The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

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Trajectory Data Mining: An Overview

Future Directions -- Semi-supervised Multiple Classifier Systems: Background and Research Directions -- Boosting -- Boosting GMM and Its Two Applications -- Boosting Soft-Margin SVM with Feature Selection for Pedestrian Detection -- Observations on Boosting Feature Selection -- Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis -- Combination Methods -- Decoding Rules for Error Correcting Output Code Ensembles -- A Probability Model for Combining Ranks -- EER of Fixed and Trainable Fusion Classifiers: A Theoretical Study with Application to Biometric Authentication Tasks -- Mixture of Gaussian Processes for Combining Multiple Modalities -- Dynamic Classifier Integration Method -- Recursive ECOC for Microarray Data Classification -- Using Dempster-Shafer Theory in MCF Systems to Reject Samples -- Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers -- On Deriving the Second-Stage Training Set for Trainable Combiners -- Using Independence Assumption to Improve Multimodal Biometric Fusion -- Design Methods -- Half-Against-Half Multi-class Support Vector Machines -- Combining Feature Subsets in Feature Selection -- ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments -- Using Decision Tree Models and Diversity Measures in the Selection of Ensemble Classification Models -- Ensembles of Classifiers from Spatially Disjoint Data -- Optimising Two-Stage Recognition Systems -- Design of Multiple Classifier Systems for Time Series Data -- Ensemble Learning with Biased Classifiers: The Triskel Algorithm -- Cluster-Based Cumulative Ensembles -- Ensemble of SVMs for Incremental Learning -- Performance Analysis -- Design of a New Classifier Simulator -- Evaluation of Diversity Measures for Binary Classifier Ensembles -- Which Is the Best Multiclass SVM Method? An Empirical Study -- Over-Fitting in Ensembles of Neural Network Classifiers Within ECOC Frameworks -- Between Two Extremes: Examining Decompositions of the Ensemble Objective Function -- Data Partitioning Evaluation Measures for Classifier Ensembles -- Dynamics of Variance Reduction in Bagging and Other Techniques Based on Randomisation -- Ensemble Confidence Estimates Posterior Probability -- Applications -- Using Domain Knowledge in the Random Subspace Method: Application to the Classification of Biomedical Spectra -- An Abnormal ECG Beat Detection Approach for Long-Term Monitoring of Heart Patients Based on Hybrid Kernel Machine Ensemble -- Speaker Verification Using Adapted User-Dependent Multilevel Fusion -- Multi-modal Person Recognition for Vehicular Applications -- Using an Ensemble of Classifiers to Audit a Production Classifier -- Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance -- Combining Audio-Based and Video-Based Shot Classification Systems for News Videos Segmentation -- Designing Multiple Classifier Systems for Face Recognition -- Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data.

Multiple Classifier Systems

With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.

Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. In this context, it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model. Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way. In this paper, we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics. In addition, we present challenges and possible research directions for each mainstream approach of ensemble learning, and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning, reinforcement learning, etc.

A survey on ensemble learning

Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.

Knowledge-Based Systems

GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about the city dynamics and human behaviors. In this paper, we aim to discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi driving frauds or road network change in modern cites. To achieve the objective, firstly we group all the taxi trajectories crossing the same source destination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an Isolation-Based Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC>0.99, over 90% detection rate at false alarm rate of less than 2%). Finally, we demonstrate the potential of iBAT in enabling innovative applications by using it for taxi driving fraud detection and road network change detection.

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iBAT: detecting anomalous taxi trajectories from GPS traces

Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose an online method that is able to detect anomalous trajectories “on-the-fly” and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43 800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) ≥ 0.99.

iBOAT: Isolation-Based Online Anomalous Trajectory Detection

Diversity among individual classifiers is recognized to play a key role in ensemble, however, few theoretical properties are known for classification. In this paper, by focusing on the popular ensemble pruning setting (i.e., combining classifier by voting and measuring diversity in pairwise manner), we present a theoretical study on the effect of diversity on the generalization performance of voting in the PAC-learning framework. It is disclosed that the diversity is closely-related to the hypothesis space complexity, and encouraging diversity can be regarded to apply regularization on ensemble methods. Guided by this analysis, we apply explicit diversity regularization to ensemble pruning, and propose the Diversity Regularized Ensemble Pruning (DREP) method. Experimental results show the effectiveness of DREP.

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Diversity regularized ensemble pruning

Taxi GPS traces can inform us the human mobility patterns in modern cities. Instead of leveraging the costly and inaccurate human surveys about people's mobility, we intend to explore the night bus route planning issue by using taxi GPS traces. Specifically, we propose a two-phase approach for bidirectional night bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick up/drop off and then propose effective methods to split big hot areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops, and bus operation time constraints, we derive several effective rules to build the bus route graph and prune invalid stops and edges iteratively. Based on this graph, we further develop a bidirectional probability-based spreading algorithm to generate candidate bus routes automatically. We finally select the best bidirectional bus route, which expects the maximum number of passengers under the given conditions and constraints. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set, which contains more than 1.57 million night passenger delivery trips, generated by 7600 taxis in a month.

B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces

Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning issue by leveraging taxi GPS traces. In this paper, we propose a two-phase approach based on the crowd-sourced GPS data for night-bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick-up/drop-off, and then propose effective methods to split big “hot” areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops as well as bus operation time constraints, we derive several effective rules to build bus routing graph and prune the invalid stops and edges iteratively. We further develop two heuristic algorithms to automatically generate candidate bus routes, and finally we select the best route which expects the maximum number of passengers under the given conditions. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set which contains more than 1.57 million passenger delivery trips, generated by 7,600 taxis for a month in Hangzhou, China.

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Nan Li

Papers

iBAT: detecting anomalous taxi trajectories from GPS traces

iBOAT: Isolation-Based Online Anomalous Trajectory Detection

Diversity regularized ensemble pruning

B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces

B-Planner: Night bus route planning using large-scale taxi GPS traces