation cost, both of which are heavily dependent on the amount of location-update stream generated by moving objects. The traditional centralized methods for range monitoring queries assume that moving objects periodically send location-updates to the server. However, when the number of moving objects becomes increasingly large, such an assumption may no longer be acceptable because the amount of location-update stream becomes enormous. Recently, some distributed methods have been proposed, where moving objects utilize their available computational capabilities for sending location-updates to the server only when necessary. Unfortunately, the existing distributed methods only deal with the objects moving in Euclidean space, and thus they cannot be extended to processing range monitoring queries over the objects moving along the road network. In this paper, we propose the distributed method for processing range monitoring queries in the road network. To utilize the computational capabilities of moving objects, we introduce the concept of vicinity region. A vicinity region, assigned to each moving object o, makes o monitor whether or not it should be included in the results of nearby queries. The proposed method includes (i) a new spatial index structure, called the Segment-based Space Partitioning tree (SSP-tree) whose role is to efficiently search the appropriate vicinity regions for moving objects based on their heterogeneous computational capabilities and (ii) the details of the communication strategy between the server and moving objects, which significantly reduce the wireless communication cost as well as the server computation cost. Through simulations, we verify the effectiveness for processing range monitoring queries over a large number of moving objects (up to 100,000) in the road network (modeled as an undirected graph). Full article

(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)

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