Automated traffic incident detection with a smaller dataset based on generative adversarial networks

Lin, Yi; Li, Fulin; Jing, Hailong; Ran, Bin; Sun, Dongye

doi:10.1016/j.aap.2020.105628

Cited by 79 publications

(45 citation statements)

References 29 publications

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“…In addition, several state-of-the-art AID methods are also introduced for comparison, including SVM and KNN ensemble learning (KNN-SVM) [34], Fuzzy Deep Learning (FDL) [28], a hybrid AID method using Wavelet Transformation and Logistic Regression (WT-LR) [33], a hybrid AID method using GAN and SVM (GAN-SVM) [35], and Tabu Search Algorithm optimized-SVM (TSA-SVM) [22]. In order to ensure the performance of comparison methods.…”

Section: The Comparisons and Analysismentioning

confidence: 99%

“…MTTD is defined as the average of time elapsed between the actual start time (reported time) of the incident and time when the incident is first detected by an AID method. However, MTTD is not considered in some studies [6], [31], [34], [35], [47]. Obviously, we expect AID methods to obtain high DR (close to 100%), low FAR (close to 0%) and short MTTD (as short as possible).…”

Section: ) Evaluation Criteriamentioning

confidence: 99%

“…Xiao [34] proposed an AID method using SVM and k-Nearest Neighbor (KNN) ensemble learning, and the experimental results indicate that the proposed method obtains the best performance with better robustness than the individual model (SVM or KNN). Lin et al [35] proposed an AID method based on Generative Adversarial Network (GAN) and SVM. The experimental results show that this method is effective in dealing with imbalanced and small training samples.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With Bayesian Optimization Algorithm

2021

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Section: The Comparisons and Analysismentioning

confidence: 99%

Section: ) Evaluation Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With Bayesian Optimization Algorithm

2021

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“…In general, the safety intervals of two aircraft are varied in three-dimensional earth space. Therefore, the target function is rewritten as Equation (17), in which the distance of two aircraft in the 3D space are computed separately. However, the integration is too complicated to obtain an analytic solution for the joint distribution of positional components.…”

Section: Conflict Detectionmentioning

confidence: 99%

“…(c) Machine learning-based approaches: the core idea of this type of approach is mining frequent transition patterns from historical trajectories [5,16], which is further used to build the trajectory patterns of the predicted flights. In general, historical trajectories of a certain flight are proven to be feasible, safe and effective [4,17]. Therefore, based on the learned transition patterns, a machine learning-based approach can obtain higher prediction accuracy than that of other hand-crafted models [18].…”

Section: Introductionmentioning

confidence: 99%

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

2020

Self Cite

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In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller’s instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarning time.

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Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder

Wang

Cheng

Roberts

et al. 2021

Human Brain Mapping

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Working memory (WM) impairments are common features of psychiatric disorders. A systematic meta‐analysis was performed to determine common and disorder‐specific brain fMRI response during performance of WM tasks in patients with SZ and patients with MDD relative to healthy controls (HC). Thirty‐four published fMRI studies of WM in patients with SZ and 18 published fMRI studies of WM in patients with MDD, including relevant HC, were included in the meta‐analysis. In both SZ and MDD there was common stronger fMRI response in right medial prefrontal cortex (MPFC) and bilateral anterior cingulate cortex (ACC), which are part of the default mode network (DMN). The effects were of greater magnitude in SZ than MDD, especially in prefrontal‐temporal‐cingulate‐striatal‐cerebellar regions. In addition, a disorder‐specific weaker fMRI response was observed in right middle frontal gyrus (MFG) in MDD, relative to HC. For both SZ and MDD a significant correlation was observed between the severity of clinical symptoms and lateralized fMRI response relative to HC. These findings indicate that there may be common and distinct anomalies in brain function underlying deficits in WM in SZ and MDD, which may serve as a potential functional neuroimaging‐based diagnostic biomarker with value in supporting clinical diagnosis, measuring illness severity and assessing the efficacy of treatments for SZ and MDD at the brain level.

show abstract

Automated traffic incident detection with a smaller dataset based on generative adversarial networks

Cited by 79 publications

References 29 publications

A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With Bayesian Optimization Algorithm

A Hybrid Method for Traffic Incident Detection Using Random Forest-Recursive Feature Elimination and Long Short-Term Memory Network With Bayesian Optimization Algorithm

A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

Shared and distinct brain fMRI response during performance of working memory tasks in adult patients with schizophrenia and major depressive disorder

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