Heterogeneous Data Uncertainties in Risk Management of a Multiscale Transportation Program

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for metrics to guide investments in resource allocation across large networks at several time horizons and geographic scales. This dissertation describes a decision aid to support a multiscale transportation access management program via a risk-cost-benefit tradeoff analysis with heterogeneous sources of data and expertise, addressing incomplete or partially relevant information on regions, decision criteria, crash rates, travel speeds, road condition, project costs, and other factors. The approach quantifies safety improvement, travel-time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across regions and time horizons, are addressed via numerical interval analyses. The integration of methods is demonstrated for 6,000 highway miles of a 43,000 square-mile region and its several sub-regions. The demonstration prioritizes route segments that would benefit from risk assessment and risk management, including (i) right of way purchases, (ii) restriction of access points, (iii) new alignments, (iv) developer proffers, (v) further data collection, (vi) further expert elicitation, (vii) etc. The philosophy of approach is generally applicable to address uncertainties of heterogeneous data in resource allocation and decision making for multiscale systems.