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EBookClubs

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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 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 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 Video Based Machine Learning for Traffic Intersections

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by CRC Press. This book was released on 2023-10-17 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

Book Towards Universality in Automatic Freeway Incident Detection

Download or read book Towards Universality in Automatic Freeway Incident Detection written by Manoel Mendonca de Castro-Neto and published by . This book was released on 2009 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Freeway automatic incident detection (AID) algorithms have been extensively investigated over the last forty years. A myriad of algorithms, covering a broad range of types in terms of complexity, data requirements, and efficiency have been published in the literature. However, a 2007 nationwide survey concluded that the implementation of AID algorithms in traffic management centers is still very limited. There are a few reasons for this discrepancy between the state-of-the-art and the state-of the-practice. First, current AID algorithms yield unacceptably high rates of false alarm when implemented in real-world. Second, the complexities involved in algorithm calibration require levels of efforts and diligence that may overburden Traffic Management Center (TMC) personnel. The main objective of this research was to develop a self-learning, transferable algorithm that requires no calibration. The dynamic thresholds of the proposed algorithm are based on historical data of traffic, thus accounting for variations of traffic throughout the day. Therefore, the novel approach is able to recognize recurrent congestion, thus greatly reducing the incidence of false alarms. In addition, the proposed method requires no human-intervention, which certainly encourages its implementation. The presented model was evaluated in a newly developed incident database, which contained forty incidents. The model performed better than the California, Minnesota, and Standard Normal Deviation algorithms.

Book Evaluation of Adaptive Neural Network Models for Freeway Incident Detection

Download or read book Evaluation of Adaptive Neural Network Models for Freeway Incident Detection written by Dipti Srinivasan and published by . This book was released on 2018 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated incident detection is an essential component of a modern freeway traffic monitoring system. A number of neural network-based incident detection models have been tested independently over the past decade. This paper evaluates the adaptability of three promising neural network models for this problem: multi-layer feed-forward neural network (MLF), basic probabilistic neural network (BPNN) and constructive probabilistic neural network (CPNN). These three models have been developed on an original freeway site in Singapore and then adapted to a new freeway site in California. Apart from their incident detection performances, their adaptation strategies and network sizes have also been compared. Results of this study show that the MLF model has the best incident detection performance at the development site while CPNN model has the best performance after model adaptation at the new site. In addition, the adaptation method for CPNN model is relatively more automatic. The efficient network pruning procedure for the CPNN network resulted in a smaller network size, making it easier to implement it for real-time application. The results suggest that CPNN model has the highest potential for use in an operational automatic incident detection system for freeways.

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 Evaluation of Incident Detection Methodologies

Download or read book Evaluation of Incident Detection Methodologies written by Hani S. Mahmassani and published by . This book was released on 1999 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The detection of freeway incidents is an essential element of an area's traffic management system. Incidents need to be detected and handled as promptly as possible in order to minimize traffic delays. Various algorithms and detection technologies are examined to determine which combinations offer optimized detection performance. This study represents an effort to compile, compare, and rank available incident detection strategies.

Book Video Based Machine Learning for Traffic Intersections

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee (Computer scientist) and published by . This book was released on 2023-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development"--