5,471 research outputs found
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 features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
Route Restoration Method for Sparse Taxi GPS trajectory based on Bayesian Network
In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional research solely based on time-spatial variables, this method additionally considers the characteristics of empty/heavy taxi status, weather conditions, drivers, vehicle running and other factors to carry out route restoration. A field case of grid network in Ningbo is taken to verify the applicability of the method, using the taxi GPS trajectory data from Ningbo Taxi Information Management Platform. The case results show that the accuracy of Bayesian network method based on multiple factors reaches 91.4%. Its performance is superior to the Multivariate logistic regression model. In addition, the proposed method is especially suitable for scenarios with a high missing rate of track data, such as a scene with timespan of about 5 min between neighbour trajectories
Travel Mode Recognition from GPS Data Based on LSTM
A large amount of GPS data contains valuable hidden information. With GPS trajectory data, a Long Short-Term Memory model (LSTM) is used to identify passengers' travel modes, i.e., walking, riding buses, or driving cars. Moreover, the Quantum Genetic Algorithm (QGA) is used to optimize the LSTM model parameters, and the optimized model is used to identify the travel mode. Compared with the state-of-the-art studies, the contributions are: 1. We designed a method of data processing. We process the GPS data by pixelating, get grayscale images, and import them into the LSTM model. Finally, we use the QGA to optimize four parameters of the model, including the number of neurons and the number of hidden layers, the learning rate, and the number of iterations. LSTM is used as the classification method where QGA is adopted to optimize the parameters of the model. 2. Experimental results show that the proposed approach has higher accuracy than BP Neural Network, Random Forest and Convolutional Neural Networks (CNN), and the QGA parameter optimization method can further improve the recognition accuracy
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
We propose a Bayesian model for extracting sleep patterns from smartphone
events. Our method is able to identify individuals' daily sleep periods and
their evolution over time, and provides an estimation of the probability of
sleep and wake transitions. The model is fitted to more than 400 participants
from two different datasets, and we verify the results against ground truth
from dedicated armband sleep trackers. We show that the model is able to
produce reliable sleep estimates with an accuracy of 0.89, both at the
individual and at the collective level. Moreover the Bayesian model is able to
quantify uncertainty and encode prior knowledge about sleep patterns. Compared
with existing smartphone-based systems, our method requires only screen on/off
events, and is therefore much less intrusive in terms of privacy and more
battery-efficient
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