:: Volume 10, Issue 3 (2-2023) ::
jgit 2023, 10(3): 71-94 Back to browse issues page
Analyzing the performance of different machine learning methods in determining the transportation mode using trajectory data
Morteza Tayebi, Parham Pahlavani *
University of Tehran
Abstract:   (734 Views)
With the widespread advent of the smart phones equipping with Global Positioning System (GPS), a huge volume of users’ trajectory data was generated. To facilitate the urban management and present appropriate services to users, studying these data was raised as a widespread research field and has been developing since then. In this research, the transportation mode of users’ trajectories was identified based on their raw GPS data. These data are often associated with errors, it was attempted to minimize them by applying a comprehensive pre-processing procedure in this research. Accordingly, various features were extracted to identify the transportation modes including walk, bike, train, bus, and driving. In this regard, four classification methods including decision tree, multilayer perceptron neural network, Naïve Bayes, and support vector machine were used to build a predictive model. In order to improve the performance of the implementation methods, the percentage of the points of each trajectory on the distance of one standard deviation from the total speed average of transportation modes has been used as a new feature. The above-mentioned four models were implemented with different regularization parameters and their values were set to the optimal values by applying a comprehensive grid search. Then, Kappa and the overall accuracy indices were employed to evaluate different methods. The results of this study show that the multilayer perceptron neural network with overall accuracy of 0.88 has the best results compared to the other models.
Keywords: Trajectory data, Determining the transportation mode, Classification, Machine learning
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Type of Study: Research | Subject: GIS
Received: 2022/05/16 | Accepted: 2023/01/21 | Published: 2023/02/6

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Volume 10, Issue 3 (2-2023) Back to browse issues page