On feature selection and evaluation of transportation mode prediction\n strategies Article Swipe
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· 2018
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.1808.03096
· OA: W2950682487
Transportation modes prediction is a fundamental task for decision making in\nsmart cities and traffic management systems. Traffic policies designed based on\ntrajectory mining can save money and time for authorities and the public. It\nmay reduce the fuel consumption and commute time and moreover, may provide more\npleasant moments for residents and tourists. Since the number of features that\nmay be used to predict a user transportation mode can be substantial, finding a\nsubset of features that maximizes a performance measure is worth investigating.\nIn this work, we explore wrapper and information retrieval methods to find the\nbest subset of trajectory features. After finding the best classifier and the\nbest feature subset, our results were compared with two related papers that\napplied deep learning methods and the results showed that our framework\nachieved better performance. Furthermore, two types of cross-validation\napproaches were investigated, and the performance results show that the random\ncross-validation method provides optimistic results.\n
