A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport. (November 2021)

• Record Type: Journal Article
• Authors: Ugirumurera, Juliette; Severino, Joseph; Ficenec, Karen; Ge, Yanbo; Wang, Qichao; Williams, Lindy; Chae, Junghoon; Lunacek, Monte; Phillips, Caleb
• Is Part Of: Transportation research. Volume 153(2021)
• Page Start: 130
• Page End: 150
• Publication Date: 2021-11
• Subjects: Airport -- Curbside modeling -- Traffic management -- Microscopic simulation -- Congestion -- Travel modes; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2021.07.013
• Languages: English
• ISSNs: 0965-8564

Abstract

Abstract: Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations' results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.