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Book Factors Influencing Incident Clearance Times

Download or read book Factors Influencing Incident Clearance Times written by Mu-Han Wang and published by . This book was released on 1991 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Modeling Freeway Incident Clearance Time

Download or read book Modeling Freeway Incident Clearance Time written by Mu-Han Wang and published by . This book was released on 1991 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Application to Chicago expressways.

Book Dynamic Traffic Flow Modeling for Incident Detection and Short term Congestion Prediction

Download or read book Dynamic Traffic Flow Modeling for Incident Detection and Short term Congestion Prediction written by and published by . This book was released on 2005 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this report is to summarize the research activities that were performed during the first year of this research project. In conducting this research, the research team split into several independent groups, each focusing on different aspects of the problem. One group has been focused on using weather and traffic flow conditions as predictors of incident conditions. Their activities are summarized in Chapter II. Other groups have been focused on developing models for producing short-term forecasts of potential congestion, using current measured traffic conditions. The results of these activities are summarized in Chapter III. Finally, the authors are beginning the process of developing a prototype tool that operators can use in a control center to display forecasted conditions. The beginnings of a high-level, functional specification for the tool are provided in Chapter IV.

Book Primary and Secondary Incident Management

Download or read book Primary and Secondary Incident Management written by and published by . This book was released on 2011 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic incidents are a major source of congestion in Virginia. Secondary incidents comprise a relatively small but important portion of all incidents, and relatively little is known about their occurrence, characteristics, and associated delays. The main objectives of this study were to define secondary incidents, understand and analyze the occurrence and nature of such incidents, and develop tools that can comprehensively and continuously analyze primary and secondary incidents at the planning and operational levels, ultimately contributing to congestion management. The scope of the study is limited to freeway incidents in the Hampton Roads (HR) area. The study found that secondary incidents account for nearly 2% of TOC-recorded incidents, using the 2006 data. Of all accidents, 7.5% had associated secondary incidents, 1.5% of disabled vehicles had secondary incidents, and 0.9% of abandoned vehicles had secondary incidents. Despite their relatively low percentages, on average, two to three secondary incidents occur daily in the HR area. Further, the average durations of secondary incidents in HR are 18 minutes, which is 4 minutes longer than the mean duration of other (independent) incidents, indicating that secondary incidents are not necessarily minor "fender benders." The study also found that a 10-minute increase in primary incident duration is associated with 15% higher odds of secondary incidents. This study developed and applied a dynamic queue-based tool (SiT) to identify primary and secondary incidents from historical incident data and incorporated the models developed for incident duration, secondary incident occurrence, and associated delays in an online prediction tool (iMiT). Although the tools developed in this study (SiT and iMiT) are currently calibrated using HR data, the methodology is transferable to other regions of Virginia. The study recommends that (1)VDOT TOC analysts (where available) use primary and secondary incidents as additional performance measures; (2) VDOT TOC analysts (where available) identify secondary incident hot-spots; (3) VDOT's Regional Traffic Operations Managers give priority (in terms of monitoring, patrol coverage, and traveler information dissemination) to secondary incident hot-spots; (4) TOC managers and their staff use the online prediction tool, iMiT; (5) VDOT TOCs continue and expand the use of service patrols to implement aggressive incident clearance procedures (where appropriate), continue and strengthen their outreach to other response agencies using the RCTO or similar mechanisms, and improve incident scene management to avoid distractions from both the same and opposite directions; and (6) VDOT Operations and Security Division staff work to reconstitute the Statewide Incident Management Committee. The benefit of reducing the number of secondary incidents by 25% (an implication of the stated goal of the HR RCTO) was calculated using two methods. The first method resulted in a benefit in terms of reduced incident delay estimated at $1.11 million per year. The second method used slightly different assumptions and resulted in an estimated delay savings of $1.23 million.

Book Developoment of a model for predicting travel time on an urban freeway

Download or read book Developoment of a model for predicting travel time on an urban freeway written by and published by . This book was released on 1974 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Statistical Methods and Crash Prediction Modeling

Download or read book Statistical Methods and Crash Prediction Modeling written by and published by . This book was released on 2006 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Real time Freeway Crash Prediction Using Conditional Logistic Regression Models

Download or read book Real time Freeway Crash Prediction Using Conditional Logistic Regression Models written by 呂悦慈 and published by . This book was released on 2018 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book A Categorical Model for Traffic Incident Likelihood Estimation

Download or read book A Categorical Model for Traffic Incident Likelihood Estimation written by Shamanth Kuchangi and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis an incident prediction model is formulated and calibrated. The primary idea of the model developed is to correlate the expected number of crashes on any section of a freeway to a set of traffic stream characteristics, so that a reliable estimation of likelihood of crashes can be provided on a real-time basis. Traffic stream variables used as explanatory variables in this model are termed as "incident precursors". The most promising incident precursors for the model formulation for this research were determined by reviewing past research. The statistical model employed is the categorical log-linear model with coefficient of speed variation and occupancy as the precursors. Peak-hour indicators and roadway-type indicators were additional categorical variables used in the model. The model was calibrated using historical loop detector data and crash reports, both of which were available from test beds in Austin, Texas. An examination of the calibrated model indicated that the model distinguished different levels of crash rate for different precursor values and hence could be a useful tool in estimating the likelihood of incidents for real-time freeway incident management systems.

Book Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information

Download or read book Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information written by Nancy Dutta and published by . This book was released on 2020 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crash analysis methods typically use annual average daily traffic as an exposure measure, which can be too aggregate to capture the safety effects of variations in traffic flow and operations that occur throughout the day. Flow characteristics such as variation in speed and level of congestion play a significant role in crash occurrence and are not currently accounted for in the American Association of State Highway and Transportation Officials’ Highway Safety Manual. This study developed a methodology for creating crash prediction models using traffic, geometric, and control information that is provided at sub-daily aggregation intervals. Data from 110 rural four-lane segments and 80 urban six-lane segments were used. The volume data used in this study came from detectors that collect data ranging from continuous counts throughout the year to counts from only a couple of weeks every other year (short counts). Speed data were collected from both point sensors and probe data provided by INRIX. The results showed that models that used data aggregated to an average hourly level reflected the variation in volume and speed throughout the day without compromising model quality. Crash predictions for urban segments underwent a 20% improvement in mean absolute deviation for total crashes and a 9% improvement for injury crashes when models using average hourly volume, geometry, and flow variables were compared to the model based on annual average daily traffic. Corresponding improvements over annual average daily traffic models for rural segments were 11% and 9%. Average hourly speed, standard deviation of hourly speed, and differences between speed limit and average speed had statistically significant relationships with crash frequency. For all models, prediction accuracy was improved across all validation measures of effectiveness when the speed components were added. The positive effect of flow variables was true irrespective of the speed data source. Further investigation revealed that the improvement achieved in model prediction by using a more inclusive and bigger dataset was larger than the effect of accounting for spatial/temporal data correlation. For rural hourly models, mean absolute deviation improved by 52% when short counts were added in comparison to the continuous count station only models. The respective value for urban segments was 58%. This means that using short count stations as a data source does not diminish the quality of the developed models. Thus, a combination of different volume data sources with good quality speed data can lessen the dependency on volume data quality without compromising performance. Although accounting for spatial and temporal correlation improved model performance, it provided smaller benefits than inclusion of the short count data in the models. This study showed that it is possible to develop a broadly transferable crash prediction methodology using hourly level volume and flow data that are currently widely available to transportation agencies. These models have a broad spectrum of potential applications that involve assessing safety effects of events and countermeasures that create recurring and non-recurring short-term fluctuations in traffic characteristics.

Book Improved Prediction Models for Crash Types and Crash Severities

Download or read book Improved Prediction Models for Crash Types and Crash Severities written by and published by . This book was released on 2021 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: The release of the Highway Safety Manual (HSM) by the American Association of State Highway and Transportation Officials (AASHTO) in 2010 was a landmark event in the practice of road safety analysis. Before it, the United States had no central repository for information about quantitative road safety analysis methodology. The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 295: Improved Prediction Models for Crash Types and Crash Severities describes efforts to develop improved crash prediction methods for crash type and severity for the three facility types covered in the HSM—specifically, two‐lane rural highways, multilane rural highways, and urban/suburban arterials. Supplemental materials to the Web-Only Document include Appendices A, B, and C (Average Condition Models, Crash Severities – Ordered Probit Fractional Split Modeling Approach, and Draft Content for Highway Safety Manual, 2nd Edition).

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 Collaborative Computing Networking Applications and Worksharing

Download or read book Collaborative Computing Networking Applications and Worksharing written by Honghao Gao and published by Springer. This book was released on 2019-02-06 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 14th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2018, held in Shanghai, China, in December 2018. The 43 full and 19 short papers presented were carefully reviewed and selected from 106 submissions. The papers reflect the conference sessions as follows: vehicular networks; social networks, information processing, data detection and retrieval & mobility, parallel computing, knowledge graph, cloud and optimization & software testing and formal verification; collaborative computing, social networks, vehicular networks, networks and sensors, information processing and collaborative computing, mobility and software testing and formal verification, web services and image information processing, web services and remote sensing.