Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context.

/pdf/a-conceptual-view-on-trajectories-k8d3970n1n.pdf

A conceptual view on trajectories

The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.

/pdf/building-real-world-trajectory-warehouses-11hob03pqz.pdf

Building real-world trajectory warehouses

Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the literature, including statistical models, shallow machine learning models, and deep learning models. Recently, graph neural networks (GNNs) have emerged as state-of-the-art traffic forecasting solutions because they are well suited for traffic systems with graph structures. This survey aims to introduce the research progress on graph neural networks for traffic forecasting and the research trends observed from the most recent studies. Furthermore, this survey summarizes the latest open-source datasets and code resources for sharing with the research community. Finally, research challenges and opportunities are proposed to inspire follow-up research.

/pdf/graph-neural-network-for-traffic-forecasting-the-research-27jfukla.pdf

Graph Neural Network for Traffic Forecasting: The Research Progress

Data Warehouses are the core of the modern systems for decision making They store integrated information extracted from various and heterogeneous data sources, making it available in multidimensional form for analyses aimed at improving the users’ knowledge of their business Though the first use of the term dates back to the 80s, only during the late 90s data warehousing has emerged as a research area on its own, though in strict correlation with several other research topics as database integration, view materialization, data visualization, etc This paper surveys more than 20 years of research on data warehouse systems, from their early relational implementations (still widely adopted in corporate environments), to the new architectures solicited by Business Intelligence 20 scenarios during the last decade, and up to the exciting challenges now posed by the integration with big data settings The timeline of research is organized into three interrelated tracks: techniques, architectures, and methodologies

From Star Schemas to Big Data: 20+ Years of Data Warehouse Research

In this paper we are interested in storing and perform OLAP queries about various aggregate trajectory properties. We consider a data stream en- vironment where a set of mobile objects send the data about its location in a irregular and unbounded way, the data volume is stored in a centralized and traditional DW with pre-computed aggregations values (preserving the trajec- tories privacy). We present an application developed to receive the stream data set, to store and compute the pre-aggregation values and to present final results in order to reveal the knowledge about the trajectories.

Trajectory Data Warehouses: Proposal of Design and Application to Exploit Data

In this paper we discuss how data warehousing technology can be used to store aggregate information about trajectories and perform OLAP operations over them. To this end, we define a data cube with spatial and temporal dimensions, discretized according to a regular grid. We investigate in depth some issues related to the computation of a holistic aggregate function, i.e, the presence, which returns the number of distinct trajectories occurring in a given spatio-temporal area. In particular, we introduce a novel way to compute an approximate, but nevertheless very accurate, presence aggregate function, which uses only a bounded amount of measures stored in the base cells of our cuboid. We also concentrate on the loading phase of our data warehouse, which has to deal with an unbounded stream of trajectory observations. We suggest how the complexity of this phase can be reduced, and we analyse the errors that this procedure induces at the level of the sub-aggregates stored in the base cells. These errors and the accuracy of our approximate aggregate functions are carefully evaluated by means of tests performed on synthetic trajectory datasets.

Approximate Aggregations in Trajectory Data Warehouses

Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.

/pdf/road-traffic-forecast-based-on-meteorological-information-1ol3y4bq.pdf

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

Designing a Spatio-Temporal Trajectory Data Warehouse

The widespread diffusion of modern technologies such as low-cost sensors, wireless, ubiquitous and location-aware mobile devices, allows one to collect an overwhelming amount of data about trajectories of moving objects. Such data are usually produced at different rates, and arrive in streams in an unpredictable and unbounded way. In this paper we discuss how standard data warehousing tools can be used to store trajectories and to compute OLAP operations over them. We construct a data cube where the dimensions are the spatial coordinates and the time, discretized according to a regular grid. The measures of interest are the number of objects and the distance travelled by such objects for any (spatio-temporal) cell of the data cube. According to the way trajectories are represented in the data warehouse the measures can be possibly affected by errors. We discuss how such errors can be influenced by the diverse spatio-temporal granularities vs. the speeds and sampling rates of the moving objects, and we study the accuracy of the computed measures vs. the amount of pre-processing of raw data. Finally, we analyze whether the usual OLAP aggregation operations, like sum and average, may introduce further errors when rolling-up.

https://www.unive.it/media/allegato/dipartimenti/informatica/Ricerca/RapportiTecnici/CS-2006-4.pdf

Towards a Trajectory Data Warehouse

Monitoring road traffic is extremely important given the possibilities it opens up in terms of studying the behavior of road users, road design and planning problems, as well as because it can be used to predict future traffic. Especially on highways that connect beaches and larger urban areas, traffic is characterized by having peaks that are highly dependent on weather conditions and rest periods. This paper describes a dataset of mobility patterns of a coastal area in Aveiro region, Portugal, fully covered with traffic classification radars, over a two-year period. The sensing infrastructure was deployed in the scope of the PASMO project, an open living lab for co-operative intelligent transportation systems. The data gathered includes the speed of the detected objects, their position, and their type (heavy vehicle, light vehicle, two-wheeler, and pedestrian). The dataset includes 74,305 records, corresponding to the aggregation of road information at 10 min intervals. A brief analysis of the dataset shows the highly dynamic nature of traffic during the two-year period. In addition, the existence of meteorological records from nearby stations, and the recording of daily data on COVID-19 infections, make it possible to cross-reference information and study the influence of weather conditions and infections on traffic behavior.

/pdf/dataset-mobility-patterns-of-a-coastal-area-using-traffic-1ygzhv7r.pdf

Dataset: Mobility Patterns of a Coastal Area Using Traffic Classification Radars

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