Inferring transportation modes from GPS trajectories using a convolutional neural network
نویسندگان
چکیده
منابع مشابه
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users’ transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters’ mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted fe...
متن کاملInferring hybrid transportation modes from sparse GPS data using a moving window SVM classification
0198-9715/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.compenvurbsys.2012.06.00 ⇑ Corresponding author. Tel.: +44 788 983 1988. E-mail addresses: [email protected] (A. Bolbol), ta [email protected] (I. Tsapakis), [email protected] 1 Tel.: +44 781 56
متن کاملDetecting Transportation Modes Using Deep Neural Network
Existing studies on transportation mode detection from global positioning system (GPS) trajectories mainly adopt handcrafted features. These features require researchers with a professional background and do not always work well because of the complexity of traffic behavior. To address these issues, we propose a model using a sparse autoencoder to extract point-level deep features from point-le...
متن کاملEMG-based wrist gesture recognition using a convolutional neural network
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...
متن کاملPredicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal
Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transportation Research Part C: Emerging Technologies
سال: 2018
ISSN: 0968-090X
DOI: 10.1016/j.trc.2017.11.021