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Methods To Explore Driving Behavior Heterogeneity Using Shrp2 Naturalistic Driving Study Trajectory Level Driving Data
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Book Synopsis Methods to Explore Driving Behavior Heterogeneity Using SHRP2 Naturalistic Driving Study Trajectory-level Driving Data by : Britton Elaine Hammit
Download or read book Methods to Explore Driving Behavior Heterogeneity Using SHRP2 Naturalistic Driving Study Trajectory-level Driving Data written by Britton Elaine Hammit and published by . This book was released on 2018 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding driving behavior and its impact on traffic flow is crucial for maintaining and operating the transportation network. Traffic analysis requires accurate representations of driving behavior—how different drivers drive and how the same driver adjusts to different driving scenarios—for the realistic development of predictive models. Heterogeneity in driving behavior impacts the capacity of the transportation network; therefore, it is crucial to account for this heterogeneity when planning, assessing alternatives, and managing real-time roadway operations. The recent availability of trajectory-level driving data offers researchers and practitioners an unprecedented opportunity to improve the depiction of driving behavior in microsimulation models. A review of literature clearly demonstrates a foundation for research in heterogeneous driving behaviors, yet countless unanswered questions and uninvestigated hypotheses remain. This dissertation is designed to connect the dots between the complex layers of theory, high resolution driving data, and behavioral analytics necessary for successful behavioral research. Starting with the formation of a hypothesis, this dissertation walks through the required steps for collecting data, processing those data, and analyzing driving behavior. At each pivotal point, contributions are made to bridge the gaps between the crucial elements of research, aspiring to add value to current and future studies. These contributions include (i) trajectory-level data sufficiency guidance, (ii) radar-vision data processing algorithms for instrumented vehicle data, (iii) recommendations for transparent and systematic procedures to calibrate car-following models, (iv) a trajectory simulation validation methodology for interpretation and validation of calibration results, and (v) an empirical car-following model developed from an Artificial Neural Network. Ultimately, an analytic framework is developed from these contributions and applied to trajectory-level data available through the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study to investigate the influence of weather conditions on driving behavior. This case study exemplifies the impact that complex human behaviors have in traffic flow theory and the importance of using trajectory-level data to accurately calibrate driving behavior used in microsimulation models.
Book Synopsis Assessing Driver Behavior in the Context of Driving Environment by : Huizhong Guo
Download or read book Assessing Driver Behavior in the Context of Driving Environment written by Huizhong Guo and published by . This book was released on 2021 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driver-related factors have long been an important component in traffic safety. Studies to assess driver behavior and the related safety concerns have primarily used data that does not capture the dynamic nature of driving tasks. The widespread use of naturalistic driving data in recent years allows researchers the capability to capture real-time driver behavior and be able to infer an individual's driving style. However, current studies focus largely on at-risk safety behavior that is often incomplete (e.g., does not consider all types of at-risk safety behavior) and broadly defined regardless of the driving environment. The goal of this dissertation is to assess driver behavior in the context of the driving environment. This is accomplished using data from the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study, which includes more than 3,000 drivers on the road from 2010 to 2013. The concept of "abnormal" driving style is proposed as a complement to "normal" driving style. More specifically, the "abnormality" measures how much a driver deviates from the average driving behavior given the driving context. In this study, the average driving behavior is defined as the average of different vehicle kinematics for drivers that participated in SHRP2 and for a specific environmental context. The study thus aims to examine the association between driving "abnormality" and driver safety. Environmental factors that contribute to the formation of "normal" driving styles were identified in a systematic way through multivariate functional data clustering method and decision trees. The "abnormality" were described by a composite score as well as a set of statistical features that capture the different aspects of a driving style. Path analysis and Structural Equation Modeling method were used to reveal associations between driver safety and driving "abnormality". Results from the study provide insights into driver behavior and implications on driver safety in different environmental contexts. For example, the study showed that drivers who were more likely to crash were also more likely to have unstable lateral control on Urban Interstates. These findings can be integrated in autonomous vehicle algorithms where individual driving styles are considered. It can also provide insights on the development of new technologies to identify risky drivers and to quantify their risky levels.
Book Synopsis Exploring Naturalistic Driving Data for Distracted Driving Measures by : Sherif S. Ishak
Download or read book Exploring Naturalistic Driving Data for Distracted Driving Measures written by Sherif S. Ishak and published by . This book was released on 2017 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Analyzing Driver Behavior Using Data from the SHRP 2 Naturalistic Driving Study by :
Download or read book Analyzing Driver Behavior Using Data from the SHRP 2 Naturalistic Driving Study written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Investigating Driver Lateral Behavior in Adverse Weather Conditions by : Anik Das
Download or read book Investigating Driver Lateral Behavior in Adverse Weather Conditions written by Anik Das and published by . This book was released on 2021 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The presence of adverse weather has a significant negative impact on driving. This research investigated driver lateral behavior under adverse weather via Big Data analytics, Machine Learning, Data Mining in addition to traditional parametric modeling using trajectory-level SHRP2 Naturalistic Driving Study datasets. Initially, driver lane-keeping behavior in adverse weather was examined using ordered logistic regression approach, which indicated that environmental, traffic, driver, and roadway characteristics affect lane-keeping ability. The following study leveraged association rules mining that demonstrated a high association of affected visibility with poor lane-keeping performance. This research was then extended to investigate lane-changing characteristics, which revealed that conservative drivers had longer lane-changing durations in heavy fog compared to clear weather. Moreover, the research provided extensive evaluation into another lateral behavior, named lane-changing gap acceptance, using Multivariate Adaptive Regression Splines. The findings illustrated that relative speed between lane-changing and lead vehicle, acceleration of lane-changing and following vehicle, traffic conditions, and roadway geometries have effects on gap acceptance behavior. Subsequently, emphasis has been provided on developing reliable, accurate, and efficient Machine Learning-based lane change detection and prediction models through a data fusion approach considering different data availability. Finally, the research focused on developing weather-based microsimulation lane change models indicating that weather-specific lane changes were unique and hence, microsimulation models should be weather-specific. The outcomes of this research have significant implications, which could be used in microsimulation model calibration related to lateral behavior and safety improvements in Connected and Autonomous Vehicles, especially in adverse weather.
Book Synopsis The Development of a Holistic Approach to Modeling Driver Behavior by : Rachel Michelle James
Download or read book The Development of a Holistic Approach to Modeling Driver Behavior written by Rachel Michelle James and published by . This book was released on 2019 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: Car-following behavior has been studied since the 1940s. However, complex calibration requirements and challenges with collecting high-resolution data have stunted advancements in this domain. Thus, methodologies to adequately capture naturalistic behavioral heterogeneity are largely missing from the literature. For this dissertation, a sample from the second Strategic Highway Research Program Naturalistic Driving Study was analyzed. This sample contains 665 trips completed on freeways in clear weather conditions. Driver demographics, vehicle CAN bus, and external sensor data are available for each trip. The trajectories in this sample were processed and used to calibrate the Gipps, Intelligent Driver Model, and Wiedemann 99 car-following models. This dissertation seeks to improve how inter-driver heterogeneity in car-following behavior is accounted for in microsimulation models. This dissertation has three primary objectives. Objective 1 identifies which driver attributes are sources of inter-driver heterogeneity. Objective 2 explores the viability of using census-level data to calibrate microsimulation models. Objective 3 develops and evaluates a new mechanism for properly capturing inter-driver heterogeneity in microsimulation: an ensemble car-following model. To achieve these objectives, first, Kruskal-Wallis one-way analysis of variance tests were applied to show statistically significant differences in both the estimated car-following model calibration coefficients and the overall model performance across groups of drivers categorized by commonalities in their driver attributes. Next, the Expectation Maximization clustering algorithm was applied to show that, despite differences in driver behavior, homogeneous driver groups, or groups of drivers that behave similarly, exist in the dataset. Moreover, this dissertation shows that drivers can be classified into their proper homogeneous driver group only knowing their driver specific attributes. Finally, VISSIM was used to implement the homogeneous driver groups in microsimulation. This case study illustrated that when inter-driver differences in driving behavior are explicitly modeled, there are notable impacts on the performance metrics collected from the microsimulation models. These performance metrics are ultimately used by decision makers to evaluate alternatives for transportation funding. Thus, this dissertation provides evidence of the importance of appropriately modeling inter-driver differences to improve the quality of the microsimulation model results and inform better funding allocation decisions
Book Synopsis Driving Volatility in Instantaneous Driving Behaviors by : Jun Liu
Download or read book Driving Volatility in Instantaneous Driving Behaviors written by Jun Liu and published by . This book was released on 2015 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasing amounts of data, generated by electronic sensors from various sources that include travelers, vehicles, infrastructure and the environment, referred to as “Big Data”, represent an opportunity for innovation in transportation systems and toward achieving safety, mobility and sustainability goals. The dissertation takes advantage of large-scale trajectory data coupled with travel behavioral information and containing 78 million second-by-second driving records from 100 thousand trips made by nearly four thousand drivers. The data covers 70 counties across the State of California and Georgia, representing various land use types, roadway network conditions and population. The trajectories cover various driving practices made by vehicles of varied body types as well as different fuel types including conventional vehicles (CVs) consuming gasoline, hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), diesel vehicles and other alternative fuel vehicles (AFVs). The dissertation establishes a framework for the research agenda in instantaneous driving behavior studies using the large-scale trajectory data. The dissertation makes both theoretical and empirical contributions: 1) Developing measures for driving volatility in instantaneous driving behaviors; 2) Understanding correlates of driving volatility in hierarchies & developing applications using large-scale trajectory data. Before using second-by-second trajectories, a study, answering research questions concerning the relationships between data sampling rates and information loss, was conducted. Then, a study for quantifying driving volatility in instantaneous driving behaviors was presented. “Driving volatility”, as the core concept in the dissertation, captures extreme driving patterns under seemingly normal conditions. After that, the dissertation presents a study on exploration of the hierarchical nature of driving volatility embedded in travel survey data using multi-level modeling techniques, and highlights the role of AFVs in travel. Last, the dissertation presents a study for customizing driving cycles for individuals using large-scale trajectory data, given heterogeneous driving performance across drivers and vehicle types. The customized driving cycles help generate more accurate fuel economy information to support cost-effective vehicle choices. The implications of the findings and potential applications to fleet vehicles and driving population are also discussed in the dissertation.
Book Synopsis Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research by : Jonas Bärgman
Download or read book Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research written by Jonas Bärgman and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Naturalistic Driving Study written by and published by . This book was released on 2015 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's second Strategic Highway Research Program (SHRP 2)Report S2-S06-RW-1: Naturalistic Driving Study: Technical Coordination and Quality Control documents the coordination and oversight of participant- and vehicle-based operations for an in-vehicle driving behavior field study collected from naturalistic driving data and associated participant, vehicle, and crash-related data. This report documents the methods used by six site contractors located at geographically distributed data collection sites throughout the United States to securely store data in a manner that protects the rights and privacy of the more than 3,000 participants enrolled in the study.
Book Synopsis Traffic Safety and Human Behavior by : David Shinar
Download or read book Traffic Safety and Human Behavior written by David Shinar and published by Emerald Group Publishing. This book was released on 2017-06-22 with total page 1262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive 2nd edition covers the key issues that relate human behavior to traffic safety. In particular it covers the increasing roles that pedestrians and cyclists have in the traffic system; the role of infotainment in driver distraction; and the increasing role of driver assistance systems in changing the driver-vehicle interaction.
Book Synopsis Machine Learning Models and Algorithms for Big Data Classification by : Shan Suthaharan
Download or read book Machine Learning Models and Algorithms for Big Data Classification written by Shan Suthaharan and published by Springer. This book was released on 2015-10-20 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Book Synopsis Computer Vision Technology in the Food and Beverage Industries by : D-W Sun
Download or read book Computer Vision Technology in the Food and Beverage Industries written by D-W Sun and published by Elsevier. This book was released on 2012-08-13 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of computer vision systems to control manufacturing processes and product quality has become increasingly important in food processing. Computer vision technology in the food and beverage industries reviews image acquisition and processing technologies and their applications in particular sectors of the food industry.Part one provides an introduction to computer vision in the food and beverage industries, discussing computer vision and infrared techniques for image analysis, hyperspectral and multispectral imaging, tomographic techniques and image processing. Part two goes on to consider computer vision technologies for automatic sorting, foreign body detection and removal, automated cutting and image analysis of food microstructure. Current and future applications of computer vision in specific areas of the food and beverage industries are the focus of part three. Techniques for quality control of meats are discussed alongside computer vision in the poultry, fish and bakery industries, including techniques for grain quality evaluation, and the evaluation and control of fruit, vegetable and nut quality.With its distinguished editor and international team of expert contributors, Computer vision technology in the food and beverage industries is an indispensible guide for all engineers and researchers involved in the development and use of state-of-the-art vision systems in the food industry. Discusses computer vision and infrared techniques for image analysis, hyperspectral and multispectral imaging, tomographic techniques and image processing Considers computer vision technologies for automatic sorting, foreign body detection and removal, automated cutting and image analysis of food microstructure Examines techniques for quality control and computer vision in various industries including the poultry, fish and bakery, fruit, vegetable and nut industry
Book Synopsis Driver Distraction by : Michael A. Regan
Download or read book Driver Distraction written by Michael A. Regan and published by CRC Press. This book was released on 2008-10-15 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: Certain activities and events both inside and outside a vehicle can distract drivers and lead to degraded performance. New technologies- such as entertainment, communication, and driver assistance systems- play a significant role in distraction. This unique volume defines driver distraction, discusses various causes, and explains how to measure acceptable and unacceptable levels of distraction. Several chapters address measurement techniques based on performance and epidemiological studies. Most importantly, the text explores ways to mitigate driver distraction as well as countermeasures including vehicle design and effective legislation.
Book Synopsis Longitudinal Driving Behavior by : Saskia Josephina Leontine Ossen
Download or read book Longitudinal Driving Behavior written by Saskia Josephina Leontine Ossen and published by . This book was released on 2008 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Design Guidance for Freeway Mainline Ramp Terminals by : Darren J. Torbic
Download or read book Design Guidance for Freeway Mainline Ramp Terminals written by Darren J. Torbic and published by Transportation Research Board. This book was released on 2012 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: "TRB's National Cooperative Highway Research Program (NCHRP) Report 730: Design Guidance for Freeway Mainline Ramp Terminals presents design guidance for freeway mainline ramp terminals based on current driver and vehicle behavior. Appendixes A to D to NCHRP Report 730 were not published as part of the print or PDF version of the report. They are only available electronically through the following links: Appendix A: Aerial View of Study Locations. Appendix B: Histograms of Observed Acceleration Rates. Appendix C: Verbal Instructions for Behavioral Study. Appendix D: Potential Changes Proposed for Consideration in the Next Edition of the Green Book (Note: Appendix D contains tracked changes that have been intentionally left intact--i.e., not accepted.)" Appendices are available at: http://www.trb.org/Highways1/Blurbs/167516.aspx--
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.
Book Synopsis Where the Weather Meets the Road by : National Research Council
Download or read book Where the Weather Meets the Road written by National Research Council and published by National Academies Press. This book was released on 2004-03-31 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Weather has broad and significant effects on the roadway environment. Snow, rain, fog, ice, freezing rain, and other weather conditions can impair the ability of drivers to operate their vehicles safely, significantly reduce roadway capacity, and dramatically increase travel times. Multiple roadway activities, from roadway maintenance and construction to shipping, transit, and police operations, are directly affected by inclement weather. Some road weather information is available to users currently, however a disconnect remains between current research and operations, and additional research could yield important safety and economic improvements for roadway users. Meteorology, roadway technology, and vehicle systems have evolved to the point where users could be provided with better road weather information through modern information technologies. The combination of these technologies has the potential to significantly increase the efficiency of roadway operations, road capacity, and road safety. Where the Weather Meets the Road provides a roadmap for moving these concepts to reality.
