A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.     

Forecasting air passenger demand with a new hybrid ensemble approach

Analyzing and modeling passenger demand dynamic, which has important implications on the management and the operation in the entire aviation industry, are deemed to be a tough challenge. Air passenger demand, however, exhibits consistently complex non-linearity and non-stationarity. To capture more precisely the aforementioned complex behavior, this paper proposes a hybrid approach VMD-ARMA/KELM-KELM for the short-term forecasting, which consists of variational mode decomposition (VMD), autoregressive moving average model (ARMA) and kernel extreme learning machine (KELM). First, VMD is adopted to decompose the original data into several mode functions so as to reduce their complexity. Then, the unit root test (ADF test) is employed to classify all the modes into the stable and unstable series. Meanwhile, the ARMA and the KELM models are used to forecast both the stationary and non-stationary components, respectively. Lastly, the final result is integrated by another KELM model incorporating the forecasting results of all components. In order to prove and verify the feasibility and robustness of the proposed approach, the passenger demands of Beijing, Guangzhou and Pudong airports are introduced to test the performance. Also, the experimental results show that the novel approach does have a more obviously advantage than other benchmark models regarding both accuracy and robustness analysis. Therefore, this approach can be utilized as a convincing tool for the air passenger demand forecasting.     

基于傅里叶级数的铁路客运量预测研究

基于傅里叶级数预测模型,以我国2004—2009年铁路客运量为数据基础,通过将时间序列划分为趋势性与季节性部分,分别采用最小二乘法与傅里叶级数法对两者进行拟合,应用Matlab软件编程,求出预测模型,并进行客运量预测。通过对预测结果的误差分析,结果表明:采用傅里叶级数预测法预测我国铁路客运量的效果较好。     

Air transportation demand forecast through Bagging Holt Winters methods

This paper expands the fields of application of combined Bootstrap aggregating (Bagging) and Holt Winters methods to the air transportation industry, a novelty in literature, in order to obtain more accurate demand forecasts. The methodology involves decomposing the time series into three adding components: trend, seasonal and remainder. New series are generated by resampling the Remainder component and adding back the trend and seasonal ones. The Holt Winters method is used to modelling each time series and the final forecast is obtained by aggregating the forecasts set. The approach is tested using data series from 14 countries and the results are compared with five methodology benchmarks (SARIMA, Holt Winters, ETS, Bagged.BLD.MBB.ETS and Seasonal Naive) using Symmetric Mean Absolute Percentage Error (sMAPE). The empirical results obtained with Bagging Holt Winters methods consistently outperform the benchmarks by providing forecasts that are more accurate.     

SARIMA damp trend grey forecasting model for airline industry

The aim of this paper is to propose a new model that improves the Damp Trend Grey Model (DTGM) with a dynamic seasonal damping factor to forecast routes passengers demand (pax) in the air transportation industry. The model is called the SARIMA Damp Trend Grey Forecasting Model (SDTGM). In the DTGM, the damp trend factor is a static smoothing factor because it does not change over time, and therefore, it cannot capture the dynamic behavior of time series data. For this reason, the modification consists in using the trend and seasonality effects of time series data to calculate a dynamic damp trend factor as time grows. The DTGM damping factor is based on the forecasted data obtained by the GM(1,1) model; otherwise, the SDTGM calculates a seasonal damping factor based on historical data using a large amount of data points for short lead-times. The SDTGM has less uncertainty than the DTGM. The simulation results show that the SDTGM captures the seasonality effect and does not allow the forecast to exponentially grow. The SDTGM forecasts more reasonable routes pax for short lead-times when having a large amount of data points than the DTGM. The United States domestic air transport market data are used to compare the performance of the DTGM against the proposed SDTGM.     

Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting

In this paper, the number of goods subject to inspection at European Border Inspections Post are predicted using a hybrid two-step procedure. A hybridization methodology based on integrating the data obtained from autoregressive integrated moving averages (SARIMA) model in the artificial neural network model (ANN) to predict the number of inspections is proposed. Several hybrid approaches are compared and the results indicate that the hybrid models outperform either of the models used separately. This methodology may become a powerful decision-making tool at other inspection facilities of international seaports or airports.     

A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application

An artificial neural forecasting model is developed for air transport passenger analysis. It uses a preprocessing method that decomposes information to reveal relevant features from the data. It is found that neural processing outperforms the traditional econometric approach and offers generalization on time series behavior, even where there are only small samples.     

北京地铁换乘站客流预测模型研究

根据北京地铁全网一票换乘和一卡通无障碍换乘机制,以及早晚高峰出行等特点,对各种地铁客流预测模型进行分析,研究北京地铁换乘站客流预测模型的应用。依据换乘站不同类型的客流,以北京地铁历史客流和实时客流数据为基础,探讨采用历史平均预测法、基于最小二乘支持向量机时间序列预测法、分峰段混合预测法、基于概率树全路网预测方法等对进站客流、出站客流、换乘客流和站内客流进行预测。     

A passenger demand model for air transportation in a hub-and-spoke network

This paper develops an air passenger model that deals with city-pair demand generation and demand assignment in a single framework. Using publicly available and regularly collected panel data, the model captures both time series and cross-sectional variation of air travel demand. The empirical analysis finds that pattern of correlations among alternatives can be described by a three-level nested logit model. Fare, frequency, flight time, direct routing, on-time performance, income, and market distance have significantly effects on air demand. Correcting for the problem of endogenous air fares using instrumental variables yields more plausible estimates of price sensitivity and value of time.     

Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models

This paper applies recent panel methodology to examine the short-run dynamics, the long-run equilibrium relationships and the Granger causal relationship between economic growth and domestic air passenger traffic. It is based on the quarterly panel data of 29 provinces in China from the period of 2006Q1 to 2012Q3. Tests for panel unit roots, cointegration in heterogeneous panels and panel causality are employed in a bi-variate panel vector error correction model (PVECM), which is estimated by the system generalized moment method (SYS-GMM). The results show evidence of a long-run equilibrium relationship between economic growth and domestic air passenger traffic. Specifically, 1% increase in the air passenger traffic is found to lead to an increase of 0.943% in real gross domestic product (GDP). A long-run and strong bi-directional Granger causal relationship is found between these two series. It is also found that there is a short-run uni-directional Granger causality running from the domestic air passenger traffic to the economic growth.     

突发事件对我国铁路客运量的干预特征研究

基于新冠肺炎疫情等突发事件对人们日常生活出行的影响,结合X-13ARIMASEATS季节调整模型的自动识别最优ARIMA模型和检测突发事件离群值功能,使用脉冲函数和阶梯函数设计基于离群值的突发事件的干预变量,构建铁路客运量的时间序列ARIMAX干预模型,对铁路客运量近年受到的SARS疫情、铁路客票实名制政策和新冠肺炎疫情等突发事件的冲击趋势进行干预比较分析。结果显示,SARS和新冠肺炎疫情对铁路客运量冲击较大,SARS疫情在冲击滞后的第5~6期铁路客运量基本得到恢复,新冠肺炎疫情对铁路客运量冲击一直在持续中,铁路客运实名制政策实施社会性较强,冲击具有波动性和不稳定性特征,持续时间较短;相对季节调整模型的趋势分析优势,干预模型拟合预测精度显著高于季节调整模型,预测显示我国铁路客运量在缓慢持续回暖中。     

Modeling monthly flows of global air travel passengers: An open-access data resource

The global flow of air travel passengers varies over time and space, but analyses of these dynamics and their integration into applications in the fields of economics, epidemiology and migration, for example, have been constrained by a lack of data, given that air passenger flow data are often difficult and expensive to obtain. Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). By refining an annual-scale model of Huang et al. (2013), we developed a set of Poisson regression models to predict monthly passenger volumes between directly connected airports during 2010. The models not only performed well in the United States with an overall accuracy of 93%, but also showed a reasonable confidence in estimating air passenger volumes in other regions of the world. Using the model outcomes, this research studied the spatio-temporal dynamics in the world airline network (WAN) that previous analyses were unable to capture. Findings on the monthly variation of WAN offer new knowledge for dynamic planning and strategy design to address global issues, such as disease pandemics and climate change.     

Impact analysis of September 11 on air travel demand in the USA

This study uses an intervention model to look at the impact of the September 11, 2001 terrorist attack on air transport passenger demand in the US. The result showed that both domestic and international air traffic was significantly impacted for 1 and 2 months, respectively. The impact pattern was thus abrupt and temporary, instead of gradual and permanent. The approach also provides better forecasts than the seasonal ARIMA benchmark.     

Air accessibility and growth – The economic effects of a capacity expansion at Vienna International Airport

We use an econometric endogenous growth model to estimate the impact of air accessibility on GDP and investment growth. This is done in a dynamic panel system estimation framework for the EU-15 between 1993 and 2006. The results are then used to predict the economic effects of an increase in capacity at the Vienna International Airport using actual forecasts of the required information set. We find a GDP elasticity of air accessibility of 0.014 and an investment elasticity of 0.05 for our sample. Given the official passenger forecast this would lead to additional GDP growth in Austria of accumulated 0.81% based on the values of the restricted scenario (no third runway). In a more conservative forecast scenario of 3% annual passenger growth, a third runway is projected to increase GDP by 0.2% by 2040.     

Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport

Air traffic flows show large seasonal variability, but arrivals and departures may also be significantly influenced by specific events which generate peaks, which generate peaks rising above baseline traffic. While seasonal variations of air flows are well studied in literature, the daily variations and their causes are seldom analysed and quantified. The paper aims at filling this gap by exploring and quantifying the effect of holidays and events (conferences, trade fairs, sport events) in terms of passenger daily fluctuations.We identified the elements affecting these variations and searched for correlations with daily demand fluctuations using an OLS econometric model applied to Milan Malpensa airport. The model allows one to reproduce the observed daily traffic, identifying the baseline component of traffic (depending on the calendar) and the additional effect ascribable to holidays and occasional events.Results show which types of events generate a visible traffic increase. The effect of some of them can be very significant indeed. The largest international design and fashion shows taking place in Milan generate up to more than 20% extra passenger traffic compared to the normal baseline traffic. In addition, the analysis showed that their effect is not limited to the event days, but impacted on the surrounding days as well. Holidays also influence the patterns of demand, creating additional traffic on certain days and more pronounced peaks, which also differ according to seasons.     

铁路客运量季节指数计算方法研究

为在铁路客运量预测中消除季节性变化的影响,采用平均数季节指数法、移动平均趋势剔除法、最小平方趋势剔除法对2002年—2004年的铁路客运量计算季节指数,通过误差分析,说明最小平方趋势剔除法能更好地反映运量的季节变动状况,是预测的最佳选择方案。     

Assessing the air cargo business models of combination airlines

Notwithstanding the fact that the air cargo business is generally a secondary one to the passenger business for combination airlines, it can have an important role to play in their profitability. However, growing challenges are threatening the market positions of the combination airlines. Improving their market positioning depends, amongst other factors, on appropriate business models. Yet, the literature on the air cargo business models of combination airlines is scarce. This paper aims to contribute to closing this gap.The research presented herein aimed to identify the representative business models of the combination airlines' cargo strategies. Three strategies have been considered. The research method included a series of structured interviews with key informants from combination airlines, namely: TAP Cargo, Brussels Airlines Cargo, SATA Cargo, Turkish Cargo, SWISS WorldCargo, Finnair Cargo, AF-KLM Cargo, Emirates SkyCargo, Lufthansa Cargo and IAG Cargo.The ten air cargo business models and the representative business models of each strategy are described. The results suggest an overlap between the business models of different strategies. In addition, the results show that an evolution in strategy does not necessarily require a redesign of the business model, but tailored changes in specific components.     

基于季节调整模型的我国铁路客运量波动分析

在研究TRAMO/SEATS季节调整模型计算方法的基础上,基于我国铁路2002年1月—2010年2月的客运量月度数据,应用Demetra软件,通过季节调整模型参数设置、模型的估计和检验,得到2010年3月—2012年2月的铁路客运量预测值,并对预测结果进行趋势性和季节性分析。研究结果表明,Tramo/Seats季节调整模型的预测精度较高。     

神经网络与四阶段预测法在铁路运量预测中的组合应用研究

利用神经网络与四阶段预测法组合构造出新的交通量预测模型,以胶济铁路提速改造为例,就构造的客运量预测模型进行了应用研究。其中以平均增长率法计算客流量的交通分布;以重力模型法计算诱发客流;依据运输阻力构建的分担率模型计算转移客流;在计算诱发客流时考虑了时间价值。     

诱发客流影响下高速铁路及航空运输客票动态定价研究

在阐述博弈论理论基础上,结合票价变动所产生的旅客流量的变化,选择斯坦科尔伯格模型对高速铁路与航空定价过程进行描述,借助双层规划建模的思想,建立了诱发客流影响下高速铁路与航空运输多层规划动态定价模型,并采用基于灵敏度分析的启发式算法通过实际算例对所构建模型进行计算。结合高速铁路与航空动态定价过程的分析,得出诱发客流影响下高速铁路与航空运输趋于均衡的客票定价区间。最后对高速铁路与航空竞争定价的博弈过程中双方客流量及利润的变化进行分析。