Semantic Trajectories

Modeling and AnalysisBy: Shahab Helmi

Outline

Larger datasets are becoming available from GPS, GSM, RFID, and other sensors.

Interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement.

Hence, semantically rich trajectories has have been promoted.

We will review:

1. Constructing trajectories from movement tracks

2. Enriching trajectories with semantic information

3. Using data mining techniques to analyze semantic trajectories

Preliminaries & Definitions

Trajectory: a trajectory is the path that a moving object follows through space as a function of time. Thus, it can be captured as a time-stamped series of location points, denoted as {x1, y1, t1, x2, y2, t2, ..., xN, yN, tN} where xi, yi represent geographic coordinates of the moving object at time ti and N is the total number of elements in the series.

Semantic Trajectory: a trajectory that has been enhanced with annotations and/or one or several complementary segmentations (e.g. segmented by “start” and “stop” points).

From Raw To Sound Trajectories

Raw movement data: data points collected from tracking devices, such as GPS.

We would like to turn imperfect raw movement data into a trajectory dataset that is correct and manageable from the viewpoint of the targeted application.

Steps:

1. Trajectory data cleaning

2. Trajectory map-matching

3. Compression of trajectory data

Preprocessing

Semantic Enrichment

Behavior Mining

Trajectory Data Cleaning

Most query processing and indexing techniques are built upon this assumption that spatio-temporal positions of moving objects can be precisely provided.

Real-life trajectory data is far from being reliable enough for applications:

Noises

Poor GPS Signal

Battery Outage

…

GPS errors can be divided into to main categories:

1. Systematic errors: happens due to low number of available satellites and invalidates the GPS position. Can be solved by automatic filtering methods -> for example using the maximum speed of the object.

2. Random errors: happens duo to external reasons. Can be reduced by smoothing methods which are based on statistical analysis -> Kernel based methods, regression based methods …

We have to find noisy points (outliers) replace them with a better value. For example, we could use the Maximum speed of an object to find outliers.

Preprocessing

Semantic Enrichment

Behavior Mining

Trajectory Map-Matching

For objects that moves in a network, such as road network.

There are two reconstruction levels:

1. Replacing the point with a point inside the network:

Geometric map-matching: point-to point, point-to-curve, curve-to-curve

Topological map matching: adjacency and connectivity of the graph are important.

Probabilistic map-matching: consider speed, direction, heading …

Hybrid

2. Transforming the raw trajectory into a semantic map-matched trajectory, for example a. sequence of road segments.

Preprocessing

Semantic Enrichment

Behavior Mining

Compression Techniques

Trajectory data in applications grow progressively and intensively as the tracking time goes by.

Such enormous amounts of data can sooner or later lead to storage, transmission, computation, and display challenges.

Objectives of compression algorithms:

Reducing the size of dataset.

Reducing the computation complexity.

Supporting low devastation (reduced and original trajectories are not too different)

Algorithms can be categorized to 3 groups:

Top-down algorithms recursively split trajectory and keep the key points (DP).

Bottom-up algorithms in each step add a point that has the lowest cost.

Windowing algorithms (online reduction).

Preprocessing

Semantic Enrichment

Behavior Mining

Semantic Enrichment of TrajectoriesThere are three general steps:

1. Trajectory segmentation into Episodes

2. Episodes annotation

3. Trajectory annotation

Preprocessing

Semantic Enrichment

Behavior Mining

Trajectory Segmentation Into Episodes

Trajectory segmentation is driven by application-dependent criteria.

The most common one: “stop”s and “move”s periods.

The challenge is to find the stop points:

No movement at all during some length of time -> probably stopping at a place of interest (POI) such as a “Restaurant”, the “Eifel tower”, a “Museum” and etc.

A five minute gap that car is not moving or low speed (Krumm and Horvitz [2006]).

Moving with a almost constant speed and direction for a fishing boat.

…

Preprocessing

Semantic Enrichment

Behavior Mining

Episodes annotation

After finding stop points, we could annotate episodes with activities or POIs:

At home, at work, …

In bus, driving, walking

Shopping, walking, …

…

Preprocessing

Semantic Enrichment

Behavior Mining

Trajectory annotation

Synthesizing all the information in the trajectory into a singe label that characterizes the whole trajectory. For example, considering annotations of episodes we could come up with the “Tourist” label for the trajectory.

Preprocessing

Semantic Enrichment

Behavior Mining

Extracting Behavioral Knowledge From Trajectories Knowledge discovery from trajectories aims at identifying behaviors, either

among individual trajectories or groups of trajectories.

Spatial and Spatiotemporal Patterns

Granularity of Trajectory Patterns Global Vs. Partial Patterns

Individual Vs. Group Patterns

Constrained Trajectory Patterns

Common techniques: Clustering trajectories sharing similar characteristics such as shape, speed, direction…

Classifying trajectories in predefined classes.

Discovering common sequences of movements (from A to B to C)

Identifying objects that their movements are related to each other (leadership, flock, …)

Semantic-Based Knowledge Discovery Semantics-based behavior discovery techniques can be divided in two

main groups:

1. approaches searching for common behaviors that are previously unknown.

2. approaches looking for specific behaviors.

Preprocessing

Semantic Enrichment

Behavior Mining

Discovering Unknown Behaviors Meet: A group of trajectories end at the same region C.

If we use semantic trajectories with stops annotated with POIs (school A, school B, . . . , cinema Lux). we can discover the frequent semantic behavior “going from school to cinema on Wednesday afternoon”, which corresponds to the fact that this cinema offers special price tickets for students on Wednesday afternoons.

The Semantic Trajectory Data Mining Query Language (ST-DMQL) [Bogorny et al. 2009] allows users to specify semantic enrichment of trajectories with contextual domain information,

The language is implemented in Weka-STPM [Bogorny et al. 2011], an extended version of the Weka data mining toolkit and the first toolkit for multilevel mining of semantic trajectories. It is a free and open-source tool that also provides spatial visualization of the semantic trajectories and behaviors.

Another tool that analyzes semantic trajectories to infer behavior is M-ATLAS [Giannotti et al. 2011]. This system provides support for both raw and semantic trajectories, and is organized in a plug-and-play architecture that allows the easy integration of different mining algorithms, from clustering to classification techniques.

Spatio-temporal vs. semantic behavior

Preprocessing

Semantic Enrichment

Behavior Mining

Discovering Unknown Behaviors Chasing behavior: provide an algorithm that evaluates if an

individual (a person or animal) called the stalker intentionally follows another individual called the target. The stalker must follow the target for a certain time period, and during this period, the movement of the two individuals must remain with similar speed and direction. Moreover, the target must always be in front of the stalker. Siqueira and Bogorny [2011].

Avoidance behavior: present an algorithm for identifying the trajectories that avoids a static object. For example, when analyzing human trajectories an avoidance of street cameras may reveal a suspect behavior;

A trajectory shows an Avoidance behavior when it moves towards a target geographic object, turns around this object without intersecting it, and after avoiding the target object, the trajectory returns to its original path. Alvares et al. [2011].

Preprocessing

Semantic Enrichment

Behavior Mining

References

Semantic Trajectories Modeling and Analysis, ACM Computing Surveys, Vol. 45, No. 4, Article 42, Publication date: August 2013.

Computing with Spatial Trajectories, Yu Zheng, Xiaofang Zhou, Springer

Mobility Data Management and Exploration, Nikos Pelekis, Yannis Theodoridis, Spronger