A data-driven operational model for traffic at the Dallas Fort Worth International Airport. (July 2021)

• Record Type: Journal Article
• Authors: Lunacek, Monte; Williams, Lindy; Severino, Joseph; Ficenec, Karen; Ugirumurera, Juliette; Eash, Matthew; Ge, Yanbo; Phillips, Caleb
• Is Part Of: Journal of air transport management. Volume 94(2021)
• Publication Date: 2021-07
• Subjects: Microsimulation -- Congestion -- Digital twin -- Machine learning -- Airport -- Traffic; Airlines -- Management -- Periodicals; Aeronautics, Commercial -- Management -- Periodicals; 387.7068
• DOI: 10.1016/j.jairtraman.2021.102061
• Languages: English
• ISSNs: 0969-6997

Abstract

Abstract: Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans. Highlights: Introduce and evaluate methods for airport traffic demand forecasting. Create a microsimulation of the Dallas Fort-Worth International Airport. Combine the demand forecast with the microsimulation in order to provide a complete picture of traffic and its consequences. The result is an operational intelligence tool for exploring policy changes and infrastructure expansion scenarios.