Browsing by Subject "Highway safety"
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Item Big data, traditional data and the tradeoffs between predictionand causality in highway-safety analysis(Analytic Methods in Accident Research, 2020-01-25) Bhat, ChandraItem A comprehensive mixed logit analysis of crash type conditional on a crash event(2015-12) Chu, Alice Ai-Ichi; Bhat, Chandra R. (Chandrasekhar R.), 1964-; Zhang, ZhanminThis thesis presents a comprehensive mixed logit model of crash types, where the crash type outcomes are defined by a combination of the nature of collision and the types of vehicles involved in the crash. While prior research in the highway safety field has largely studied and modeled crashes along specific dimensions and categories, this study attempts to model the influence of various explanatory factors on crash type probabilities in a comprehensive and holistic way. The model considers 20 different crash types (alternatives) simultaneously. Using the 2011-2013 General Estimates System (GES) crash database in the United States, this research effort presents a mixed logit model that characterizes the effects of weather and seasonal variables, temporal attributes, roadway characteristics, and driver factors on the probability of observing various crash types. The model reveals the competing influences of various factors on different crash outcomes and the presence of significant unobserved heterogeneity in the manner in which variables affect crash type probabilities. The model offers a framework for developing safety measures and devices that do not result in unintended consequences where a reduction in one crash type probability is met with an increase in another crash type probability.Item Data-driven methodologies for supporting decision-making in roadway safety and pavement management(2023-08) Xu, Yang, M.S. in Engineering; Bhasin, Amit; Li, Jenny; Caldas, Carlos H; Boyles, Stephen DThere has been a significant rise in the utilization of data-driven methods within the contemporary realm of transportation engineering. This trend is primarily attributed to the limitations associated with experience-based methods, such as subjectivity and non-reproducibility. In contrast, data-driven methods have proven to offer a more objective and effective approach to problem analysis, thereby providing decision-makers with a reliable basis for informed decision-making. This present research focuses on two types of data-driven methodologies: geostatistical analyses utilizing geographic information systems (GIS) and cutting-edge algorithms associated with artificial intelligence (AI). In numerical analysis, data provides a means to gain valuable insights into a problem of interest. While AI-oriented methods have been shown in many studies to be more effective than traditional approaches, the accuracy of the analysis still heavily depends on the quality of the data. This dissertation endeavors to shed light on the pivotal role that data plays in both roadway safety analysis and pavement management. To accomplish this, four distinct studies are proposed that examine different aspects of data-driven methods. The studies encompass an evaluation of data consistency in motor vehicle crash databases, the identification of crash hot spots within a road network, a synthesis of advancements in the application of AI algorithms to various activities of pavement management, and an exploration of the relationship between pavement conditions and roadway safety using AI-oriented methods. The knowledge acquired from these studies serves as a foundation for future research, advancements, and the adoption of innovative approaches to enhance the efficiency of safety analysis and pavement management. This research ultimately facilitates informed decision-making, effective resource allocation, and the implementation of cost-effective interventions to enhance roadway safety and optimize pavement management practices.Item A framework for developing road risk indices using quantile regression based crash prediction model(2011-08) Wu, Hui, doctor of civil engineering; Zhang, Zhanmin, 1962-; Murphy, Michael; Machemehl, Randy B.; Waller, Steven T.; Popova, ElmiraSafety reviews of existing roads are becoming a popular practice of many agencies nationally and internationally. Knowing road safety information is of great importance to both policymakers in addressing safety concerns and travelers in managing their trips. There have been various efforts in developing methodologies to measure and assess road safety in an effective manner. However, the existing research and practices are still constrained by their subjective and reactive nature. The goal of this research is to develop a framework of Road Risk Indices (RRIs) to assess road risks of existing highway infrastructure for both road users and agencies based on road geometrics, traffic conditions, and historical crash data. The proposed RRIs are intended to give a comprehensive and objective view of road safety, so that safety problems can be identified at an early stage before they rise in the form of accidents. A methodological framework of formulating RRIs that integrates results from crash prediction models and historical crash data is proposed, and Linear Referencing tools in the ArcGIS software are used to develop digital maps to publish estimated RRIs. These maps provide basic Geographic Information System (GIS) functions, including viewing and querying RRIs, and performing spatial analysis tasks. A semi-parameter count model and quantile regression based estimation are proposed to capture the specific characteristics of crash data and provide more robust and accurate predictions on crash counts. Crash data collected on Interstate Highways in Washington State for the year 2002 was extracted from the Highway Safety Information System (HSIS) and used for the case study. The results from the case study show that the proposed framework is capable of capturing statistical correlations between traffic crashes and influencing factors, leading to the effective integration of safety information in composite indices.