Development Of Probe Vehicle Incident Detection Algorithm For Arterial Roads Using Discriminant And Neural Network Analysis
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Development Of Probe Vehicle Incident Detection Algorithm For Arterial Roads Using Discriminant And Neural Network Analysis
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Author : Shih-Hsun Tsai
language : en
Publisher:
Release Date : 1995
Development Of Probe Vehicle Incident Detection Algorithm For Arterial Roads Using Discriminant And Neural Network Analysis written by Shih-Hsun Tsai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Traffic engineering categories.
Real Time Data Fusion For Arterial Street Incident Detection Using Neural Networks
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Author : John Naylor Ivan
language : en
Publisher:
Release Date : 1994
Real Time Data Fusion For Arterial Street Incident Detection Using Neural Networks written by John Naylor Ivan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Multisensor data fusion categories.
Detecting Arterial Incidents Using Probe Vehicles
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Author : Nikhil Bhandari
language : en
Publisher:
Release Date : 1994
Detecting Arterial Incidents Using Probe Vehicles written by Nikhil Bhandari and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Incident Detection Algorithm Development On Signalized Urban Arterial Streets
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Author : Jung-Taek Lee
language : en
Publisher:
Release Date : 1997
Incident Detection Algorithm Development On Signalized Urban Arterial Streets written by Jung-Taek Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computer algorithms categories.
Incident Detection On Arterials Using Neural Network Data Fusion Of Simulated Probe Vehicle And Loop Detector Data
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Author : Kim Thomas
language : en
Publisher:
Release Date : 2005
Incident Detection On Arterials Using Neural Network Data Fusion Of Simulated Probe Vehicle And Loop Detector Data written by Kim Thomas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Electronic traffic controls categories.
A Complete Review Of Incident Detection Algorithms Their Deployment
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Author : A. Emily Parkany
language : en
Publisher:
Release Date : 2005
A Complete Review Of Incident Detection Algorithms Their Deployment written by A. Emily Parkany and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computer algorithms categories.
A Deep Learning Approach For Spatiotemporal Data Driven Traffic State Estimation
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Author : Amr Abdelraouf
language : en
Publisher:
Release Date : 2022
A Deep Learning Approach For Spatiotemporal Data Driven Traffic State Estimation written by Amr Abdelraouf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed up to 60 minutes ahead. Att-MED was designed to encode multiple traffic input sequences: short-term, daily, and weekly cyclic behavior. The proposed network produced an average prediction accuracy of 97.5%, which was superior to the compared baseline models. In addition to improving the output performance, the model’s attention weights enhanced the model interpretability. This research additionally explored the utility of low-penetration connected probe-vehicle data for network-wide traffic parameters estimation and prediction on freeways. A novel sequence-to-sequence recurrent graph networks (Seq2Se2 GCN-LSTM) was designed. It was then trained to estimate and predict traffic volume and speed for a 60-minute future time horizon. The proposed methodology generated volume and speed predictions with an average accuracy of 90.5% and 96.6%, respectively, outperforming the investigated baseline models. The proposed method demonstrated robustness against perturbations caused by the probe vehicle fleet’s low penetration rate. Secondly, the application of deep learning for road weather detection using roadside CCTVs were investigated. A Vision Transformer (ViT) was trained for simultaneous rain and road surface condition classification. Next, a Spatial Self-Attention (SSA) network was designed to consume the individual detection results, interpret the spatial context, and modify the collective detection output accordingly. The sequential module improved the accuracy of the stand-alone Vision Transformer as measured by the F1-score, raising the total accuracy for both tasks to 96.71% and 98.07%, respectively. Thirdly, a real-time video-based traffic incident detection algorithm was developed to enhance the utilization of the existing roadside CCTV network. The methodology automatically identified the main road regions in video scenes and investigated static vehicles around those areas. The developed algorithm was evaluated using a dataset of roadside videos. The incidents were detected with 85.71% sensitivity and 11.10% false alarm rate with an average delay of 27.53 seconds. In general, the research proposed in this dissertation maximizes the utility of pre-existing traffic infrastructure and emerging probe traffic data. It additionally demonstrated deep learning algorithms’ capability of modeling complex spatiotemporal traffic data. This research illustrates that advances in the deep learning field continue to have a high applicability potential in the traffic state estimation domain.
Development Of Adaptive Expressway Incident Detection Systems Using Genetic Algorithm And Neural Networks
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Author : Xin Jin
language : en
Publisher:
Release Date : 2000
Development Of Adaptive Expressway Incident Detection Systems Using Genetic Algorithm And Neural Networks written by Xin Jin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Electronic traffic controls categories.
Neural Network Model For Automatic Traffic Incident Detection
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Author : Hojjat Adeli
language : en
Publisher:
Release Date : 2001
Neural Network Model For Automatic Traffic Incident Detection written by Hojjat Adeli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Disabled vehicles on express highways categories.
Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelligent system approach and several innovative algorithms were developed for solution of the freeway traffic incident detection problem employing advanced signal processing, pattern recognition, and classification techniques. The methodology effectively integrates fuzzy, wavelet, and neural computing techniques to improve reliability and robustness.
Comparative Performance Of Freeway Automated Incident Detection Algorithms
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Author : H. Dia
language : en
Publisher:
Release Date : 1996
Comparative Performance Of Freeway Automated Incident Detection Algorithms written by H. Dia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Algorithms categories.