SAFETY IMPLICATIONS OF ROADWAY DESIGN AND MANAGEMENT: NEW EVIDENCE AND INSIGHTS IN THE TRADITIONAL AND EMERGING (AUTONOMOUS VEHICLE) OPERATING ENVIRONMENTS

2019-08-13T16:47:10Z (GMT) by Sikai Chen

In the context of highway safety factors, road geometrics and pavement condition are of particular interest to highway managers as they fall within their direct control and therefore can be addressed through highway projects. In spite of the preponderance of econometric modeling in highway safety research, there still remain areas worthy of further investigation. These include 1) the lack of sufficient feedback to roadway preservation engineers regarding the impacts of road-surface condition on safety; 2) the inadequate feedback to roadway designers on optimal lane and shoulder width allocation; 3) the need for higher predictive capability and reliability of models that analyze roadway operations; and 4) the lack of realistic simulations to facilitate reliable safety impact studies regarding autonomous vehicles (AV). In an attempt to contribute to the existing knowledge in this domain and to throw more light on these issues, this dissertation proposes a novel framework for enhanced prediction of highway safety that incorporates machine learning and econometrics with optimization to evaluate and quantify the impacts of safety factors. In the traditional highway operating environment, the proposed framework is expected to help agencies improve their safety analysis. Using an Indiana crash dataset, this dissertation implements the framework, thereby 1) estimating the safety impacts of the road-surface condition with advanced econometric specifications, 2) optimizing space resource allocations across highway cross-sectional elements, and 3) predicting the fatality status of highway segments using machine learning algorithms. In addition, this dissertation discusses the opportunities and the expected safety impacts and benefits of AV in the emerging operating environment. The dissertation also presents a proposed deep learning-based autonomous driving simulation framework that addresses the limitations of AV testing and evaluation on in-service roads and test tracks.