Download or read book Estimation of Hybrid Models for Real time Crash Risk Assessment on Freeways written by Anurag Pande and published by . This book was released on 2005 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thorough review of the literature suggested that existing real-time crash ‘prediction’ models (classification or otherwise) are generic in nature, i.e., a single model has been used to identify all crashes (such as rear-end, sideswipe, or angle), even though traffic conditions preceding crashes are known to differ by type of crash. Moreover, a generic model would yield no information about the collision most likely to occur.
Download or read book Real time Crash Prediction of Urban Highways Using Machine Learning Algorithms written by Mirza Ahammad Sharif and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools. The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study's selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring. Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution. Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately. The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well. This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase.
Download or read book Understanding Freeway Crashes Through Data driven Solutions written by John Eugene Ash and published by . This book was released on 2021 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic safety has been and continues to be one of the most active research areas within transportation engineering as government agencies consistently name safety their top priority. While fundamental problems in the field (e.g., crash frequency modeling) often remain the same, advances in statistical methodologies, data availability, and computing continue to enable new solutions to these problems, as well as options for framing these problems in a new and different manner. Notably, real-time crash prediction modeling (RTCPM) has been an area gaining attention over recent years. RTCPM studies the relationship between crash risk and changes in traffic conditions (measured by different sensors) over short-duration time periods; it thus assumes the occurrence of a crash is related to the traffic conditions occurring in some time period before the crash takes place. While several studies have indicated correlation between traffic conditions and crashes, there is still much work to be done especially when it comes to critical evaluation of appropriate study design and application of traffic sensing data to derive appropriate and representative features describing traffic conditions. This dissertation examines this question, along with others related to crash frequency modeling as part of a broader effort to investigate and gain a better understanding of the nature of the relationship between traffic operations and crashes, as well as better understanding of variation in crash frequency estimates. A key component of the RTCPM effort in this work is application of probe vehicle trajectory data derived from GPS trace points provided by mobile location services, consumer GPS devices, and commercial vehicle transponders. Such data have not been used in this application before (to the author’s knowledge) and provide finer spatial/temporal measurement resolution than obtainable through conventional traffic sensing infrastructure (e.g., loop detectors). Use of this trajectory data also provides novelty in that it (1) only describes a sample of the traffic stream, so thus, there are questions as to if it can be used to make population-level inference and (2) the dataset is substantially larger than that used in previous studies, necessitating an efficient data processing method. The RTCPM component of this study takes a comprehensive look at study design, feature extraction, modeling techniques, and interpretation of results. A final component of this dissertation focuses on how to better understand and account for variation in crash frequency modeling efforts. The bulk of existing studies produce point estimates for crash frequency, which only tell part of the story. At their core, crash frequency models produce estimates for a hierarchy of parameters, each of which can exhibit substantial variation. As such, this study derives confidence and prediction intervals for several types of mixed-Poisson models commonly used for crash frequency estimation in order to better capture and show the variation associated with crash estimates as one varies different factors. This study begins with the formulation of a mixed-Poisson model and discussion of several key mixture distributions used in crash frequency modeling efforts. Then, the intervals are derived based on the variance of the safety (also known as the Poisson parameter), and a case study is presented for a real crash dataset to show how the method can be applied, as well to demonstrate the variation in estimates between and within models.
Download or read book Laser Scanning Systems in Highway and Safety Assessment written by Biswajeet Pradhan and published by Springer. This book was released on 2019-04-02 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.
Download or read book Advanced Multimedia and Ubiquitous Engineering written by James J. Park and published by Springer. This book was released on 2018-11-28 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the combined proceedings of the 12th International Conference on Multimedia and Ubiquitous Engineering (MUE 2018) and the 13th International Conference on Future Information Technology (Future Tech 2018), both held in Salerno, Italy, April 23 - 25, 2018. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing.
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:
Download or read book Safe Mobility written by Dominique Lord and published by Emerald Group Publishing. This book was released on 2018-04-18 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book increases the level of knowledge on road safety contexts, issues and challenges; shares what can currently be done to address the variety of issues; and points to what needs to be done to make further gains in road safety.
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.
Download or read book Artificial Intelligence in Highway Safety written by Subasish Das and published by CRC Press. This book was released on 2022-09-29 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.
Download or read book Advanced Statistical Modeling of the Frequency and Severity of Traffic Crashes on Rural Highways written by Irfan Uddin Ahmed and published by . This book was released on 2022 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of practitioners working on traffic safety is to reduce the number and severity of crashes. The Highway Safety Manual (HSM) provides practitioners with analytical tools and techniques to estimate the expected crash frequency and severity with the aim to identify and evaluate safety countermeasures. Expected crash frequency can be estimated using Safety Performance Functions (SPFs) provided in Part C of the HSM. The HSM provides simple SPFs which are developed using the most frequently used crash counts model, the negative binomial regression model. The rural nature of Wyoming highways coupled with the mountainous terrain (i.e., challenging roadway geometry) make the HSM basic SPFs unsuitable to determine crash contributing factors for Wyoming conditions. In this regard, the objective of this study is to implement advanced statistical methods such as the different functional forms of Negative Binomial, and Bayesian approach, to develop crash prediction models, investigate crash contributing factors, and determine the impact of safety countermeasures. Bayesian statistics in combination with the power of Markov Chain Monte Carlo (MCMC) sampling techniques provide frameworks to model small sample datasets and complex models at the same time, where the traditional Maximum Likelihood Estimation (MLE) based methods tend to fail. As such, a novel No-U-Turn Sampler for Hamiltonian Monte Carlo (NUTS HMC) sampling technique in a Bayesian framework was utilized to investigate the crash frequency, injury severity of crashes on the interstate freeways and some rural highways in Wyoming. The Poisson and the Negative Binomial (NB) models are the most commonly used regression models in traffic safety analysis. The advantage of the NB model can be further enhanced by providing different functional forms of the variance and the dispersion structure. The NB-2 is the most common form of the NB model, typically used in developing safety performance functions (SPFs) largely due to the mean-variance quadratic relationship. However, studies in the literature have shown that the mean-variance relationship could be unrestrained. Another introduced formulation of the NB model is NB-1, which assumes that there is a constant ratio linking the mean and the variance of the crash frequencies. A more general type of the NB model is the NB-P model, which does not constrain the mean-variance relationship. Thus, leveraging the power of this unrestrained mean-variance relationship, more accurate safety models could be developed, and these would lead to more accurate estimation of crash risk and benefits of potential solutions. This study will help practitioners to implement advanced methodologies to solve traffic safety problems of rural highways that have plagued the researchers for a long time now. The methodologies proposed in this study will help practitioners to replace the outdated and inefficient traditional models and obtain more accurate traffic safety models to predict crashes and the resulting crash injury severity. Moreover, this research quantified the safety effectiveness of some unique countermeasures on rural highways.
Download or read book Computational Logistics written by Dario Pacino and published by Springer. This book was released on 2013-09-19 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Computational Logistics, ICCL 2013, held in Copenhagen, Denmark, in September 2013. The 19 papers presented in this volume were carefully reviewed and selected for inclusion in the book. They are organized in topical sections named: maritime shipping, road transport, vehicle routing problems, aviation applications, and logistics and supply chain management.
Download or read book Applications of Machine Learning written by Prashant Johri and published by Springer Nature. This book was released on 2020-05-04 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.
Download or read book A Deep Learning Approach for Real time Crash Risk Prediction at Urban Arterials written by Pei Li and published by . This book was released on 2020 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. Four urban arterials in Orlando, FL are selected to conduct a case study. Different types of data are utilized to predict the crash risk, including traffic data, signal timing data, and weather data. Various data preparation techniques are applied also. In addition, the synthetic minority over-sampling technique (SMOTE) is used for oversampling the crash cases to address the data imbalance issue. The LSTM-CNN is fine-tuned on the training data and validated on the test data via different metrics. In the end, five other benchmarks models are also developed for model comparison, including Bayesian Logistics Regression, XGBoost, LSTM, CNN, and Sequential LSTM-CNN. Experimental results suggest that the proposed LSTM-CNN outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this thesis indicate the promising performance of using LSTM-CNN to predict real-time crash risk at arterials.
Download or read book Highway Safety Analytics and Modeling written by Dominique Lord and published by Elsevier. This book was released on 2025-05-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highway Safety Analytics and Modeling, Second Edition comprehensively covers the key elements for effective transportation engineering and policy decisions based on highway safety data analysis in a single reference. It includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating results. It discusses the challenges of working with crash and naturalistic data, identifies problems, and proposes well-researched methods to solve them. It examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. This thoroughly updated second edition updates the material contained in the book based on the latest advancements in highway safety research as well as feedback from readers. It includes entirely new sections on topics such as digital twins as a source of data, model validation, extreme value models, temporal instability, joint crash frequency and severity modeling, sample size, quasi-induced exposure method, autonomous vehicle safety estimate, and more. This book serves as a valuable reference for students, researchers, and practitioners alike. It provides more examples and exercises to help in using the book for courses, and it continues to complement the Highway Safety Manual (HSM) published by the American Association of State Highway and Transportation Officials (AAHSTO), thus helping in the training of engineers and practitioners to better understand the concepts and methods outlined in the forthcoming HSM. - Offers a better understanding of the nuances associated with safety data (such as low sample mean, small sample size, and repeated measurement) - Provides examples and exercises not available in research papers as well as learning aids such as online datasets and slides - Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials
Download or read book Highway Safety written by and published by . This book was released on 2001 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Research Record contains the following papers: Incorporating crash risk in selecting congestion-mitigation strategies : Hampton Roads area (Virginia) case study (Garber, NJ and Subramanyan, S); Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections (Abdelwahab, HT and Abdel-Aty, MA); Transferability of models that estimate crashes as a function of access management (Miller, JS, Hoel, LA, Kim, S and Drummond, KP); Sensor-friendly vehicle and roadway cooperative safety systems : benefits estimation (Misener, JA, Thorpe, C, Ferlis, R, Hearne, R, Siegal, M and Perkowski, J); Interstate highway crash injuries during winter snow and nonsnow events (Khattak, AJ and Knapp, KK); Simulation of road crashes by use of systems dynamics (Mehmood, A, Saccamanno, F and Hellinga, B); Longitudinal analysis of fatal run-off-road crashes, 1975 to 1997 (McGinnis, RG, Davis, MJ and Hathaway, EA); Injury severity in multivehicle rear-end crashes (Khattack, AJ); Computing and interpreting accident rates for vehicle types driver groups (Hauer, E); Geographics information system-based accident data management for Mexican federal roads (Mendoza, A, Mayoral, EF, Vicente, JL and Quintero, FL); Bayesian identification of high-risk intersections for older drivers via gibbs sampling (Davis, GA and Yang, S); Automated accident detection system (Harlow, C and Wang, Y); Evaluation of inexpensive global positioning system units to improve crash location data (Graettinger, AJ, Rushing, TW and McFadden, J).
Download or read book Relationship Between Speed Metrics and Crash Frequency and Severity written by Kristin Kersavage and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Reducing the number and severity of crashes on highways and streets is of high importance for government officials and transportation professionals in the United States. Substantial research has focused on various speed metrics, such as operating speeds and the posted speed limit, and their relationship to safety, such as crash frequency and crash severity. Crash severity is the safety measure most often linked to measures of speed and is based on dissipation of kinetic energy. However, many aspects of the relationships between speed metrics and crash frequency and risk have yet to be studied in depth, so a complete understanding of speeding-related crashes is unknown. Design speeds are used to establish geometric design criteria, and operating speed results from the geometric design process. Posted speed limits may be established based on operating speeds or by statute. When posted speed limits are inconsistent with design or operating speeds, road safety performance may be affected. A more complete understanding of the relationship between safety performance and operating speeds, posted speed limits, and design speeds may produce rational speed limits and lead to improved safety performance on roadways.This research combined real-time vehicle probe speed data, roadway inventory data, and crash data to assess crash risk and crash frequency.This thesis first determined the risk of a crash on two-lane rural highways based on operating speed metrics, differences between speed metrics, and traffic volume data. Results from the crash risk analysis indicate that operating speeds in 1-minute and 5-minute averages improve the statistical fit and prediction of binary logistic regression models. Higher traffic volumes and operating speeds higher than either the road average speed or road reference speed were associated with increased crash risk. Whereas, variations in travel speeds between vehicles were associated with decreased crash risk. This thesis also analyzed the frequency of crashes on horizontal curve segments of two-lane rural roadways using operating speed data, differences among speed metrics, traffic volume data, roadway inventory data, and crash data. Negative binomial regression models improve the statistical fit and prediction of crash frequency models compared to random-effects negative binomial regression. Generally, increases in the differences between operating speed and road average speed and the differences between operating speed and inferred design were associated with an increase in crash frequency. Increases in the differences between inferred design speed and posted speed limit were also associated with an expected increase in crash frequency; however, increases in the operating speed variance and in the difference between operating speeds and posted speed limit were associated with an expected decrease in crash frequency.
Download or read book Emerging Cutting Edge Developments in Intelligent Traffic and Transportation Systems written by M. Shafik and published by IOS Press. This book was released on 2024-03-05 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent and development of AI and other new technologies, traffic and transportation have changed enormously in recent years, and the need for more environmentally-friendly solutions is also driving innovation in these fields. This book presents the proceedings of ICITT 2023, the 7th International Conference on Intelligent Traffic and Transportation, held from 18-20 September 2023 in Madrid, Spain. This annual conference is becoming one of the leading international conferences for presenting novel and fundamental advances in the fields of intelligent traffic and transportation. It also serves to foster communication among researchers and practitioners working in a wide variety of scientific areas with a common interest in intelligent traffic and transportation and related techniques. ICITT welcomes scholars and researchers from all over the world to share experiences and lessons with other enthusiasts, and develop opportunities for cooperation. The 27 papers included here represent an acceptance rate of 64% of submissions received, and were selected following a rigorous review process. Topics covered include autonomous technology; industrial automation; artificial intelligence; machine, deep and cognitive learning; distributed networking; transportation in future smart cities; hybrid vehicle technology; mobility; cyber-physical systems; design and cost engineering; enterprise information management; product design; intelligent automation; ICT-enabled collaborative global manufacturing; knowledge management; product-service systems; optimization; product lifecycle management; sustainable systems; machine vision; Industry 4.0; and navigation systems. Offering an overview of recent research and current practice, the book will be of interest to all those working in the field.
