Document Type



Doctor of Philosophy (PhD)


Civil Engineering

First Advisor's Name

Priyanka Alluri

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Albert Gan

Second Advisor's Committee Title

Co-Committee Chair

Third Advisor's Name

Mohammed Hadi

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Xia Jin

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Wensong Wu

Fifth Advisor's Committee Title

Committee Member


Network Screening, Prioritization, Multi-Criteria Decision-Making, Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), Fuzzy Analytic Network Process (FANP), Crash Modification Factor (CMF), Roadway Characteristics, Crowdsourced Bicycle Activity Data, Cross-Sectional Analysis, Zero Inflated Negative Binomial, Transferability

Date of Defense



Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods.

After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.





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