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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.