Download or read book Quantifying Incident induced Travel Delays on Freeways Using Traffic Sensor Data written by Yinhai Wang and published by . This book was released on 2010 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Quantifying Incident induced Travel Delays on Freeways Using Traffic Sensor Data written by Yinhai Wang and published by . This book was released on 2008 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quantifying Incident induced Travel Delays on Freeways Using Traffic Sensor Data written by Yinhai Wang and published by . This book was released on 2011 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Measuring Recurrent and Non recurrent Traffic Congestion written by Alexander Skabardonis and published by . This book was released on 2002 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper describes a methodology and its application to measure total, recurrent, and non-recurrent (incident related) delay on urban freeways. The methodology uses data from loop detectors and calculates the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridorsone in Los Angeles and the other in the Bay Areaindicates that reliable measurement of congestion should also provide measures of uncertainty in congestion. In the two applications, incident-related delay is found to be between 13 to 30 percent of the total congestion delay during peak periods. The methodology also quantifies the congestion impacts on travel time and travel time variability.
Download or read book Development of Methods for Improving Inductance Loop Data Quality and Quantifying Incident induced Delay on Freeways written by Patikhom Cheevarunothai and published by . This book was released on 2008 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Feasibility of Using In Vehicle Video Data to Explore How to Modify Driver Behavior That Causes Nonrecurring Congestion written by Hesham Rakha and published by Transportation Research Board. This book was released on 2011 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This research report - a product of the Reliability focus area of the second Strategic Highway Research Program (SHRP 2) - presents findings on the feasibility of using existing in-vehicle data sets, collected in naturalistic driving settings, to make inferences about the relationship between observed driver behavior and nonrecurring congestion. General guidance is provided on the protocols and procedures for conducting video data reduction analysis. In addition, the report includes technical guidance on the features, technologies, and complementary data sets that researchers should consider when designing future instrumented in-vehicle data collection studies. Finally, a new modeling approach is advanced for travel time reliability performance measurement across a variety of traffic congestion conditions"--Publisher's description.
Download or read book Developing a Methodology for Quantifying Non recurring Freeway Congestion Delay written by Amy Epps and published by . This book was released on 1993 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Traffic Incident Management Handbook written by and published by . This book was released on 2000 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Developing Methodologies for Quantifying Freeway Congestion Delay written by Amy Epps and published by . This book was released on 1994 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Analysis of Large scale Traffic Incidents and en Route Diversions Due to Congestion on Freeways written by Xiaobing Li and published by . This book was released on 2018 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: En route traffic diversions have been identified as one of the effective traffic operations strategies in traffic incident management. The employment of such traffic operations will help relieve the congestion, save travel time, as well as reduce energy use and tailpipe emissions. However, little attention has been paid to quantifying the benefits by deploying such traffic operations under large-scale traffic incident-induced congestion on freeways, specifically under the connected vehicle environment. New Connected and Automated Vehicle technology, known as "CAV", has the potential to further increase the benefits by deploying en route traffic diversions. This dissertation research is intended to study the benefits of en route traffic diversion by analyzing large-scale incident-related characteristics, as well as optimizing the signal plans under the diversion framework. The dissertation contributes to the art of traffic incident management by 1) understanding the characteristics of large-scale traffic incidents, and 2) developing a framework under the CAV to study the benefits of en route diversions. Towards the end, 4 studies are linked together for the dissertation. The first study will be focusing on the analysis of the large-scale traffic incidents by using the traffic incident data collected on East Tennessee major roadways. Specifically, incident classification, incident duration prediction, as well as sequential real-time prediction are studied in detail. The second study mainly focuses on truck-involved crashes. By incorporating injury severity information into the incident duration analysis, the second study developed a bivariate analysis framework using a unique dataset created by matching an incident database and a crash database. Then, the third study estimates and evaluates the benefit of deploying the en route traffic diversion strategy under the large-scale traffic incident-induced congestion on freeways by using simulation models and incorporating the analysis outcomes from the other two studies. The last study optimizes the signal timing plans for two intersections, which generates some implications along the arterial corridor under connected vehicles environment to gain more benefits in terms of travel timing savings for the studies network in Knoxville, Tennessee. The implications of the findings (e.g. faster response of agencies to the large-scale incidents reduces the incident duration, penetration of CAVs in the traffic diversion operations further reduces traffic network system delay), as well as the potential applications, will be discussed in this dissertation study.
Download or read book Incident Modeling with the Use of Video Reidentification written by Ryan Pierce and published by . This book was released on 2004 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major problem on urban freeways is incident induced delay. Over half of the delay present on the freeway system is due to incidents. In order to solve this problem, a method is needed to quantify the delay impacts of incidents. A new method for analyzing the impact of incidents is video ReIdentification (ReID). ReID is a method that tracks individual vehicle travel times along a freeway segment over time by matching vehicles from an upstream camera and a downstream camera. Incident induced delay was calculated by taking the difference in travel times between the incident condition and the normal condition for each collected incident. Incidents were collected in the St. Louis, Missouri area by the use of incident chasing. Incident chasing consisted of a team of two researchers that monitored the traffic reports for incidents. Once an incident was detected, the team would set up two cameras: one upstream and the other downstream from the incident and recorded the vehicles that passed through the incident. The vehicles were then matched to obtain the vehicle travel times during the incident. A total of 18 incidents were analyzed from the St. Louis area. The average delay for all 18 incidents, including the incidents that showed zero delay, was 27,573 veh-min. The maximum and minimum delay was 123,976 veh-min and 0 veh-min, respectively.
Download or read book Quantifying the Potential of Automatic Freeway Incident Detection Using Travel Time Data from AVI Equipped Vehicles written by Geoffrey S. Knapp and published by . This book was released on 1999 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quantifying Travel Time Variability in Transportation Networks written by Stephen David Boyles and published by . This book was released on 2010 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonrecurring congestion creates significant delay on freeways in urban areas, lending importance to the study of facility reliability. In locations where traffic detectors record and archive data, approximate probability distributions for travel speed or other quantities of interest can be determined from historical data; however, the coverage of detectors is not always complete, and many regions have not deployed such infrastructure. This report describes procedures for estimating such distributions in the absence of this data, considering both supply-side factors (reductions in capacity due to events such as incidents or poor weather) and demand-side factors (such as daily variation in travel activity). Two demonstrations are provided: using data from the Dallas metropolitan area, probability distributions fitting observed speed data are identified, and regression models developed for estimating their parameters. Using data from the Seattle metropolitan area, the appropriate capacity reduction applied to planning delay functions in the case of an incident is identified.
Download or read book Estimating Incident Related Congestion on Freeways Based on Incident Severity written by Avinash Kripalani and published by . This book was released on 2006 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: The effects of traffic incidents on metropolitan freeways extend beyond causing congestion and delays. Immediate impacts include decreased productivity, increased pollution and reduced safety on highways. State and local governments spend billions of dollars annually on construction projects and Intelligent Transportation Systems (ITS) in an effort to curb the adverse consequences of incidents and incident related delays. Effective identification and response on highways is one key to reducing the costs associated with traffic incidents. Within the context of a prototype incident identification and response system developed by the University of Virginia's Systems Technology Integration Laboratory (STIL), this research aims to develop a statistical approach to modeling congestion associated with freeway incidents. The ability to predict congestion will provide more information and greater situational awareness to emergency responders and traffic managers, and will allow travelers to make more informed route selection decisions. By combining data from multiple sources, it is possible to match incident severity estimates for freeway incidents with associated traffic flow counts at the time of the accident. Four metrics for freeway congestion were derived from the traffic flow data, and these metrics were then modeled as functions of the incident severity estimates. The results of multiple linear regression analysis showed that quantifiable relationships exist between the congestion metrics and incident severity data such as the number of vehicles involved in an incident as well as the number of serious injuries reported at the scene.
Download or read book Freeway Incidents written by and published by . This book was released on 2011 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report presents an analysis of freeway incidents on the Salt Lake Valley freeway network. Different types of incidents at the most common/difficult locations are analyzed through traffic microsimulation using VISSIM simulation software. The analysis focuses on incident induced freeway delays, but it also looks into other parameters, such as vehicle throughput, travel times and network-wide delays. The goal of this project is to develop a set of incident management strategies that would help TOC operators to make decisions that will optimize their response in terms of time and resulted delay, and minimize users' cost due to delay on the freeway network.
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.
Download or read book Diverted Traffic Measurement written by Ravindra Gudishala and published by . This book was released on 2020 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
