<p>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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8855591 |
| Date | 13 August 2019 |
| Creators | Sikai Chen (6941321) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/SAFETY_IMPLICATIONS_OF_ROADWAY_DESIGN_AND_MANAGEMENT_NEW_EVIDENCE_AND_INSIGHTS_IN_THE_TRADITIONAL_AND_EMERGING_AUTONOMOUS_VEHICLE_OPERATING_ENVIRONMENTS/8855591 |
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