Author: Patikhom Cheevarunothai
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 358
Book Description
Development of Methods for Improving Inductance Loop Data Quality and Quantifying Incident-induced Delay on Freeways
Author: Patikhom Cheevarunothai
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 358
Book Description
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 358
Book Description
Quantifying Incident-induced Travel Delays on Freeways Using Traffic Sensor Data
Author: Yinhai Wang
Publisher:
ISBN:
Category : Disabled vehicles on express highways
Languages : en
Pages : 71
Book Description
To quantify incident-induced delay (IID) over a regional freeway network using existing traffic sensor measurements, a new approach for IID estimation was developed in this study. This new approach combines a modified deterministic queuing diagram with short-term traffic flow forecasting techniques to overcome the limitation of the zero vehicle-length assumption in the traditional deterministic queuing theory. A remarkable advantage with this new approach over most other methods is that it uses only volume data from traffic detectors to compute IID and hence is easy to apply. Verification with the video-extracted ground truth IID data found that the IID estimation errors with the new approach were within 6 percent for the two incident cases studied.
Publisher:
ISBN:
Category : Disabled vehicles on express highways
Languages : en
Pages : 71
Book Description
To quantify incident-induced delay (IID) over a regional freeway network using existing traffic sensor measurements, a new approach for IID estimation was developed in this study. This new approach combines a modified deterministic queuing diagram with short-term traffic flow forecasting techniques to overcome the limitation of the zero vehicle-length assumption in the traditional deterministic queuing theory. A remarkable advantage with this new approach over most other methods is that it uses only volume data from traffic detectors to compute IID and hence is easy to apply. Verification with the video-extracted ground truth IID data found that the IID estimation errors with the new approach were within 6 percent for the two incident cases studied.
Dissertation Abstracts International
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 800
Book Description
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 800
Book Description
Quantifying Incident-induced Travel Delays on Freeways Using Traffic Sensor Data
Author: Yinhai Wang
Publisher:
ISBN:
Category : Disabled vehicles on express highways
Languages : en
Pages : 71
Book Description
To quantify incident-induced delay (IID) over a regional freeway network using existing traffic sensor measurements, a new approach for IID estimation was developed in this study. This new approach combines a modified deterministic queuing diagram with short-term traffic flow forecasting techniques to overcome the limitation of the zero vehicle-length assumption in the traditional deterministic queuing theory. A remarkable advantage with this new approach over most other methods is that it uses only volume data from traffic detectors to compute IID and hence is easy to apply. Verification with the video-extracted ground truth IID data found that the IID estimation errors with the new approach were within 6 percent for the two incident cases studied.
Publisher:
ISBN:
Category : Disabled vehicles on express highways
Languages : en
Pages : 71
Book Description
To quantify incident-induced delay (IID) over a regional freeway network using existing traffic sensor measurements, a new approach for IID estimation was developed in this study. This new approach combines a modified deterministic queuing diagram with short-term traffic flow forecasting techniques to overcome the limitation of the zero vehicle-length assumption in the traditional deterministic queuing theory. A remarkable advantage with this new approach over most other methods is that it uses only volume data from traffic detectors to compute IID and hence is easy to apply. Verification with the video-extracted ground truth IID data found that the IID estimation errors with the new approach were within 6 percent for the two incident cases studied.
Developing a Methodology for Quantifying Non-recurring Freeway Congestion Delay
Author: Amy Epps
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 256
Book Description
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 256
Book Description
Developing Methodologies for Quantifying Freeway Congestion Delay
Author: Amy Epps
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 296
Book Description
Publisher:
ISBN:
Category : Traffic congestion
Languages : en
Pages : 296
Book Description
Deliver a Set of Tools for Resolving Bad Inductive Loops and Correcting Bad Data
Author: Xiao-Yun Lu
Publisher:
ISBN:
Category : Vehicle detectors
Languages : en
Pages : 218
Book Description
This project prototyped and demonstrated procedures to find and mitigate loop detector errors, and to derive more valuable data from loops. Specifically, methods were developed to find and isolate out loop data which is "bad" or invalid, so that mitigation means, or "fixes" can be implemented. Methods of extracting very accurate speed (+/- 3mph) and vehicle length data (+/- 1meter) from single loop stations were demonstrated to be much more accurate than current Caltrans practice. The validity of these methods were statistically proven using hundreds of thousands of vehicles. Additionally, more accurate and reliable methods of detecting the onset of both recurring or "incident" based congestion were demonstrated. These methods require access to the unprocessed loop detector card data. This unprocessed data can be acquired from the Log170 program, third party loop readers like the Infotek Wizard, or DRI's ubiquitous "C1 reader". DRI intends to implement many of these methodologies in the C1 reader client software, Videosync.
Publisher:
ISBN:
Category : Vehicle detectors
Languages : en
Pages : 218
Book Description
This project prototyped and demonstrated procedures to find and mitigate loop detector errors, and to derive more valuable data from loops. Specifically, methods were developed to find and isolate out loop data which is "bad" or invalid, so that mitigation means, or "fixes" can be implemented. Methods of extracting very accurate speed (+/- 3mph) and vehicle length data (+/- 1meter) from single loop stations were demonstrated to be much more accurate than current Caltrans practice. The validity of these methods were statistically proven using hundreds of thousands of vehicles. Additionally, more accurate and reliable methods of detecting the onset of both recurring or "incident" based congestion were demonstrated. These methods require access to the unprocessed loop detector card data. This unprocessed data can be acquired from the Log170 program, third party loop readers like the Infotek Wizard, or DRI's ubiquitous "C1 reader". DRI intends to implement many of these methodologies in the C1 reader client software, Videosync.
Developing a Real-time Freeway Incident Detection Model Using Machine Learning Techniques
Author: Moggan Motamed
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
Book Description
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.
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
Book Description
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.
HRIS Abstracts
Author: National Research Council (U.S.). Highway Research Information Service
Publisher:
ISBN:
Category : Highway research
Languages : en
Pages : 600
Book Description
Publisher:
ISBN:
Category : Highway research
Languages : en
Pages : 600
Book Description
Traffic Incident Management Handbook
Author:
Publisher:
ISBN:
Category : Emergency management
Languages : en
Pages : 176
Book Description
Intended to assist agencies responsible for incident management activities on public roadways to improve their programs and operations.Organized into three major sections: Introduction to incident management; organizing, planning, designing and implementing an incident management program; operational and technical approaches to improving the incident management process.
Publisher:
ISBN:
Category : Emergency management
Languages : en
Pages : 176
Book Description
Intended to assist agencies responsible for incident management activities on public roadways to improve their programs and operations.Organized into three major sections: Introduction to incident management; organizing, planning, designing and implementing an incident management program; operational and technical approaches to improving the incident management process.