scholarly journals A High-Dimensional Video Sequence Completion Method with Traffic Data Completion Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lan Wu ◽  
Tian Gao ◽  
Chenglin Wen ◽  
Kunpeng Zhang ◽  
Fanshi Kong
Keyword(s):  
Intelligent Transportation Systems ◽  
Spatial Information ◽  
Transportation Systems ◽  
Video Data ◽  
Generative Adversarial Networks ◽  
High Dimensional ◽  
Traffic Data ◽  
Adversarial Network ◽  
Time Space ◽  
Data Completion

The lack of traffic data is a bottleneck restricting the development of Intelligent Transportation Systems (ITS). Most existing traffic data completion methods aim at low-dimensional data, which cannot cope with high-dimensional video data. Therefore, this paper proposes a traffic data complete generation adversarial network (TDC-GAN) model to solve the problem of missing frames in traffic video. Based on the Feature Pyramid Network (FPN), we designed a multiscale semantic information extraction model, which employs a convolution mechanism to mine informative features from high-dimensional data. Moreover, by constructing a discriminator model with global and local branch networks, the temporal and spatial information are captured to ensure the time-space consistency of consecutive frames. Finally, the TDC-GAN model performs single-frame and multiframe completion experiments on the Caltech pedestrian dataset and KITTI dataset. The results show that the proposed model can complete the corresponding missing frames in the video sequences and achieve a good performance in quantitative comparative analysis.

scholarly journals Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3193
Author(s):  
Adrian Fazekas ◽  
Markus Oeser
Keyword(s):  
Data Acquisition ◽  
Cross Section ◽  
Intelligent Transportation Systems ◽  
Estimation Algorithm ◽  
Initial Step ◽  
Original Data ◽  
Transportation Systems ◽  
Location Estimation ◽  
Traffic Data ◽  
Multiple Sensors

The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. On the infrastructure side, several sensor techniques exist today that are able to record the data of individual vehicles at a cross-section, such as static radar detectors, laser scanners, or computer vision systems. In order to record the position data of individual vehicles over longer sections, the use of multiple sensors along the road with suitable synchronization and data fusion methods could be adopted. This paper presents appropriate methods considering realistic scale and accuracy conditions of the original data acquisition. Datasets consisting of a timestamp and a speed for each individual vehicle are used as input data. As a first step, a closed formulation for a sensor offset estimation algorithm with simultaneous vehicle registration is presented. Based on this initial step, the datasets are fused to reconstruct microscopic traffic data using quintic Beziér curves. With the derived trajectories, the dependency of the results on the accuracy of the individual sensors is thoroughly investigated. This method enhances the usability of common cross-section-based sensors by enabling the deriving of non-linear vehicle trajectories without the necessity of precise prior synchronization.

scholarly journals A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 815 ◽  
Author(s):  
Minghui Ma ◽  
Shidong Liang ◽  
Yifei Qin
Keyword(s):  
Data Quality ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Nearest Neighbor ◽  
Transportation Systems ◽  
Traffic Data ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Nearest Neighbor Searching ◽  
Searching Strategy

Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods.

An improved algorithm for clustering uncertain traffic data streams based on Hadoop platform

2019 ◽  
Vol 33 (19) ◽  
pp. 1950203
Author(s):  
Weixiang Xu ◽  
Jiaojiao Li
Keyword(s):  
Data Streams ◽  
Intelligent Transportation Systems ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Transportation Systems ◽  
Traffic Data ◽  
Distributed Structure ◽  
Streaming Traffic ◽  
Query Algorithm ◽  
The Impact

During the development of intelligent transportation systems, traffic data has the characteristics of streaming, high dimension and uncertainty. In order to realize the query of uncertain traffic data streams in a distributed environment, the authors design the algorithm of Uncertain Traffic Data Stream Parallel Continuous Query algorithm (UTDSPCQ). Firstly, the sliding window mode is applied to realize the data receiving and buffering in the data stream environment, so as to adapt to the MapReduce computing framework of the Hadoop distributed structure. Then, the impact of the high dimensionality and uncertainty of the data on the feature analysis of the dataset is reduced, through the dimension reduction and data rewriting. Finally, a multi-attribute data point RePoint is newly defined, to solve the problem of data dimension increase caused by data rewriting. Experiments show that this algorithm optimizes the traditional density-based clustering algorithm, and make it more adaptable to parallel continuous queries for uncertain traffic data streams, and can fully consider the newly generated streaming traffic data.

scholarly journals An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation

2019 ◽  
Vol 8 (11) ◽  
pp. 3951-3954
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Activation Function ◽  
Transportation Systems ◽  
Data Imputation ◽  
Traffic Data ◽  
Spatio Temporal

Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.

scholarly journals Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3131
Author(s):  
Zhongwei Li ◽  
Xue Zhu ◽  
Ziqi Xin ◽  
Fangming Guo ◽  
Xingshuai Cui ◽  
...  
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Generative Adversarial Networks ◽  
Hyperspectral Image Classification ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Spatial Features ◽  
Virtual Samples

Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguous, and GANs are prone to the mode collapse, which lead the poor generalization abilities ultimately. Moreover, most of these models only consider the extraction of spectral or spatial features. They fail to combine the two branches interactively and ignore the correlation between them. Consequently, the variational generative adversarial network with crossed spatial and spectral interactions (CSSVGAN) was proposed in this paper, which includes a dual-branch variational Encoder to map spectral and spatial information to different latent spaces, a crossed interactive Generator to improve the quality of generated virtual samples, and a Discriminator stuck with a classifier to enhance the classification performance. Combining these three subnetworks, the proposed CSSVGAN achieves excellent classification by ensuring the diversity and interacting spectral and spatial features in a crossed manner. The superior experimental results on three datasets verify the effectiveness of this method.

Costs and Benefits of Using Intelligent Transportation Systems as an Alternative Data Source: Case Study

2002 ◽  
Vol 1804 (1) ◽  
pp. 85-90
Author(s):  
Pat S. Hu ◽  
Richard T. Goeltz ◽  
Richard L. Schmoyer
Keyword(s):  
Intelligent Transportation Systems ◽  
Value Added ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Data ◽  
Costs And Benefits ◽  
Data Resource ◽  
Address Data ◽  
Data Source

Intelligent transportation systems (ITSs) are an alternative data source that could lead to win–win situations: this source will not only benefit the transportation operations and planning communities by allowing them to access more and better data, but it will also enhance the appeal of ITS deployment by significantly broadening its originally intended benefits. Use of ITS-generated data as an alternative data resource is reflected in the archived data user services (ADUS) in the national ITS architecture. Usually, an agency will evaluate the costs and the benefits of ADUS before it decides whether to deploy ADUS. The costs and benefits of ADUS are examined on the basis of results from a case study in which ITS-generated traffic data were analyzed to determine whether they can help meet such traffic data needs as estimating the total travel volume and the total vehicle miles traveled. The cost is measured in terms of the effort needed to archive and reformat the data, revamp the software, and address data quality and data integration issues. The benefits are measured in terms of the value added by the ITS-generated data. Although the costs are high to use ITS-generated data for purposes other than the originally intended use, the research has proved that ITS-generated data can improve transportation decisions by, in this case, improving traffic estimates.

Methodology for Capturing Driver Eye Glance Behavior during In-Vehicle Secondary Tasks

2005 ◽  
Vol 1937 (1) ◽  
pp. 61-65 ◽  
Author(s):  
David L. Smith ◽  
James Chang ◽  
Richard Glassco ◽  
James Foley ◽  
Daniel Cohen
Keyword(s):  
Eye Movements ◽  
Visual Attention ◽  
Intelligent Transportation Systems ◽  
Driver Behavior ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Video Data ◽  
Visual Task ◽  
Secondary Tasks

Driving is a visual task; any driver behavior that takes the driver's visual attention from the driving environment is likely to increase the risk of a crash. Many new intelligent transportation systems telematics applications are now being installed in cars, and some of these may result in substantial degradation of visual attention. Accurately capturing driver eye glance behavior during the evaluation of these devices is critical to assessing the safety impact of engaging in secondary tasks while driving. The development of a methodology is reported; it reliably determines on-road and off-road eye glance duration by using video data collected during highway driving evaluation trials while drivers were engaged with secondary tasks. Human raters determined glance location and duration by detailed coding of the driver's eye movements on the videos. The raters agreed most of the time, but sufficient disagreement suggested that using the consensus of multiple raters significantly improved the reliability of the resulting eye glance data.

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