A route-based network simulation framework for airport ground system disruptions
Introduction
Demand for air transportation has steadily increased in recent years, and this increase is projected to continue at a rate of 2.5% per year until 2021 (Kim, 2013a). According to a Federal Aviation Administration (FAA) report, the number of passengers flying on US-based airlines is predicted to reach 1 billion by 2029 (Federal Aviation Administration, 2011). As the volume of flights at airports increases, the effect of delays and cancellations will be exacerbated as more flights and passengers will be impacted by them. While some cancellations and delays in flight schedules are expected to occur under normal conditions, such delays and cancellations become much more frequent in cases of disruptive events such as runway closures and extreme weather. According to the Bureau of Transportation Statistics (BTS), only 76.25% of domestic flights arrived at their destinations on time in 2014, and the other 23.75% were delayed, cancelled, or diverted (Bureau of Transportation Statistics, 2015a). Due to the importance of the air transportation network on local, national, and international economies, these delays and cancellations can cost millions of dollars and significant man-hours. For instance, the Airlines for America reported that the direct aircraft operating cost per block minute was $62.55 in 2016 and delay minutes cost more than $7 billion in aircraft operations for scheduled U.S. passenger airlines only (Airlines for America, 2016). In order to mitigate the financially draining consequences of disruptive events such as severe weather, industrial disputes, and emergencies, airport operations should be managed in an agile way to make the airports resilient against system abnormalities.
In this study, resilience is defined as an airport’s ability to maintain an operational level as close to normal as possible during and immediately after the occurrence of a disruptive event. Based on this definition, resilience can be modeled in airport ground management as a function of taxi-in and taxi-out times. The airport ground network is a complex system of different interconnected components, gates, taxiways, and runways. Taxi times are the elapsed time between departure from the origin airport gate and wheels-off for departure flights, and the time between wheels-on and gate arrival at the destination airport for arrival flights. Due to the interconnected structure of an airport ground system, modeling these components separately may result in significant congestions in the other components even under normal conditions. The problem becomes even more critical in case of uncontrollable disruptive events since these events have different non-linear impacts on the utilization and congestion levels of each component. Hence, a comprehensive model capable of analyzing the dynamics of the each component as a whole system is needed in order to better capture the effect of disruptive events on the airport ground system. Addressing this issue, in this work, we develop a route-based network simulation framework (RuNSim) in order to analyze the adversarial impact of such disruptive events on an airport ground system. The proposed framework simulates an airport ground network in a comprehensive way by considering both its technical aspects (i.e., scheduled flights, existing runway configurations, regulations, etc.) and inherent system uncertainties (i.e., early or late aircraft arrivals/departures from schedules, emergency events, weather conditions, etc.) singlehandedly. To the best of our knowledge, this study is the first to model airport ground network considering runways, taxiways and gates as a whole using a network simulation and to analyze the effect of disruptive events on the taxi-times.
Due to its economic importance and complexity, many studies have been proposed in the literature addressing the modeling and optimization needs of an airport ground system. However, majority of these works mainly focus on the modeling of a particular subsystem within an airport ground system such as runways, taxiways, and gates. Idris et al. (1999) presents various scheduling algorithms for better runway utilization assuming that runways comprise the major bottlenecks of airport ground networks. Others focus on the scheduling of either aircraft landings (Balakrishnan and Chandran, 2006, Boysen and Fliedner, 2011, Chandran and Balakrishnan, 2007) or takeoffs (Idris et al., 1999, Pujet et al., 2000). While these studies provide efficient scheduling to increase runway utilization, they fail to consider the interconnectedness of arrivals and departures due to airports’ runway configurations or regulations. Balakrishnan and Chandran, 2006, Balakrishnan and Chandran, 2010 present a modified dynamic programming method for scheduling flight arrivals considering both arrival and departure flight schedules. However, their method is implemented on a single runway configuration only. The solutions obtained using these studies can often be infeasible due to an airport’s existing runway configuration. For instance, LaGuardia Airport, NY has two crossing runways whose scheduling requires simultaneous handling of its arrival and departure flights. Even for parallel runways, considering landings and takeoffs together may provide more realistic and accurate results compared to their independent scheduling. As such, it is vital to consider these configuration factors when modeling the takeoff and landing processes on runways.
Taxi planning is known to have a significant impact on the taxi-in and taxi-out processes. Hence, several mixed-integer linear programming models have been developed for reducing taxiway delay times (Marin, 2006, Marín and Codina, 2008) to desirable levels. The key limitation of these models is the lack of consideration of uncertainties within the system operations. Clare and Richards, 2011, Anderson and Milutinović, 2013 combined the runway scheduling and taxiway planning problems in order to obtain a more realistic and comprehensive understanding of a ground system, where the model proposed in Anderson and Milutinović (2013) can also handle uncertainties in taxiway speeds, push-back times and congestion modeling. Yet, the model can only find a solution in a reasonable amount of time when the taxiway speed is assumed to be constant. Moreover, the uncertainties associated with runways, which may significantly affect the taxiway route due to their impact on runway exit node, are not included in the aforementioned models.
When it comes to resilience, the aforementioned models are designed by assuming that the system is operating under normal conditions, at all times. In case of an unexpected event such as a taxiway pavement damage, a runway closure, or a change in the runway configuration, these models may result in an infeasible solution due to a non-linear impact of these events on the components of the considered airport ground system. Only a few works in the literature considered weather as a disturbance affecting the components of an airport ground network and used to determine an airport’s resilience (Faturechi et al., 2014, Pejovic et al., 2009). Faturechi et al. (2014) has incorporated the effects of meteorological damage on the ability to use the runways and taxiways in order to optimize an airport’s resilience. However, it fails to consider the other impacts that weather may have on an airport ground system without causing any damage to the physical runway and taxiway networks. Pejovic et al. (2009) developed a logistic regression for the impact of weather on flight delays and overall airport resilience, but did not actually model the airport ground system. Furthermore, neither of these studies investigate how to respond to a situation when safety regulations do not allow some aircrafts to takeoff or land due to weather or changes on the ground network (i.e., runway expansion, runway configuration).
Simulation is one of the most convenient methods for modeling uncertainties and analyzing the effect of unforeseen events in a given system. Some earlier studies mentioned above (Clare and Richards, 2011, Marin, 2006, Marín and Codina, 2008) have developed different simulation models to evaluate the solution quality of their optimization models. However, these models are discrete time simulations and limited to mimicking the technical constraints to analyze an airport’s resilience. In addition to these studies, Cheng (1998) proposed a network simulation model to solve pushback conflicts in apron taxiways. While their model analyzes the conflicts during pushback process, without considering whole taxiway system, the capabilities of that model is limited due to its myopic nature. The authors in Chen, Li, and Gao (2015) developed a simple simulation model for strategic planning of the ground network of an airport. While their model simulates all three main parts of an airport ground network, it does not incorporate the operational and technical details that may have a significant effect on delays and cancellations. While many of these earlier studies aim at decreasing delays and airport congestions or increasing throughput, they view the system in parts without considering the overall airport ground network resilience and the fact that the entire system is connected and every component is affected by changes in any other part of the system. Table 1 compares the literature related to this study and outlines the key limitations that are addressed by our network simulation framework.
Airport ground networks are complex systems that rely heavily on external factors and unpredictable conditions. This makes the analysis of the effect of disruptive events on the airports’ resilience too complicated for analytical models. To this end, in this study, we develop a unified and comprehensive airport ground simulation framework in order to analyze and measure an airport’s resilience under different disruptive events. RuNSim consists of four modules: pre-processing, runway simulation, route-based taxiway simulation, and apron simulation. Each module is designed considering the characteristics of each subsystem in a ground network. However, the modules come together in the simulation to be able to analyze the effect of each component on the other components and/or the entire system. The capabilities of the proposed framework are demonstrated through two different case studies based on real data obtained from the LaGuardia Airport ground system. In these case studies, two disruptive events, namely taxiway pavement network damage and runway closure, are investigated in terms of their impact on airport resilience. Because RuNSim is designed in a generic manner, it is applicable to any airport after redesigning only the airport ground network layout. Our framework provides an analysis of an entire airport ground network, which is critical in measuring the airport’s resilience. In this study, airport resilience is modeled as a function of time. The time that an aircraft spends in an airport ground network (taxi-in and taxi-out times), especially in taxiways, is quite difficult to be expressed with analytical equations due to sophisticated technical and regulatory aspects of the system. In contrast to analytical models, RuNSim estimates taxi-in and taxi-out times by simulating each aircraft incorporating not only regulatory and technical constraints, but also the system uncertainties. This enables the proposed framework to analyze the behavior of the airport ground system when an unexpected event occurs. For example, since any delay in the system may affect the whole airport’s congestion, the proposed framework gives a better understanding of utilization of the components and bottleneck resources. More importantly, it provides valuables insights about how efficient an airport is in terms of airport resilience by quantifying the effect of disruptive conditions on airport ground operations, and evaluating new scheduling strategies, technologies and regulations. Therefore it proves useful for contingency analysis of unexpected disturbances in practice and providing better system-wide solutions.
The rest of the paper is organized as follows. In Section 2, the test network, LaGuardia Airport runway, taxiway and gate system, is described. In Section 3, the four modules of RuNSim pre-processing, runway simulation, route-based taxiway simulation, and apron simulation are explained in detail. Then, the experiments and results of RuNSim are discussed in Section 4. Finally, conclusion and future venues are given in Section 5.
Section snippets
LaGuardia airport ground network
The validation and capabilities of the proposed framework are demonstrated through two different case studies based on real data obtained from the LaGuardia Airport ground network. The LaGuardia Airport (LGA) is the 20th busiest airport in the United States and serves 360 thousand planes and 27 million passengers per year (Port Authority of NY & NJ, 2017). LGA is also consistently recognized as one of the airports with the highest rate of delays in the country. In fact, only 71.34% of flights
Route-based network simulation framework
In this study, we demonstrate the capabilities of the RuNSim using a case study of the LaGuardia Airport ground network. The proposed framework consists of four main modules: pre-processing, runway simulation, route-based taxiway simulation, and apron simulation. The four modules with their associated sub-processes in the airport ground system are shown in Fig. 2. Among these modules, the pre-processing module collates the data that is specific to the considered airport network (in this study,
Results and discussions
We simulated the complete airport ground system using the programming language MATLAB as a tool for a network simulation and visualization. Scenarios were run based on real scheduled times obtained from FlightStats for May 1, 2016 (Flightstats, 2017). When arrival and departure flights are ready for landing and pushback, respectively, the entities are created and visualized in RuNSim as shown in Fig. 12. In the figure, aircrafts 13 and 14 are in the queue for landing, and they are waiting for
Conclusion
In an airport ground management, it is challenging to keep flights on schedule, and it is increasingly difficult when disruptive events occur in the system which can cause significant delays and cancellations to flights. In order to mitigate the effects of these events, it is important to take proper actions to adapt the system to the unexpected events. The developed analytical models and simulation models in the literature failed the analysis of interconnected airport ground system especially
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