A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Juliette Ugirumurera; Joseph Severino; Karen Ficenec; Yanbo Ge; Qichao Wang; Lindy Williams; Junghoon Chae; Monte Lunacek; Caleb Phillips

doi:10.1016/j.tra.2021.07.013

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Juliette Ugirumurera, Joseph Severino, Karen Ficenec, Yanbo Ge, Qichao Wang, Lindy Williams, Junghoon Chae, Monte Lunacek, Caleb Phillips

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4 Scopus citations

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.

Original language	English
Pages (from-to)	130-150
Number of pages	21
Journal	Transportation Research Part A: Policy and Practice
Volume	153
DOIs	https://doi.org/10.1016/j.tra.2021.07.013
State	Published - Nov 2021

Funding

This work was supported by the U.S. Department of Energy (U.S. DOE) and Dallas-Fort Worth International Airport (DFW). The contents of this paper reflect the view of the authors who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of the U.S. DOE. This work was only made possible the official view or policies of the U.S. DOE. This work was only made possible other partners including North Central Texas Council of Governments and American Airlines. This research team acknowledges and appreciates particular guidance and technical support from Robert Horton, Esther Chitsinde, Kris Russell, Sarah Ziomek, Zoe Bolack, Jannette Benefee, Greg Royster, Richard Gurley, Smitha Radhakrishnan, and Stefan Hildebrand.

Keywords

Airport
Congestion
Curbside modeling
Microscopic simulation
Traffic management
Travel modes

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10.1016/j.tra.2021.07.013

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title = "A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport",

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{\textquoteright} 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.",

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year = "2021",

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AU - Severino, Joseph

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AU - Ge, Yanbo

AU - Wang, Qichao

AU - Williams, Lindy

AU - Chae, Junghoon

AU - Lunacek, Monte

AU - Phillips, Caleb

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ER -

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

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