Development and Application of a Dynamic Simulation Model for Airport Curbsides

Duncan, Gavin; Johnson, Hugh

doi:10.1061/40530(303)12

Cited by 5 publications

(5 citation statements)

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“…The most relevant prior research on this problem used socalled microscopic, i.e., high-fidelity simulations that modeled the behaviour of individual vehicles [5][6][7]. Such approaches can represent detailed scenarios and evaluate them comprehensively, but are typically data and time intensive to create and computationally expensive to query.…”

Section: Imentioning

confidence: 99%

“…A related team of authors then used discrete event simulation to model curbside vehicular traffic at airports and applied it to Austin-Bergstrom International Airport [26]. Most early efforts relied on simple bespoke models, e.g., dual roadways for loading/unloading and circulation [6] and a mathematical queueing framework [27]; such approaches do not benefit and improve from real-time data about the airports. More recent works have harnessed vastly improved computational resources to develop high fidelity microsimulations that are carefully calibrated to facility data; two notable examples are a study on improving taxi drop-off throughput at a terminal [28] (they modeled a railway station in Nanjing but the principles hold), and a comprehensive modeling framework of Dallas Fort-Worth International Airport [7].…”

Section: Landside Management Policies For Airportsmentioning

confidence: 99%

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Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

Nazir¹,

Vasisht²,

Choudhury³

et al. 2022

Preprint

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We present a data-driven control framework for adaptively managing landside congestion at airports. Ground traffic significantly impacts airport operations and critical efficiency, environmental, and safety metrics. Our framework models a real-world traffic intervention currently deployed at Seattle-Tacoma International Airport (SEA), where a digital signboard recommends drivers to divert to Departures or Arrivals depending on current congestion. We use measured vehicle flow/speed and passenger volume data, as well as time-stamped records of diversionary messages, to build a macroscopic system dynamics model. We then design a model predictive controller that uses our estimated dynamics to recommend diversions that optimize for overall congestion. Finally, we evaluate our approach on 50 real-world historical scenarios at SEA where no diversions were deployed despite significant congestion. Our results suggest that our framework would have improved speed in the congested roadway by up to three times and saved between 20 and 80 vehicle-hours of cumulative travel time in every hour of deployment. Overall, our work emphasizes the opportunity of algorithmic decisionmaking to augment operator judgment and intuition and yield better real-world outcomes.

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Section: Imentioning

confidence: 99%

Section: Landside Management Policies For Airportsmentioning

confidence: 99%

Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

Nazir¹,

Vasisht²,

Choudhury³

et al. 2022

Preprint

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show abstract

“…They also believed that the maximum number of vehicles presented by the time of day is a more interesting measure used in determining the roadway performance. Leigh Fisher Associates (LFA) developed the Leigh Fisher Associates' Curbside Traffic Simulation (LFACTS) model to specifically address the traffic operations at airport curbsides by assuming vehicle arrivals and dwell times and considering that destinations along the curbside are timedependent (27). Similarly, Liu et al proposed a simulation model to calculate the length of curbside by setting uniformly distributed arriving times and dwell times (28).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring the Length of a Two-Lane Curbside Based on Traffic Features

Liu

Zhou

Rong

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The geometric design of airport curbsides is critical for both airport planners and passengers as it affects airport ground traffic capacity and creates airline delays. Rational design of curbsides can yield increased efficiency in the use of road space and time resources. Yet a limited number of studies have reported the impact of traffic features on curbside’s geometric design. This paper provides a two-lane curbside measurement with a simulation model to calculate the length of curbsides, which solves the curbside berth supply and curbside length calculation problems using a refined curbside parking model (RCPM) with constraints of a car-following (CF) model, as well as uniformly distributed speed, dwell time, and parking demand; it thereby unveils the detailed microscopic mechanisms of interaction of traffic features and curbsides’ length. The required lengths of curbsides under various parking demands are obtained after a set of numerical simulations. The result further reveals the significant impacts of car-following and braking behaviors, particularly under the huge parking demands on curbside traffic flow. It also demonstrates that a certain delay is created and a suboptimum level of service (LOS) occurs even though satisfying all parking demands, which indicates that lengthening the curbside and increasing parking supply do not necessarily improve the LOS.

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“…Duncan and Johnson [2] present the Leigh Fisher Associates Curbside Traffic Simulation (LFACTS) model. The model assumes stochastic demand and vehicle speed and uses rule-based dynamics to control and define travel behavior for each individual vehicle, allowing them to interact with other elements in the system.…”

Section: Airport Curbside Simulationmentioning

confidence: 99%

“…While microsimulation models capture a high level of detail and are able to evaluate many policies, they require substantial data. Moreover, most off-the-shelf microsimulation packages are designed for simulating urban transportation systems and are less well-suited to simulate the unique airport roadway system [2]. Hence, we propose a mesoscopic simulation model which combines elements of both micro-and macrosimulation to capture more measures of effectiveness (MOEs) while requiring a reasonable level of detail.…”

Section: Introductionmentioning

confidence: 99%

A Mesoscopic Simulation Model for Airport Curbside Management

Harris

Nourinejad

Roorda

2017

Journal of Advanced Transportation

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Airport curbside congestion is a growing problem as airport passenger traffic continues to increase. Many airports accommodate the increase in passenger traffic by relying on policy and design measures to alleviate congestion and optimize operations. This paper presents a mesoscopic simulation model to assess the effectiveness of such policies. The mesoscopic simulation model combines elements of both microscopic simulation which provides a high level of detail but requires large amounts of data and macroscopic simulation which requires very little data but provides few performance measures. The model is used to simulate scenarios such as double parking, alternative parking space allocation, increased passenger demand, and enforced dwell times at Pearson International Airport in Toronto, Canada. Scenario analysis shows that adjusting model inputs provides reasonable results, demonstrating the value in using this approach to evaluate curbside management policies. The results show that double parking reduces the utilization ratio and the level of service of the outer curbside but cuts down the passenger and vehicle waiting time. Inclement weather conditions reduce the utilization ratio of the inner curbside and the supply of commercial vehicles since it takes them longer to return to the airport. Finally, reducing the allowable parking time at the curbside decreases the average dwell time of private vehicles from 89 seconds to 75 seconds but increases the number of circulating vehicles by 30%.

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Development and Application of a Dynamic Simulation Model for Airport Curbsides

Cited by 5 publications

References 0 publications

Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

Measuring the Length of a Two-Lane Curbside Based on Traffic Features

A Mesoscopic Simulation Model for Airport Curbside Management

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