Exploring airport traffic capability using Petri net based model
Research highlights
► We present a Petri net model that represent main operations of airport traffic. ► We present GPenSIM, a new tool for Petri net based modeling and simulation. ► GPenSIm allows integration of Petri net models with any MATLAB toolboxes.
Introduction
Petri net is being widely accepted by the research community for modeling and simulation of discrete event-driven systems, mainly due to Petri net’s rigorous modeling techniques (Cheng and Yang, 2009, Shih et al., 2007). There are a number of Petri net tools available for free academic use; see PNWorld (Petri Net World, 2009) for a list of tools. These tools are advanced tools flexible enough to model complex and large systems. This paper presents the development of a novel Petri net simulator. The major reasons for building a new simulator are:
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Flexible: the simulator should enable easy integration with other libraries and tools, so that developing hybrid models (e.g. Fuzzy Petri nets, by integrating Petri net with Fuzzy Logic) becomes easy.
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Extensible: the simulator should enable users writing their own extensions, either extending or rewriting the existing functions or developing new functions.
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Easy of use: for those who does not want to use mathematics when developing a model, the tool should provide a natural language user interface, so that the mathematical details are abstracted away from the user.
General-purpose Petri net simulator (GPenSIM, 2009) is developed in order to satisfy the three criteria stated above (flexible, extensible, and ease of use). GPenSIM is realized as toolbox for the MATLAB platform, so that diverse toolboxes that available in the MATLAB environment (e.g. Fuzzy Logic Toolbox, Control Systems Toolbox) can be used in the models that are developed with GPenSIM. In this paper, GPenSIM is introduced. Secondly, a case study is illustrated on modeling an airport with Petri net for exploring its traffic capability.
Section snippets
Existing tools for discrete event simulation
Many existing tools satisfy some of the three criteria mentioned. Automata, Stateflow, and Petri nets are the well-known tools used for simulation of discrete event systems. Though automata have a strong footing in computer science, the serious shortcoming with it is the lack of structure – the ability to modularize a system (decompose a system into modules) (Avinor, 2009). Stateflow is commercial software that runs in MATLAB environment (Extend, 2009). Stateflow is similar to Petri net;
Architecture of GPenSIM
GPenSIM is designed using the well-proven paradigms in software engineering such as: layered architecture, modular components, and natural language interface.
Methodology for modeling and simulation with GPenSIM
Creating a Petri net model consists of two steps:
- (1)
Defining the static Petri net graph, and
- (2)
Assigning initial dynamics in the main simulation file.
- Step (1)
Defining the Petri net graph in one or more Petri net definition files (PDF): this is the static part. This step consist of three sub-steps:
- Step (1)
- (a)
Identifying the basic elements of a Petri net graph: the places.
- (b)
Identifying the basic elements of a Petri net graph: the transitions, and
- (c)
Connecting the elements with arcs.
- Step (2)
Assigning the dynamics of a Petri net in the
- Step (2)
Case study: event-based model for assessing air traffic at Evenes airport
This case study is to verify whether GPenSIM is capable of modeling and simulation of large industrial applications. The case under scrutiny is the Harstad/Narvik Airport (EVE), Evenes, in the North Norway. To some extend EVE airport functions as a hub for the flight traffic in the North Norway region. There are two more airports that are close to the EVE Airport: Narvik Airport, Framnes and the Bardufoss airport, Bardufoss. Fig. 5 shows the locations of the three airports.
During winter time,
Conclusion
This paper presents a novel Petri net simulator, called General Purpose Petri Net simulator (GPenSIM), for modeling and simulation of discrete event systems. The focus in this study was on how the simulator is devised to achieve the following:
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Flexibility: ability to cooperate with diverse tools and libraries.
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Extensibility: ability to offer support for rewriting or extending existing functions or new functions.
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Ease of use: tool may be based on rigorous mathematics, but the user need not know it.
References (29)
- et al.
A fuzzy Petri nets approach for railway traffic control in case of abnormality: Evidence from Taiwan railway system
Expert Systems with Applications
(2009) - et al.
Efficient use of airport capacity
Transportation Research Part A
(2002) - et al.
Hybrid simulation models: When, why, how?
Expert Systems with Applications
(2010) - et al.
A decision support system for airport strategic planning
Transportation Research Part C
(2004) - Airport (2009). GPenSIM code for airport simulation. Available from:...
- Avinor (2009)....
- et al.
An operations research model for the evaluation of an airport terminal: SLAM (simple landside aggregate model)
Journal of Air Transport Management
(1999) - et al.
A study of aircraft taxi performance for enhancing airport surface traffic control
IEEE Transactions on Intelligent Transportation Systems
(2001) Congestion pricing and capacity of large hub airports: A bottleneck model with stochastic queues
Econometrica
(1995)- et al.
A dynamic programming approach for the airport capacity allocation problem
IMA Journal of Management Mathematics
(2003)
Airport capacity: Representation, estimation, optimization
IEEE Transactions on Control Systems Technology
Airport capacity: Representation, estimation, optimization
IEEE Transactions on Control Systems Technology
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