Recently the domestic aviation industry has been influenced by rapidly growing ultra low-cost carriers (ULCCs). The pattern of airport markets served by ULCCs is incongruous with legacy carriers and low-cost airlines alike. Existing literature, however, is limited for North American ULCCs: research has only recently begun to identify them separately from mainstream low-cost carriers. This study sought to understand the market factors that influence ULCC service decisions. The relationship between ULCC operations and airport market factors was analyzed using three methods: mapping 2019 flight data for four ULCCs combined, two regression analyses to evaluate variables, and three case studies examining distinct scenarios through interviews with airport managers. Enplanement data were assembled for every domestic airport offering scheduled service in 2019. Independent variables were collected for each Part 139 airport. The first model estimated an ordinary least squares regression model to analyze ULCC enplanements. The second model estimated a binary logistic equation for presence of ULCC service. Case studies for Bellingham, Waco, and Lincoln were selected using compelling airport factors and relevant ULCC experience. Maps of ULCC enplanements revealed concentrations of operations on the East Coast. Both regression analyses showed strong relationships between population and non-ULCC enplanements (two measures of airport market size) and ULCC operations. A significant relationship also existed between tourism and enplanements. In the logit model, distance and competition variables were associated with ULCC presence. Case studies emphasized the importance of airport fees and competition in ULCC preferences, although aeronautical costs were generally not significant in the regressions.

Recently the domestic aviation industry has been influenced by rapidly growing ultra low-cost carriers (ULCCs). The pattern of airport markets served by ULCCs is incongruous with legacy carriers and low-cost airlines alike. Existing literature, however, is limited for North American ULCCs: research has only recently begun to identify them separately from mainstream low-cost carriers. This study sought to understand the market factors that influence ULCC service decisions. The relationship between ULCC operations and airport market factors was analyzed using three methods: mapping 2019 flight data for four ULCCs combined, two regression analyses to evaluate variables, and three case studies examining distinct scenarios through interviews with airport managers. Enplanement data were assembled for every domestic airport offering scheduled service in 2019. Independent variables were collected for each Part 139 airport. The first model estimated an ordinary least squares regression model to analyze ULCC enplanements. The second model estimated a binary logistic equation for presence of ULCC service. Case studies for Bellingham, Waco, and Lincoln were selected using compelling airport factors and relevant ULCC experience. Maps of ULCC enplanements revealed concentrations of operations on the East Coast. Both regression analyses showed strong relationships between population and non-ULCC enplanements (two measures of airport market size) and ULCC operations. A significant relationship also existed between tourism and enplanements. In the logit model, distance and competition variables were associated with ULCC presence. Case studies emphasized the importance of airport fees and competition in ULCC preferences, although aeronautical costs were generally not significant in the regressions.

Universities host a large, young and diverse population that commutes to the same location every day, which makes them ideally suited for public transportation ridership. However, at many universities in the US, this potential for high levels of transit ridership is not being maximized. This research aims to identify the areas where Valley Metro’s public transit service to ASU’s Tempe campus is over- and under-performing in comparison with the overall public transportation service to the entire Phoenix metro area. The hypothesis states that proximity to campus and the convenience of using public transportation would be the two main factors in determining the success of an area’s public transportation service. ASU’s Parking & Transit Services provided confidential data with the addresses of all the students and employees who purchased a parking pass, transit pass and bike registration. With these data, the public transportation mode share for commuters to ASU in each census block group was calculated and compared to the mode share for the general public, which was based on US Census data. The difference between the public transit mode shares of ASU pass holders vs. commuting by the general public was then computed and analyzed to identify areas as hot and cold spots. These heat maps are then compared to the hypothesized factors of proximity to campus and the convenience of public transportation in terms of the light rail line, park-and-ride lots, and number of transfers needed to connect to campus. The transfers were estimated using origin and destination survey data provided by Valley Metro. Results show that the convenience of public transportation was a driving factor in explaining where the transit mode share to ASU is higher than that of the general public, whereas the proximity to campus had little impact on the areas with high ASU-specific transit mode shares. There is an absence of hot spots directly around the campus which is explained by the combination of both high transit share for the non-ASU population and the large share of ASU students and employees using active transportation and free circulator buses this close to campus. These findings are significant specifically to ASU because the university can learn where the transit service is performing well and where it is underperforming. Using these findings, ASU PTS can adjust its pricing, policies, services and infrastructure and work with Valley Metro and the City of Tempe to improve the ridership for both students and employees. Future research can compare more factors to further interpret what leads to success for transit service to university campuses.