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Chen Y, Kuang K, Wu C. Trajectory classification to support effective and efficient field-road classification. PeerJ Comput Sci 2024; 10:e1945. [PMID: 38660171 PMCID: PMC11042022 DOI: 10.7717/peerj-cs.1945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/28/2024] [Indexed: 04/26/2024]
Abstract
Field-road classification, which automatically identifies in-field activities and out-of-field activities in global navigation satellite system (GNSS) recordings, is an important step for the performance evaluation of agricultural machinery. Although several field-road classification methods based only on GNSS recordings have been proposed, there is a trade-off between time consumption and accuracy performance for such methods. To obtain an optimal balance, it is important to choose a suitable field-road classification method for each trajectory based on its GNSS trajectory quality. In this article, a trajectory classification task was proposed, which classifies the quality of GNSS trajectories into three categories (high-quality, medium-quality, or low-quality). Then, a trajectory classification (TC) model was developed to automatically assign a quality category to each input trajectory, utilizing global and local features specific to agricultural machinery. Finally, a novel field-road classification method is proposed, wherein the selection of field-road classification methods depends on the trajectory quality category predicted by the TC model. The comprehensive experiments show that the proposed trajectory classification method achieved 86.84% accuracy, which consistently outperformed current trajectory classification methods by about 2.6%, and the proposed field-road classification method has obtained a balance between efficiency and effectiveness, i.e., sufficient efficiency with a tolerable accuracy loss. This is the first attempt to examine the balance problem between efficiency and effectiveness in existing field-road classification methods and to propose a trajectory classification specific to these methods.
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Affiliation(s)
- Ying Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Kaiming Kuang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Caicong Wu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing, China
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2
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Spiers HJ, Coutrot A, Hornberger M. Explaining World-Wide Variation in Navigation Ability from Millions of People: Citizen Science Project Sea Hero Quest. Top Cogn Sci 2023; 15:120-138. [PMID: 34878689 DOI: 10.1111/tops.12590] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 02/01/2023]
Abstract
Navigation ability varies widely across humans. Prior studies have reported that being younger and a male has an advantage for navigation ability. However, these studies have generally involved small numbers of participants from a handful of western countries. Here, we review findings from our project Sea Hero Quest, which used a video game for mobile and tablet devices to test 3.9 million people on their navigation ability, sampling across every nation-state and from 18 to 99 years of age. Results revealed that the task has good ecological validity and across all countries sufficiently sampled (N = 63), age is linked to a near-linear decline in navigation ability from the early 20s. All countries showed a male advantage, but this varied considerably and could be partly predicted by gender inequality. We found that those who reported growing up in a city were on average worse at navigating than those who grew up outside cities and that navigation performance helped identify those at greater genetic risk of Alzheimer's disease. We discuss the advantages and challenges of using a mobile app to study cognition and the future avenues for understanding individual differences in navigation ability arising from this research.
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Affiliation(s)
- Hugo J Spiers
- Department of Experimental Psychology, Division of Psychology and Language Sciences, Institute of Behavioural Neuroscience, University College London
| | - Antoine Coutrot
- Laboratoire des Sciences du Numérique de Nantes, CNRS.,Laboratoire d'InfoRmatique en Image et Systèmes d'information, CNRS
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3
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Liu W, Ma L, Qu S, Peng Z. A trajectory summarisation generation method based on the mobile robot behaviour analysis. IET CYBER-SYSTEMS AND ROBOTICS 2022. [DOI: 10.1049/csy2.12063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Weifeng Liu
- School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an China
| | - Liwen Ma
- School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an China
| | - Shaoyong Qu
- School of Artificial Intelligence Hangzhou Dianzi University Hangzhou China
| | - Zhangming Peng
- School of Mechanical Engineering Hangzhou Dianzi University Hangzhou China
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4
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Gao N, Xue H, Shao W, Zhao S, Qin KK, Prabowo A, Rahaman MS, Salim FD. Generative Adversarial Networks for Spatio-temporal Data: A Survey. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3474838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.
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Affiliation(s)
- Nan Gao
- RMIT University, Melbourne, Victoria, Australia
| | - Hao Xue
- RMIT University, Melbourne, Victoria, Australia
| | - Wei Shao
- RMIT University, Melbourne, Victoria, Australia
| | - Sichen Zhao
- RMIT University, Melbourne, Victoria, Australia
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Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition. SENSORS 2022; 22:s22010346. [PMID: 35009888 PMCID: PMC8749573 DOI: 10.3390/s22010346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 12/24/2021] [Accepted: 12/28/2021] [Indexed: 12/04/2022]
Abstract
As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.
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6
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He Y, Li Z, Fu L, Wang A, Zhang P, Zhou S, Zhang J, Yu T. TARA-Net: A Fusion Network for Detecting Takeaway Rider Accidents. ACM T INTEL SYST TEC 2021. [DOI: 10.1145/3457218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders’ contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a
TakeAway
Rider
Accident
detection fusion network
TARA-Net
to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world
Ele.me
dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.
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Affiliation(s)
- Yifan He
- Fudan University, Shanghai, China
| | - Zhao Li
- Alibaba Group, Hangzhou, China
| | - Lei Fu
- Alibaba Group, Hangzhou, China
| | | | | | | | - Ji Zhang
- The University of Southern Queensland, Queesland, Australia
| | - Ting Yu
- Zhejiang Lab, Hangzhou, China
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Rossi L, Ajmar A, Paolanti M, Pierdicca R. Vehicle trajectory prediction and generation using LSTM models and GANs. PLoS One 2021; 16:e0253868. [PMID: 34197526 PMCID: PMC8248611 DOI: 10.1371/journal.pone.0253868] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022] Open
Abstract
Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.
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Affiliation(s)
- Luca Rossi
- Dipartimento di Ingegneria dell’Informazione (DII), Universitá Politecnica delle Marche, Ancona, Italy
| | - Andrea Ajmar
- Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino, Torino, Italy
| | - Marina Paolanti
- Dipartimento di Ingegneria dell’Informazione (DII), Universitá Politecnica delle Marche, Ancona, Italy
- * E-mail:
| | - Roberto Pierdicca
- Dipartimento di Ingegneria Civile, Edile e dell’Architettura (DICEA), Universitá Politecnica delle Marche, Ancona, Italy
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Zhang Y, Yang Y, Zhou W, Wang H, Ouyang X. Multi-city traffic flow forecasting via multi-task learning. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02074-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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