Research on Space-Time Taxiing Optimization of Aircraft Based on Carbon Emission

doi:10.12141/j.issn.1000-565X.230220

Abstract

Abstract:

The aviation transportation industry is one of the largest industries of carbon emission. With the development of the aviation transportation industry and the strengthening of environmental regulations, the industry is facing enormous environmental pressure. How to reduce the carbon emissions of the air transport industry has attracted wide attention. Aircrafts taxiing in airport scene emits a lot of carbon. Therefor, optimizing aircraft taxiing is an important way to reduce aviation transportation carbon emissions. In addition, to ensure the safety and efficiency of aviation transportation, taxiing on the airport apron must also consider factors such as taxiing waiting time and taxiing conflicts. To solve this problem, this paper proposed an optimization model of space-time taxiing of aircraft considering carbon emission. By deciding the taxiing path in the upper model and the taxiing time in the lower model, the upper and lower interactive optimization minimizes the total carbon emission of aircraft taxiing, the taxiing waiting time of approach aircrafts and the conflict of aircraft taxiing. To improve the quality of the model solutions, an iterative neighborhood search algorithm based on the interaction optimization of upper and lower models was designed to solve the model and the conflict-free neighborhood operator and stochastic perturbation operator were introduced. The proposed model and algorithm were validated with actual operational data from Guangzhou Baiyun International Airport. The experimental results show that compared with the shortest taxiing strategy, the proposed model reduced the total carbon emission for 47 aircrafts by 971 kg, accounting for 1.8% of the total carbon emissions, in the planning period of one and a half hours under the condition of avoiding aircraft taxiing conflict. This research can provide a more environmentally friendly taxiing strategy while improving taxiing efficiency and safety, helping the aviation transportation industry reduce carbon emissions and achieve greater sustainability.

Key words: aircraft taxi, carbon emission, space-time taxiing optimization, time to start taxiing, neighborhood search

CLC Number:

V351.1

JIANG Yu, WANG Yasha, XUE Qingwen, et al. Research on Space-Time Taxiing Optimization of Aircraft Based on Carbon Emission[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 152-159.

Figures/Tables 13

Fig.1

Table 1

Definition of parameters"

参数	定义
$F$	进离场航空器集合
$A$	进场航空器集合， $A ∈ F$
$D$	离场航空器集合， $D ∈ F$
$a i$	进场航空器 $i$ 的计划进场时间，min
$p i$	离场航空器 $i$ 的计划开始滑行时间，min
$d i$	离场航空器 $i$ 的计划离场时间，min
$V$	机场节点集，含跑道端节点、滑行道节点、机坪出入口节点
$S i$	航空器 $i$ 的滑行起始节点， $S i ∈ V$
$E i$	航空器 $i$ 的滑行终止节点， $E i ∈ V$
$M$	极大正数
$φ$	所有航空器滑行冲突的总次数
$T A$	进场航空器的最大等待时间，min
$T D$	离场航空器的最大等待时间，min
$t k, i$	决策变量，进离场航空器 $i$ 在节点 $k$ 的时刻
$T i, k, l$	航空器 $i$ 在节点 $k$ 和 $l$ 之间滑行时间，min
$T i t a x i$	航空器 $i$ 在滑行道上滑行的总时间，min
$n i$	航空器 $i$ 的航空器发动机数
$θ i$	航空器 $i$ 的航空器发动机燃油流率，kg/s
$α$	航空煤油折标准煤系数，取值1.471
$β$	标准煤折合碳排放系数，取值3.155
$d k, l$	节点 $k$ 和 $l$ 之间弧段的距离，m
$d s$	航空器滑行的安全间隔距离，m
$v i$	进离场航空器 $i$ 在滑行道的平均滑行速度
$γ i$	航空器 $i$ 进/出机坪的滑行时间，min
$T i s$	航空器 $i$ 在停机位的最小保障服务时间，min
$b i, j, k$	布尔变量，航空器 $i$ 和 $j$ 先后经过节点 $k$ 则为1，否则为0
$r i, k, l$	布尔变量，航空器 $i$ 先后经过节点 $k$ 和 $l$ 则为1，否则为0

Table 1

Fig.2

Fig.3

Table 2

Fig.4

Table 3

Parameter of algorithm"

模型和算法参数	数值
航空器 $i$ 在滑行道直线弧段的平均滑行速度 $v i$ /（m·s^-1）	8
航空器进场等待时间上限 $T A$ / min	20
航空器离场等待时间上限 $T D$ / min	20
随机扰动概率 $λ$	0.2
单层模型的邻域搜索次数 $w$	100
上下层模型的最大迭代交互次数 $n m a x$	100

Table 3

Table 4

Table 5

Fig.5

Fig.6

Fig.7

Fig.8

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航空器	机型	计划进场时间/min	计划离场时间/min	进场跑道	离场跑道	机位
1	A320	5	65	02R	19	155
2	A320	10	85	02R	02L	164
3	A320	15	95	1	19	208
4	B737	20	85	1	02L	227
︙	︙	︙	︙	︙	︙	︙

航空器编号	进/离场路径	计划进/离场时间/min	滑行开始时间/min	滑行结束时间/min
1	1	5	5.4	11.4
1	1	65	53.8	64.6
2	2	10	11.0	15.8
2	1	85	77.3	84.4
3	3	15	15.8	18.2
3	1	95	81.2	89.0
︙	︙	︙	︙	︙

算法	碳排放总量/kg	总等待时间/s	平均等待时间/s	冲突位置数
对照组	53 955	0	0	40
时空优化模型	52 984	4 856	103.3	0

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