In many cases, such as trajectories clustering and classification, we often divide a trajectory into segments as preprocessing. In this paper, we propose a trajectory semantic segmentation method based on learned behavior models. In the proposed method, we learn some behavior models from video sequences. Next, using learned behavior models and a hidden Markov model, we segment a trajectory into semantic segments. Comparing with the Ramer-Douglas-Peucker algorithm, we show the effectiveness of the proposed method.
@cite_13: 
@cite_1: Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users' raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user's GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user's present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point-based segmentation method, an inference model and a post-processing algorithm based on conditional probability. The change point-based segmentation method was compared with two baselines including uniform duration based and uniform length based methods. Meanwhile, four different inference models including Decision Tree, Bayesian Net, Support Vector Machine (SVM) and Conditional Random Field (CRF) are studied in the experiments. We evaluated the approach using the GPS data collected by 45 users over six months period. As a result, beyond other two segmentation methods, the change point based method achieved a higher degree of accuracy in predicting transportation modes and detecting transitions between them. Decision Tree outperformed other inference models over the change point based segmentation method.
@cite_6: Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.
@cite_10: User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
@cite_11: Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.
Task oriented methods are also proposed. @cite_13 propose a system called T-Finder, which recommends to taxi drivers places where as many potential customers exist as possible, and to end users places where taxis are expected to be find. To this end, they estimate taxi locations based on taxi driving trajectories and segments the trajectories as pre-processing. @cite_11 proposes TRAOD, an algorithm for finding outliers in trajectories based on segmentation by using the Minimum Description Length (MDL) principle. estimates Transportation Mode @cite_6 @cite_1 @cite_10 such as walk, car, bus, and bike used for semantic segmentation in terms of a mode of transportation.