| Summary: | In the last decades, studies on travel mode detection from location data have been increasing exponentially. However, these studies have struggled with three limitations: data collection-, feature selection-, and classification approach–related issues. Thus, we propose a novel framework to collect trajectory data and infer travel modes by making a great deal of effort. First, we conduct a travel survey with smartphones in Shanghai City, China. Furthermore, we use a prompted recall survey with surveyor intervention by telephones. In the survey, the surveyor asks respondents to validate the travel information automatically detected from trajectory data. Second, we use well-known sequential forward selection procedures to select the most reasonable combination of features. This set of features is expected to help achieve high classification accuracy with few features. Third, as a machine learning approach incorporating high resistance to noise in features, a continuous hidden Markov model is used to classify segments in dataset 1 that comprises Global Positioning System data alone. Consequently, 94.37% of segments are flagged correctly for the training dataset, while 93.47% are detected properly for the test dataset by making a comparison between detected travel modes and travel modes validated during the prompted recall survey. A higher accuracy (95.28%) is achieved in the test dataset on dataset 2 that consists of Global Positioning System, accelerometer, Global System for Mobile communication, and Wi-Fi data. The promising results obtained with this method provide a new perspective in understanding travel mode detection and other related issues in Global Positioning System travel surveys, including trip purpose detection.
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