A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices

Li, Zhishuai; Xiong, Gang; Wei, Zebing; Lv, Yisheng; Anwar, Noreen; Wang, Fei‐Yue

doi:10.1109/jiot.2021.3115239

Cited by 20 publications

(3 citation statements)

References 44 publications

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“…To avoid using these hand-crafted features, some studies employ supervised [2,5,6,[20][21][22][23] and semi-supervised [24][25][26] DL techniques to extract multiple layers of features automatically, often yielding comparable or even superior results than ML. Still, they demand significant computational resources and large volumes of training segments with equal length, which require interpolation or padding in real-world data.…”

Section: Related Workmentioning

confidence: 99%

POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection

Cardoso-Pereira,

Borges,

Viana

et al. 2024

IEEE Internet Things J.

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Section: Related Workmentioning

confidence: 99%

POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection

Cardoso-Pereira,

Borges,

Viana

et al. 2024

IEEE Internet Things J.

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“…Trajectory recovery is a fundamental task in Intelligent Transport Systems (ITS), which aims to recover a complete and continuous trajectory from sparse and discrete input road segments from GPS records [23,27,32,37]. Trajectory recovery further enables various applications such as urban movement behavior study [13,14,16,19,46], traffic prediction [7,15,18,31], next location prediction [19,20,41], route planning [34], anomaly detection [17], and travel time estimation [7,38].…”

Section: Introductionmentioning

confidence: 99%

A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery

Li,

et al. 2023

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data

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Trajectory on the road traffic is commonly collected at a low sampling rate, and trajectory recovery aims to recover a complete and continuous trajectory from the sparse and discrete inputs. Recently, sequential language models have been innovatively adopted for trajectory recovery in a pre-trained manner: it learns road segment representation vectors, which will be used in the downstream tasks. However, existing methods are incapable of handling complex trajectories: when the trajectory crosses remote road segments or makes several turns, which we call critical nodes, the quality of learned representations deteriorates, and the recovered trajectories skip the critical nodes. This work is dedicated to offering a more robust trajectory recovery for complex trajectories. Firstly, we define the trajectory complexity based on the detour score and entropy score, and construct the complexity-aware semantic graphs correspondingly. Then, we propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training from two aspects: 1) adaptively aggregate the multi-view graph features considering trajectory pattern, and 2) higher attention to critical nodes in a complex trajectory. Such that, our MGCAT is perceptual when handling the critical scenario of complex trajectories. Extensive experiments are conducted on large-scale datasets. The results prove that our method learns better representations for trajectory recovery, with 5.22% higher F1-score overall and 8.16% higher F1-score for complex trajectories particularly. The code is available here.

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“…Existing geographic movement research has improved analysis methods (Dodge et al, 2012;Dodge, 2016a;Dodge et al, 2016;Dodge, 2016b;Graser et al, 2021Soares Junior et al, 2017;Huang, 2017) and shown how these methods can derive valuable information about human movement and wildlife movement (Wang et al, 2020a;Dodge et al, 2014;Miller et al, 2019;Zhu et al, 2021;Li et al, 2021). However, partly due to the challenges and because computational techniques to address them are relatively new, this prior research focused on geographic movement in precise movement trajectories from sensors such as GPS and mostly ignored movement described in text documents.…”

Section: Introductionmentioning

confidence: 99%

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

Pezanowski

Mitra

MacEachren

2022

KN J. Cartogr. Geogr. Inf.

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Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable. Supplementary Information The online version contains supplementary material available at 10.1007/s42489-022-00098-3.

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A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices

Cited by 20 publications

References 44 publications

POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection

POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode Detection

A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

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