Gate assignment problem (GAP) is the core issue of airport operation management. However, the limited resources of airport gates and the increase of flight scale result in serious problems for gate allocation. In this paper, to provide decision-making support for large-scale GAPs, a model based on gate assignment rules (e.g., flight type constraints, safe time interval constraints, and adjacency conflict constraints) is built to formulate the problem. An improved adaptive parallel genetic algorithm (APGA) is then designed to solve the model. The algorithm is effective because it introduces the idea of elite strategy and parallel design and can adaptively adjust the crossover probability. Moreover, different instances are presented to demonstrate the proposed algorithm. The calculation results of this algorithm are compared with those of standard genetic algorithm and CPLEX, which show that the proposed algorithm has better performance and takes a shorter computational time. In addition, we verify the stability and practicability of the algorithm by repeated experiments on large-scale flight data.
Data-driven car-following modeling is of great significance to traffic behavior analysis and the development of connected automated vehicle (CAV) technology. The existing researches focus on reproducing the car-following process by capturing the behavior of the host vehicle using the information of its nearest preceding vehicle. While the other preceding vehicles may affect the host vehicle as well. To fill the gap above, this paper presents an improved sequence-tosequence deep learning-based (ISDL) car-following model for a CAV system. Firstly, the kinematics information considering the multiple preceding vehicles are organized as the input characteristics. Secondly, an improved sequence-to-sequence deep learning framework is proposed by integrating an encoder with the bidirectional gated recurrent unit (GRU) neural network and a decoder using an attention-based GRU neural network in an end-to-end fashion. Finally, the car-following data with multiple preceding vehicles captured from the NGSIM dataset are employed to train and calibrate the proposed model. Experimental results indicate that the deep learning-based models' performance in learning heterogeneous driving behavior can be enhanced by adding information about multiple preceding vehicles. In addition, the proposed ISDL model outperforms the benchmark car-following models in terms of the accuracy of the simulated speeds and simulated positions. Through tests on platoon simulation, the ISDL model is capable of reshaping the traffic oscillation phenomenon as well.INDEX TERMS Car-following modeling; improved sequence-to-sequence model; information flow topology; gated recurrent unit neural network; connected automated vehicle
With the continuous development of civil aviation, the airline's flight network and fleet size are increasing. In order to ensure that all flights of airlines can successfully perform flight missions and improve the overall operation efficiency of airlines, airlines need to invest more human and material resources to carry out the scheduling of flight schedules. At the same time, in some special cases, when some airports or flights are delayed due to irresistible factors, how to formulate a reasonable flight recovery schedule quickly and effectively is a key that affects the revenue and operation efficiency of airlines. Aiming at the flight delay scenarios under different circumstances, this paper proposes a flight schedule recovery method based on the existing operations of airlines, aiming at maximizing the marginal benefit, that is, the comparison between the marginal revenue of a flight and the marginal cost paid by the airline. This paper intends to improve the efficiency of flight recovery in the case of airline flight delays, and provide some reference for relevant enterprises.
Due to uncertain factors such as weather, large-scale flight delays will occur on the ground at the airport. A reasonable and effective flight sequence can make the airport resume regular operation as soon as possible. However, limited shuttle bus resources will limit the execution of the flight sequence plan. In order to provide decision support for airport authorities, we establish an integrated optimization model for flight sequences that consider shuttle bus resources. The objective function comprehensively considers the operational losses, airline profit losses, and loss of passenger time value. Then we design a multi-chromosome genetic algorithm to solve the model according to the characteristics of the integration problem. In addition, a simulation experiment is conducted based on the data from Yunnan Kunming Changshui International Airport. The simulation experiment results show that compared with the current first-come-first-served (FCFS) strategy, the algorithm reduces the overall loss caused by delays by 7.03%. The results of the simulation example verify the effectiveness of the proposed model and algorithm.
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