Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time. Finally, we outline the essential qualities that a trajectory data management system should possess in order to maximize flexibility.

pdf/a-survey-on-trajectory-data-management-analytics-and-152rfwv0pr.pdf

A Survey on Trajectory Data Management, Analytics, and Learning

Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time. Finally, we outline the essential qualities that a trajectory data management system should possess to maximize flexibility.

In this paper, we study the problem of large-scale trajectory data clustering, k-paths, which aims to efficiently identify k "representative" paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters, k-paths can be used for visual exploration in applications such as traffic monitoring, public transit planning, and site selection. By combining map matching with an efficient intermediate representation of trajectories and a novel edge-based distance (EBD) measure, we present a scalable clustering method to solve k-paths. Experiments verify that we can cluster millions of taxi trajectories in less than one minute, achieving improvements of up to two orders of magnitude over state-of-the-art solutions that solve similar trajectory clustering problems.

/pdf/fast-large-scale-trajectory-clustering-5797kndh5s.pdf

Fast large-scale trajectory clustering

MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include machine learning, and spatial processing. In meantime, huge volumes of spatio-temporal trajectory data are coming from different sources over sometime, raised the demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in trajectory data processing. This work describes Summit; a full-fledged MapReduce framework with native support for trajectory data.

Summit: a scalable system for massive trajectory data management

Despite two decades of research in moving object databases and a few research prototypes that have been proposed, there is not yet a mainstream system targeted for industrial use. In this article, we present MobilityDB, a moving object database that extends the type system of PostgreSQL and PostGIS with abstract data types for representing moving object data. The types are fully integrated into the platform to reuse its powerful data management features. Furthermore, MobilityDB builds on existing operations, indexing, aggregation, and optimization framework. This is all made accessible via the SQL query interface.

/pdf/mobilitydb-a-mobility-database-based-on-postgresql-and-527hvsfnz5.pdf

MobilityDB: A Mobility Database Based on PostgreSQL and PostGIS

Massive trajectory data is being generated by GPS-equipped devices, such as cars and mobile phones, which is used increasingly in transportation, location-based services, and urban computing. As a result, a variety of methods have been proposed for trajectory data management and analytics. However, traditional systems and methods are usually designed for very specific data management or analytics needs, which forces users to stitch together heterogeneous systems to analyze trajectory data in an inefficient manner. Targeting the overall data pipeline of big trajectory data management and analytics, we present a unified platform, termed as UlTraMan. In order to achieve scalability, efficiency, persistence, and flexibility, (i) we extend Apache Spark with respect to both data storage and computing by seamlessly integrating a key-value store, and (ii) we enhance the MapReduce paradigm to allow flexible optimizations based on random data access. We study the resulting system's flexibility using case studies on data retrieval, aggregation analyses, and pattern mining. Extensive experiments on real and synthetic trajectory data are reported to offer insight into the scalability and performance of UlTraMan.

/pdf/ultraman-a-unified-platform-for-big-trajectory-data-573xmi54gm.pdf

UlTraMan: a unified platform for big trajectory data management and analytics

The invention discloses a personalized recommendation method for a nearest neighbor query based on big trajectory data In the method, an efficient storage and index structure is designed for the nearest neighbor query based on the big trajectory data to process the big trajectory data The method comprises the steps of firstly carrying out extraction, noise reduction, conversion and storage processing on the big trajectory data; then establishing an overall R-tree index and a local R-tree index for the stored trajectory data; establishing a trajectory serial number set-based index and a trajectory number-based index for each sub-region; and when a user submits a query, visiting the index structure, carrying out the nearest neighbor query based on the big trajectory data so as to provide personalized recommendation service The method satisfies the demand of the nearest neighbor query of a trajectory in a big data environment well, greatly improves the processing efficiency of the nearest neighbor query based on the big trajectory data and provides the optimal performance

Personalized recommendation method for nearest neighbor query based on big trajectory data

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UlTraMan: a unified platform for big trajectory data management and analytics

Personalized recommendation method for nearest neighbor query based on big trajectory data