Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.

Location prediction has attracted much attention due to its important role in many location-based services, such as food delivery, taxi-service, real-time bus system, and advertisement posting. Traditional prediction methods often cluster track points into regions and mine movement patterns within the regions. Such methods lose information of points along the road and cannot meet the demand of specific services. Moreover, traditional methods utilizing classic models may not perform well with long location sequences. In this paper, a spatial-temporal-semantic neural network algorithm (STS-LSTM) has been proposed, which includes two steps. First, the spatial-temporal-semantic feature extraction algorithm (STS) is used to convert the trajectory to location sequences with fixed and discrete points in the road networks. The method can take advantage of points along the road and can transform trajectory into model-friendly sequences. Then, a long short-term memory (LSTM)-based model is constructed to make further predictions, which can better deal with long location sequences. Experimental results on two real-world datasets show that STS-LSTM has stable and higher prediction accuracy over traditional feature extraction and model building methods, and the application scenarios of the algorithm are illustrated.

Trajectory simplification has become a research hotspot since it plays a significant role in the data preprocessing, storage, and visualization of many offline and online applications, such as online maps, mobile health applications, and location-based services. Traditional heuristic-based algorithms utilize greedy strategy to reduce time cost, leading to high approximation error. An Optimal Trajectory Simplification Algorithm based on Graph Model (OPTTS) is proposed to obtain the optimal solution in this paper. Both min-# and min-ε problems are solved by the construction and regeneration of the breadth-first spanning tree and the shortest path search based on the directed acyclic graph (DAG). Although the proposed OPTTS algorithm can get optimal simplification results, it is difficult to apply in real-time services due to its high time cost. Thus, a new Online Trajectory Simplification Algorithm based on Directed Acyclic Graph (OLTS) is proposed to deal with trajectory stream. The algorithm dynamically constructs the breadth-first spanning tree, followed by real-time minimizing approximation error and real-time output. Experimental results show that OPTTS reduces the global approximation error by 82% compared to classical heuristic methods, while OLTS reduces the error by 77% and is 32% faster than the traditional online algorithm. Both OPTTS and OLTS have leading superiority and stable performance on different datasets.