The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, trafic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect trajectories, the community sufers from a general lack of publicly available datasets. This is due to privacy concerns that make it dificult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to diferent types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-Builder, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-Builder has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities. The running example provided towards the end of the paper highlights how MAT-Builder's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature [
One might wonder which benefits of having such extremely enriched trajectories can provide. First, we observe that enriched trajectories may allow to better understand object movements – for instance, animal behaviour analysis that uses enriched trajectories may allow to better understand how environmental factors impact animal movements. Secondly, the availability of contextual features can help in the prediction or recommendation of itineraries – for instance, a tourist recommending system may take advantage of contextual information to better tailor personalized suggestions.
While there are already contributions to the modelling and analysis of semantic trajectories
and multiple aspects trajectories [
In this work we aim to address the above challenges and issues by introducing MAT-Builder, an interactive system that supports practitioners during the whole trajectory semantic enrichment process and that enables users to generate datasets of multiple aspects trajectories.
The main novel contributions of this work are as follows: (1) we provide a system that supports
the user in the complex process of building multiple aspect trajectories from heterogeneous
data sources and with diferent semantic aspects, (2) this system is able to extract, combine,
and build enriching information from a variety of external data sources (e.g., OpenStreetMap2),
and (3) it is designed with modularity and extensibility in mind, thus allowing developers to
add new aspects, external data sources, and functionalities. It is important to point out that we
designed MAT-Builder to reuse functionalities provided by existing mobility data management
libraries, including some of those that we already mentioned and referred above, i.e., GeoPandas,
Scikit-mobility [
The rest of the paper is organized as follows. In Section 2 we present some fundamental
notions which will be then used in the subsequent sections. Section 3 presents the modular
architecture of MAT-Builder. Section 4 presents a running example highlighting the main
features and benefits that our system brings to the mobility data analysis community. Finally,
Section 5 draws some final conclusions. The full version of this paper has been published as a
demo paper in [
In this section we introduce some fundamental notions that will be used throughout the rest
of the paper. We define a raw trajectory to be a sequence of time-stamped spatial coordinates
representing the movement of an object. We define an enriched trajectory to be a raw trajectory
enriched with semantic information. A multiple aspect trajectory is an enriched trajectory
with multiple types, or aspects, of semantic information (e.g., weather, transportation means,
POIs) [
The MAT-Builder system is written in Python and is made up of a user interface (UI) and,
the core component of the system, the modular backend3. The UI exposes the MAT-Builder’s
functionalities to the users, and translates the users’ needs into queries that are then processed
by the backend. We postpone the UI illustration to the running example (Section 4). The backend
is the core component of our system as it represents the MAT-Builder’s query processing
engine. Following the process described in [
It is important to highlight that the backend is designed to be extensible with new modules, thus allowing developers to provide additional functionalities (e.g., additional aspects or management of further external sources) to the system. Indeed, every module in the system must extend a common interface that specifies the methods that every module should implement in order to be integrated and used within the system. More precisely, the interface requires every module to implement the following methods: (1) one that sets in the UI its input parameters, (2) one that executes the module operations according to the input parameters, and (3) one that specifies how the results should be provided via the UI. Note that any new module may also be built on top of preexisting modules via subclassing. Finally, our system provides data workflow management capabilities that let the user specify which modules shall be used among those available, and the order in which these shall be executed.
In the following we discuss the general goals of each backend module, and provide some details concerning their current implementation. Furthermore, we explain how the modules are connected to each other through intermediate saved results.
3Upon acceptance of the paper we will provide a github link to our software and the datasets we used.
Raw trajectories
Systematic stop
Multiple-Aspect Trajectories
Occasional stop
enrichment Systematic stop enrichment
Moves enrichment
Nearest POIs Home/Work/Other
Adding speed, acceleration, etc.
Enrichment data sources
Temporal criteria
Rank Transport mode detection
Systematic stop
Rank
POIs Occasional stop
Systematic
stop
The pre-processing module (blue block in Figure 1) takes in input a set of raw trajectories
and filters out noisy or unusable data to facilitate the activities of the other modules. One of
the functions of the pre-processing module is to discard trajectories that have an insuficient
sampling rate. The module also filters out the outliers by analysing their spatio-temporal
characteristics. Another functionality is the trajectory compression adopted from the
scikitmobility library [
The segmentation module (green block in Figure 1) takes in input a set of pre-processed
trajectories and partitions every trajectory into sub-trajectories (or segments). A well known
and widely used segmentation criterion is the stop and move [
The segment enrichment module (yellow block in Figure 1) takes the output of the previous module and identifies the diferent segments to enrich, the aspects to consider, the datasets to be used to enrich the segments with diferent aspects, and the enrichment criteria. In MATBuilder’s current implementation this module has been divided into two sub-modules, one dealing with the enrichment of stops and the other with the enrichment of moves.
The stop enrichment sub-module enriches the stop segments with the regularity aspect, i.e., it distinguishes between systematic and occasional stops. The geographical area of interest is discretized into a spatial grid by means of a Geohash function4, which is then used to assign each stop segment to a cell. Stops that fall within the same cell more than a given number of times are considered to belong to the same systematic stop. Conversely, stops that do not satisfy this criterion fall within the occasional category.
Systematic stops are then further enriched with the activity aspect by inferring the activity performed in it. The module is currently tailored on people’s movements, therefore classifying systematic stops in home, work and others. The home activity enriches a systematic stop that occurs in the night. Work, on the other hand, is an activity that enriches a systematic stop that occurs on weekdays during working hours (i.e., a user defined time range). Systematic stops that do not satisfy any of the above criteria are enriched as other.
Occasional stops are harder to characterize than systematic stops, as they do not exhibit any substantial spatio-temporal regularity. Thus, the module associates to each stop the points of interest (POIs) that are located within a given range. Once the set of POIs have been identified, they are ranked in some order of preference to select the best one to enrich the segment. In the module current implementation POIs are ranked according to two criteria, i.e., their distance from the stop and the overlap between their opening hours and the stop starting and ending times (the latter criterion filters out POIs that are closed when the stop occurs). Notice that POIs can be gathered either from Open Street Map or from other external data sources, assuming that minimum information is provided (e.g., identifier, latitude, and longitude) and that it is compliant with the required formatting criteria.
The move enrichment sub-module focuses on enriching the move segments. In the present
version, the module enriches with two aspects: (1) quantitative numerical measures and (2)
transportation mean. The first aspect associates to each move quantitative numerical information
extracted from the underlying sub-trajectory, i.e., maximum and average speed, acceleration,
bearing rate, and total length. These information are computed via the PTrail library [
The MAT-Builder system targets users who want to semantically enrich movement data, possibly with many diferent aspects, and who would like to have control on how the various enrichment tasks are conducted. Indeed, thanks to MAT-Builder’s highly flexible modular design, users can decide which and how many aspects they want to consider to enrich their trajectories. Furthermore, the operations conducted by the various MAT-Builder’s modules are highly customizable, as a large set of modifiable parameters enable users to precisely control and fine-tune the whole enrichment process. To show our system capabilities we introduce a running example of use of the system though the Uniser Interface.
The users will be able to interact with the system through the MAT-Builder’s UI at the
Preprocessing Segmentation Segment enrichment Upload your dataset
../path/to/yourfiles Browse Customize pre-process steps Min. points per trajectory 10 Insert Max speed from the previous point threshold 300.0 Insert (km/h)
Dataset statistics Tot. users: 181 Tot. trajectories: 301
No.oftrajectoriesperyear diferent steps of the enrichment process. The UI presents three tabs corresponding to the three backend modules. In the pre-processing tab (Figure 2) the user will be able to select and input the raw trajectory dataset they intend to enrich.
Here the pre-processing tab lets the user customize some of the pre-processing operations, i.e., they will be able to indicate the minimum number of points a trajectory should have and a km/h threshold between two consecutive points the module uses to filter out outliers. Once the raw trajectories have been pre-processed, the UI will show the results of this step.
The user will then proceed to the trajectory segmentation tab (Figure 3). Here the interface lets the user specify the minimum duration and the spatial radius the system will use to identify the stop segments (and, indirectly, the move ones). Once the trajectories have been segmented, the UI will again show a few statistics about this step.
The user will finally proceed to the segment enrichment tab (Figure 4), where they will be able to enrich the stop and move segments with diferent aspects. The first aspect added to the stop MAT-builder Preprocessing Segmentation Segment enrichment Customize segmentation Min. duration of a stop (minutes) Max. spatial search radius (km) 10
Insert 300.0 Insert
USER 1 No. trajectories: 5 No. stops: 4 Avarage duration of stops: 23 min. segments concerns their regularity, i.e., whether they belong to some type of systematic stop or they are an occasional one. Next, the user will be able to select the data sources to be used to enrich the occasional stops. Here, the user can provide a file for each aspect of interest – the system currently supports POIs, weather, or social media posts. Moreover, MAT-Builder is able to gather POIs from OpenStreetMap in the eventuality a POI file cannot be provided: in this case, the user must first specify a bounding box of geographical coordinates from which the POIs should be retrieved. POIs might have an extremely large number of attributes, and many of these may have lots of missing values. To deal with this issue the UI lets the user specify a value to discard attributes with too many missing values. With the POIs in place, the module decides which POIs should be used to enrich the occasional stops by ranking them according to the criteria described in Section 3 (i.e., distance and temporal overlap). Finally, the user will be able to choose whether to enrich the moves with the transportation mean. This aspect is estimated with a random-forest classifier as described in Section 3.
In this paper we proposed MAT-Builder, a new system that supports users in creating multiple aspect trajectory datasets starting from raw trajectories and external data sources. The semantic enrichment process ofered by MAT-Builder includes trajectory pre-processing, trajectory segmentation, and segment enrichment. The backend, which is the core component of MAT-Builder, implements said process and it is currently instantiated with stop and move segmentation, enrichment with systematic and occasional stops, activity inference, and transportation mean estimation. A peculiar characteristic of MAT-Builder is that it is designed to be modular and easily extensible which, in conjunction with MAT-Builder’s data workflow management capabilites, allows users to easily set up their own semantic enrichment process. This work has been partially supported by the European Union’s Horizon 2020 Research and Innovation program under the project MobiDataLab (GA 101006879) and MSCA project MASTER (GA 777695).