Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

Abstract

Abstract: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.

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

“…On the other hand, trying to incorporate the complexity of real trajectories, hierarchical clustering algorithms build models by introducing global or local variables, such as the speed, duration, curvature, and other descriptors of trajectories [20][21][22][23][24]. Mohammad et al showed the extraction of new point features: bearing rate, the rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles, enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores [25].…”

Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach

“…Among these features, global features focus on describing whole characteristics of trajectory parameters (e.g., speed, acceleration, etc.) including average values (i.e., mean, absolute mean, median, mode), variance, standard deviation, percentiles, skewness, and kurtosis [21] Conversely, local features focus on describing local characteristics of trajectory parameters. For example, Deng et al [30] proposed a random forest based method to split the trajectory into segments and obtain features including the mean, standard deviation, and slope from the interval part of the trajectory.…”

“…In existing studies, global features [19,21,[25][26][27][28][29], local features [12,30], time-domain features [26], frequency-domain features [25,26] and specific features [19,28,[31][32][33] were extracted through corresponding methods. Among these features, global features focus on describing whole characteristics of trajectory parameters (e.g., speed, acceleration, etc.)…”

“…A technical framework for researching detection includes four components [12][13][14][19][20][21][22][23][24][25]: (i) Data preprocessing; (ii) Feature extraction and selection; (iii) Classifier identifications; (iv) Evaluations and analyses. A proper combination of methods in these four components determines the quality of identification.…”

Shape-Based Single Object Classification Using Ensemble Method Classifiers

“…Selection of the optimum attributes from the data set is also another step of the tuning process. This will be repeated until the right combination of parameters is selected to generate the best model [16].…”