Inferring transportation modes from GPS trajectories using a convolutional neural network
By Sina Dabiri, Kevin P. Heaslip. arXiv.org, 2018
371 citations recorded by the source corpus.
Abstract 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.
Subfields: Building and Construction, Transportation
View Paper: https://www.semanticscholar.org/paper/0e2b4e953991c4d15c10d3c58296a0ce6b0bc0e4
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Citations in the corpus
- Tackling Climate Change with Machine Learning (2019, methodology): “by high speed rail in China. There is also work on inferring people’s travel modes and destinations from social media or various mobile phone sensors such as GPS (transportation mode detection), e.g. [246, 247]. Also in the freight sector, ML has been applied to analyze modal trade-offs, for example”
- Identifying intercity freight trip ends of heavy trucks from GPS data (2021, methodology): “methods [48, 49] to identify the trip ends of heavy trucks to further improve the identifi-”
- The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations (2021, background): “In the same vein, attempts to rely on smartphones to infer individuals ’ transportation choices (Dabiri and Heaslip 2018) may lead to biased choices unless communities with lower smartphone uptake are properly accounted for.”