Fundamental diagrams of airport surface traffic: Models and applications

Yang, Lei; Yin, Suwan; Han, Ke; Haddad, Jack; Hu, Minghua

doi:10.1016/j.trb.2017.10.015

Cited by 50 publications

(24 citation statements)

References 58 publications

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“…In order to reduce fuel consumption and exhaust emissions during taxiing on the airport surface, push-back control strategy [11,12], taxiing trajectory and speed profile optimizations [13,14], and improvement of taxiing mode (e.g., single-engine taxiing, operational tow-outs, and electric taxiing) [15,16] have been comprehensively investigated. During climb and descent, especially in high-density terminal airspace, prior research has focused more on how to make full use of runway capacity, while minimizing noise, total fuel consumption, emissions, and delays [17][18][19], and put forward operating concepts and operating procedures such as Continuous Climbing Operation (CCO) [20], Continuous Descending Operation (CDO) [21], Point Merge (PM) [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

Liu

Xie

et al. 2021

Sustainability

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Investigating potential ways to improve fuel efficiency of aircraft operations is crucial for the development of the global air traffic management (ATM) performance target. The implementation of trajectory-based operations (TBOs) will play a major role in enhancing the predictability of air traffic and flight efficiency. TBO also provides new means for aircraft to save energy and reduce emissions. By comprehensively considering aircraft dynamics, available route limitations, sector capacity constraints, and air traffic control restrictions on altitude and speed, a “runway-to-runway” four-dimensional trajectory multi-objective planning method under loose-to-tight heterogeneous constraints is proposed in this paper. Taking the Shanghai–Beijing city pair as an example, the upper bounds of the Pareto front describing potential fuel consumption reduction under the influence of flight time were determined under different airspace rigidities, such as different ideal and realistic operating environments, as well as fixed and optional routes. In the congestion-free scenario with fixed route, the upper bounds on fuel consumption reduction range from 3.36% to 13.38% under different benchmarks. In the capacity-constrained scenario, the trade-off solutions of trajectory optimization are compressed due to limited available entry time slots of congested sectors. The results show that more flexible route options improve fuel-saving potentials up to 8.99%. In addition, the sensitivity analysis further illustrated the pattern of how optimal solutions evolved with congested locations and severity. The outcome of this paper would provide a preliminary framework for predicting and evaluating fuel efficiency improvement potentials in TBOs, which is meaningful for setting performance targets of green ATM systems.

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Section: Introductionmentioning

confidence: 99%

Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

Liu

Xie

et al. 2021

Sustainability

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“…Studies at the airport of Duesseldorf have shown that a constant speed does not exist: taxiing speed can differ and even speed drops can be seen. One mesoscopic model based on cell transmission [10,11] is the model of Yang et al [12] using empirical data of Guangzhou Baiyun International Airport (China). Although this model discribes the dynamic of airplanes on a high level it is not applicable to the airport of Duesseldorf due to the need of unexceptional uni-directional taxiways and traveling direction for links between taxiways.…”

Section: Cellular Automata For Airplane Trafficmentioning

confidence: 99%

Simulation and Optimization of Ground Traffic on Airports using Cellular Automata

Mazur

Schreckenberg

2018

Coll Dyn

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Due to increasing numbers of airplane operations on international airports in Germany, the efficiency of airport-ground operations becomes more and more important. Some airports, such as the international airport of Duesseldorf, have reached their capacity limit and need major improvements in order to handle the continuously growing number of airplanes and passengers. Further expansion of the airport is impossible because of limited space. To improve airport-ground operations, simulations are useful for evaluating suggestions in advance and for avoiding poor planning. Therefore the CAMAT-Model was developed (Cellular Automaton Model for Airport Traffic), seeking to simulate the dynamics of all airplanes as realistically as possible. Improvements on the current layout as well as new taxiways and new taxiing routes can be simulated.In this paper we present the CAMAT-Model as well as examples of possible improvements at the airport of Duesseldorf and their influence on airplanes' average taxiing times.

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“…In 2017, passenger throughput in China exceeded 1.1 billion, or a 12.9% increase from the previous year, with total aircraft operations across airports reaching 102.49 million nationwide. With the fast-paced growth of air traffic, the Chinese civilian airspace-both near terminals and en route-is under increasing stress, with flight delays a common phenomenon at many locations in the system [7,8]. In response, the Civil Aviation Administration of China (CAAC) has introduced a series of new regulations on flight delays.…”

Section: Introductionmentioning

confidence: 99%

How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study

et al. 2020

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Flight departure delays cost airlines and airports millions of dollars and become a systematic problem. The on-time performance at an airport is connected to and easily affected by delay propagation from previous operations of flights using the airport. In this paper, we employ both Ordinary Least Square (OLS) and quantile regressions to investigate the impact of various influencing factors on flight departure delay. By using historical flight records and weather information, the impacts of delay propagation-related and other factors are quantified to study the correlations between the explanatory and response variables. Three variables, including previous arrival delay, turnaround buffer time, and the first order of a day, are used to examine the propagation effects. We find that aircraft type, flying on a weekday, and being the first flight of a day have significant impacts on short departure delays. Ground buffer is conducive to mitigating delay propagation. For long delays, however, ground buffer cannot work in an efficient way, and the previous arrival effect is more important. Convective weather and aircraft type are the crucial factors in this situation. Interestingly, flying on a weekday suddenly becomes one of the main components under extreme delays. Meanwhile, propagated delay and airport congestion remain significantly impactful on the on-time performance.

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Fundamental diagrams of airport surface traffic: Models and applications

Cited by 50 publications

References 58 publications

Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

Predicting Fuel Consumption Reduction Potentials Based on 4D Trajectory Optimization with Heterogeneous Constraints

Simulation and Optimization of Ground Traffic on Airports using Cellular Automata

How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study

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