Understanding Factors Affecting Arterial Reliability Performance Metrics

In recent years, the importance of travel time reliability has become equally important as average travel time. However, the majority focus of travel time research is average travel time or travel time reliability on freeways. In addition, the identification of specific factors (i.e., peak hours, nighttime hours, etc.) and their effects on average travel time and travel time variability are often unknown. The current study addresses these two issues through a travel time-based study on urban arterials. Using travel times collected via Bluetooth data, a series of analyses are conducted to understand factors affecting reliability metrics on urban arterials. Analyses include outlier detection, a detailed descriptive analysis of select corridors, median travel time analysis, assessment of travel time reliability metrics recommended by the Federal Highway Administration (FHWA), and a bivariate Tobit model. Results show that day of the week, time of day, and holidays have varying effects on average travel time, travel time reliability, and travel time variability. Results also show that evening peak hours have the greatest effects in regards to increasing travel time, nighttime hours have the greatest effects in regards to decreasing travel time, and directionality plays a vital role in all travel time-related metrics

Incorporating Conditional ß-Mean based Equity Metric in Coverage based Facility Location Problems

A classical maximum coverage facility location problem (MCLP) tries to maximize the coverage by serving all possible demands within the specified coverage radius. Depending on the applications, several families of MCLP variants have been developed - each with its unique set of additional constraints. One specific application is locating Emergency medical service (EMS) units. EMS units such as ambulances or drones with emergency supplies are located using models such as the maximum expected survival location problem (MEXSLP). However, a classical MEXSLP model tends to locate EMS units near densely populated regions, resulting in increased response times for lowly populated regions. If equity is to be taken into consideration, the EMS units would be placed more uniformly across the region which might result in reduced overall coverage. In this thesis, a conditional expectation measure called the conditional ß-mean (CBM) is considered and integrated into the facility location models. First, the CBM measure is integrated into the MCLP, resulting in the development of a new MCLP model. Using an alternative and more intuitive definition for CBM, another modified MCLP model incorporated with CBM is developed. These new MCLP models incorporated with CBM are single objective facility location models with flexible coverage radius constraints. Two standard test cases and a Portland Metropolitan region dataset are used to test the MCLP models. A simple greedy based heuristic is developed, and its performance is compared to a state-of-the-art Mixed Integer Programming solver Gurobi. The integration of CBM into the MCLP model has improved its coverage. The proposed heuristic provides reliable solutions and saves considerable computational time. Using the same CBM measure, equity is incorporated into the MEXSLP model. We propose two different approaches to estimate CBM and develop two modified MEXSLP models improving the quality of service provided to outlying and lowly populated areas. The two MEXSLP models modified with CBM are linear, and the decision maker has the leverage to chose the level of equity in the solution using the parameter ß. The same Portland dataset is used for testing the MEXSLP models. Integration of CBM into the MEXSLP has improved the service in Portland suburbs by locating few EMS units closer to them