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Book Development of Probe Vehicle Incident Detection Algorithm for Arterial Roads Using Discriminant and Neural Network Analysis

Download or read book Development of Probe Vehicle Incident Detection Algorithm for Arterial Roads Using Discriminant and Neural Network Analysis written by Shih-Hsun Tsai and published by . This book was released on 1995 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book A Deep Learning Approach for Spatiotemporal data driven Traffic State Estimation

Download or read book A Deep Learning Approach for Spatiotemporal data driven Traffic State Estimation written by Amr Abdelraouf and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Neural Network Model for Automatic Traffic Incident Detection

Download or read book Neural Network Model for Automatic Traffic Incident Detection written by Hojjat Adeli and published by . This book was released on 2001 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Incident Detection on Arterials Using Neural Network Data Fusion of Simulated Probe Vehicle and Loop Detector Data

Download or read book Incident Detection on Arterials Using Neural Network Data Fusion of Simulated Probe Vehicle and Loop Detector Data written by Kim Thomas and published by . This book was released on 2005 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book A Review of Automatic Incident Detection Techniques

Download or read book A Review of Automatic Incident Detection Techniques written by Marc Solomon and published by . This book was released on 1991 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book The Use of Real time Connected Vehicles and HERE Data in Developing an Automated Freeway Incident Detection Algorithm

Download or read book The Use of Real time Connected Vehicles and HERE Data in Developing an Automated Freeway Incident Detection Algorithm written by Hendry Nyanza Imani and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic incidents cause severe problems on roadways. About 6.3 million highway crashes are reported annually only in the United States, among which more than 32,000 are fatal crashes. Reducing the risk of traffic incidents is key to effective traffic incident management (TIM). Quick detection of unexpected traffic incidents on roadways contribute to quick clearance and hence improve safety. Existing techniques for the detection of freeway incidents are not reliable. This study focuses on exploring the potential of emerging connected vehicles (CV) technology in automated freeway incident detection in the mixed traffic environment. The study aims at developing an automated freeway incident detection algorithm that will take advantage of the CV technology in providing fast and reliable incident detection. Lee Roy Selmon Expressway was chosen for this study because of the THEA CV data availability. The findings of the study show that emerging CV technology generates data that are useful for automated freeway incident detection, although the market penetration rate was low (6.46%). The algorithm performance in terms of detection rate (DR) and false alarm rate (FAR) indicated that CV data resulted into 31.71% DR and zero FAR while HERE yielded a 70.95% DR and 9.02% FAR. Based on Pearson's correlation analysis, the incidents detected by the CV data were found to be similar to the ones detected by the HERE data. The statistical comparison by ANOVA shows that there is a difference in the algorithm's detection time when using CV data and HERE data. 17.07% of all incidents were detected quicker when using CV data compared to HERE data, while 7.32% were detected quicker when using HERE data compared to CV data.

Book Optimal Design and Operation of Freeway Incident Detection service Systems

Download or read book Optimal Design and Operation of Freeway Incident Detection service Systems written by Adolf Darlington May and published by . This book was released on 1975 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes optimization techniques which have been developed and applied for the evaluation of design and operations of freeway incident detection-service systems. The report has four major parts: (1) analysis and design of stationary service systems; (2) analysis and design of incident detection algorithms; (3) analysis and design of incident response systems; and (4) analysis and design of freeway on-ramp traffic-responsive control methodology for normal and incident conditions.

Book Incident Detection Algorithm Evaluation

Download or read book Incident Detection Algorithm Evaluation written by and published by . This book was released on 2001 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Developing a Real time Freeway Incident Detection Model Using Machine Learning Techniques

Download or read book Developing a Real time Freeway Incident Detection Model Using Machine Learning Techniques written by Moggan Motamed and published by . This book was released on 2016 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time incident detection on freeways plays an important part in any modern traffic management operation by maximizing road system performance. The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. An effective incident detection and management operation cannot prevent incidents, however, it can diminish the impacts of non-recurring congestion problems. The main purpose of real-time incident detection is to reduce delay and the number of secondary accidents, and to improve safety and travel information during unusual traffic conditions. The majority of automatic incident detection algorithms are focused on identifying traffic incident patterns but do not adequately investigate possible similarities in patterns observed under incident-free conditions. When traffic demand exceeds road capacity, density exceeds critical values and traffic speed decreases, the traffic flow process enters a highly unstable regime, often referred to as “stop-and-go” conditions. The most challenging part of real-time incident detection is the recognition of traffic pattern changes when incidents happen during stop-and-go conditions. Recently, short-term freeway congestion detection algorithms have been proposed as solutions to real-time incident detection, using procedures known as dynamic time warping (DTW) and the support vector machine (SVM). Some studies have shown these procedures to produce higher detection rates than Artificial Intelligence (AI) algorithms with lower false alarm rates. These proposed methods combine data mining and time series classification techniques. Such methods comprise interdisciplinary efforts, with the confluence of a set of disciplines, including statistics, machine learning, Artificial Intelligence, and information science. A literature review of the methodology and application of these two models will be presented in the following chapters. SVM, Naïve Bayes (NB), and Random Forest classifier models incorporating temporal data and an ensemble technique, when compared with the original SVM model, achieve improved detection rates by optimizing the parameter thresholds. The main purpose of this dissertation is to examine the most robust algorithms (DTW, SVM, Naïve Bayes, Decision Tree, SVM Ensemble) and to develop a generalized automatic incident detection algorithm characterized by high detection rates and low false alarm rates during peak hours. In this dissertation, the transferability of the developed incident detection model was tested using the Dallas and Miami field datasets. Even though the primary service of urban traffic control centers includes detecting incidents and facilitating incident clearance, estimating freeway incident durations remains a significant incident management challenge for traffic operations centers. As a next step this study examines the effect of V/C (volume/capacity) ratio, level of service (LOS), weather condition, detection mode, number of involved lanes, and incident type on the time duration of traffic incidents. Results of this effort can benefit traffic control centers improving the accuracy of estimated incident duration, thereby improving the authenticity of traveler guidance information.

Book Automatic Incident Detection on Urban Arterials

Download or read book Automatic Incident Detection on Urban Arterials written by John Naylor Ivan and published by . This book was released on 1992 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: