A Basic Study of the Forecast of Air Transportation Networks Using Different Forecasting Methods

This research applies network structuring theories to the aviation domain and predicts aviation network growth, considering a flight connection between airports as a link between nodes. Our link prediction approach is based on network structure information, and to improve prediction accuracy, it is necessary to estimate the mechanism of aviation network growth. This research critically evaluates the prediction accuracy of two methods: the receiver operating characteristic curve method (ROC) and the logistic regression method. We propose a four-step method to evaluate the relative predictive accuracy among different link prediction methods. A case study of US aviation networks indicated that the ROC method provided better prediction accuracy compared with the logistic regression method. This result suggests that tuning of the prediction distribution and the regression model coefficients can further improve the accuracy of the logistic regression method.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Network Aggregation Process in Multilayer Air Transportation Networks

The air transportation network, one of the common multilayer complex systems, is composed of a collection of individual airlines, and each airline corresponds to a different layer. An important question is then how many airlines are really necessary to represent the optimal structure of a multilayer air transportation system. Here we take the Chinese air transportation network （CATN） as an example to explore the nature of multiplex systems through the procedure of network aggregation. Specifically, we propose a series of structural measures to characterize the CATN from the multilayered to the aggregated network level. We show how these measures evolve during the network aggregation process in which layers are gradually merged together and find that there is an evident structural transition that happened in the aggregated network with nine randomly chosen airlines merged, where the network features and construction cost of this network are almost equivalent to those of the present CATN with twenty-two airlines under this condition. These findings could shed some light on network structure optimization and management of the Chinese air transportation system.

Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

Internet of Things(IoT)is a network that connects things in a special union.It embeds a physical entity through an intelligent perception system to obtain information about the component at any time.It connects various objects.IoT has the ability of information transmission,information perception,and information processing.The air quality forecasting has always been an urgent problem,which affects people’s quality of life seriously.So far,many air quality prediction algorithms have been proposed,which can be mainly classified into two categories.One is regression-based prediction,the other is deep learning-based prediction.Regression-based prediction is aimed to make use of the classical regression algorithm and the various supervised meteorological characteristics to regress themeteorological value.Deep learning methods usually use convolutional neural networks(CNN)or recurrent neural networks(RNN)to predict the meteorological value.As an excellent feature extractor,CNN has achieved good performance in many scenes.In the same way,as an efficient network for orderly data processing,RNN has also achieved good results.However,few or none of the above methods can meet the current accuracy requirements on prediction.Moreover,there is no way to pay attention to the trend monitoring of air quality data.For the sake of accurate results,this paper proposes a novel predicted-trend-based loss function(PTB),which is used to replace the loss function in RNN.At the same time,the trend of change and the predicted value are constrained to obtain more accurate prediction results of PM_(2.5).In addition,this paper extends the model scenario to the prediction of the whole existing training data features.All the data on the next day of the model is mixed labels,which effectively realizes the prediction of all features.The experiments show that the loss function proposed in this paper is effective.

Research on Decision Support System (DSS) of Atmospheric Environment Management in Anhui Province Based on Air Quality Forecasting

With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastructure, technological support, monitoring and early monitoring, decision support and information services were established. These platforms have 15 subsystems, including stereoscopic monitoring network, visual business consultation, high-performance computing environment, comprehensive management of atmospheric data, emission inventories of pollu-tion sources, evaluation tools of atmospheric models, monitoring and management of air pollution, forecasting and early warning of air quality, diag-nostic analysis of atmospheric environment, tracking of air pollution sources, emergency management of air pollution, conformity management of air quality, comprehensive display of information, releasing of information to external networks, and releasing of information by mobile networks. The decision support system （DSS） of atmospheric environment management could realize an integration business system of 11 air quality forecast - heavy pollution weather warning - diagnosis of pollution causes （dynamic analysis of pollution sources） -air quality conformity planning （air pollu-tion emergency management） -evaluation of forecasting and warning results （evaluation pf management measures） -air quality forecasting＂ and provide the technical support for the prevention and control of atmosphere pollution in Anhui province.

Laplacian energy maximizationfor multi-layer air transportation networks

To increase airspace capacity, alleviate flight delay,and improve network robustness, an optimization method of multi-layer air transportation networks is put forward based on Laplacian energy maximization. The effectiveness of taking Laplacian energy as a measure of network robustness is validated through numerical experiments. The flight routes addition optimization model is proposed with the principle of maximizing Laplacian energy. Three methods including the depth-first search( DFS) algorithm, greedy algorithm and Monte-Carlo tree search( MCTS) algorithm are applied to solve the proposed problem. The trade-off between system performance and computational efficiency is compared through simulation experiments. Finally, a case study on Chinese airport network( CAN) is conducted using the proposed model. Through encapsulating it into multi-layer infrastructure via k-core decomposition algorithm, Laplacian energy maximization for the sub-networks is discussed which can provide a useful tool for the decision-makers to optimize the robustness of the air transportation network on different scales.

Hub-and-Spoke System in Air Transportation and Its Implications to Regional Economic Development——A Case Study of United States

Considerable changes have taken place in commercial passenger air transport since the enactment of the 1978 Airline Deregulation Act in the US and the deregulation of airline networks that has occurred elsewhere. The commercial and operational freedoms have led most of the larger carriers to develop hub-and-spoke networks, within which certain cities or metropolitan areas emerge as key nodes possessing tremendous advantages over other locations in the air transport system. This paper examines the nature of hub-and-spoke operations in air transportation services, and the benefits that accrue to a city or geographical region that is host to an airline hub. In particular, it looks into linkages between the air service hub and local economic development. Four potential types of impact of airports on the regional economy are defined and discussed. As an example, the assessment of the economic impacts of Cincinnati-Northern Kentucky International Airport (CVG), a major Delta Airlines hub, is introduced.

A Systems-Theoretic Security Model for Large Scale, Complex Systems Applied to the US Air Transportation System

Classical risk-based or game-theoretic security models rely on assumptions from reliability theory and rational expectations economics that are not applicable to security threats. Additionally, these models suffer from serious deficiencies when they are applied to software-intensive, socio-technical systems. A new approach is proposed in this paper that applies principles from control theory to enforce constraints on security threats thereby extending techniques used in system safety engineering. It is applied to identify and mitigate the threats that could emerge in critical infrastructures such as the air transportation system. Insights are provided to assist systems engineers and policy makers in securely transitioning to the Next Generation Air Transportation System (NGATS).

Accurate Multi-Site Daily-Ahead Multi-Step PM_(2.5)Concentrations Forecasting Using Space-Shared CNN-LSTM

Accurate multi-step PM_(2.5)(particulate matter with diameters≤2.5 um)concentration prediction is critical for humankinds’health and air populationmanagement because it could provide strong evidence for decisionmaking.However,it is very challenging due to its randomness and variability.This paper proposed a novel method based on convolutional neural network(CNN)and long-short-term memory(LSTM)with a space-shared mechanism,named space-shared CNN-LSTM(SCNN-LSTM)for multi-site dailyahead multi-step PM_(2.5)forecasting with self-historical series.The proposed SCNN-LSTM contains multi-channel inputs,each channel corresponding to one-site historical PM_(2.5)concentration series.In which,CNN and LSTM are used to extract each site’s rich hidden feature representations in a stack mode.Especially,CNN is to extract the hidden short-time gap PM_(2.5)concentration patterns;LSTM is to mine the hidden features with long-time dependency.Each channel extracted features aremerged as the comprehensive features for future multi-step PM_(2.5)concentration forecasting.Besides,the space-shared mechanism is implemented by multi-loss functions to achieve space information sharing.Therefore,the final features are the fusion of short-time gap,long-time dependency,and space information,which enables forecasting more accurately.To validate the proposed method’s effectiveness,the authors designed,trained,and compared it with various leading methods in terms of RMSE,MAE,MAPE,and R^(2)on four real-word PM_(2.5)data sets in Seoul,South Korea.The massive experiments proved that the proposed method could accurately forecast multi-site multi-step PM_(2.5)concentration only using self-historical PM_(2.5)concentration time series and running once.Specifically,the proposed method obtained averaged RMSE of 8.05,MAE of 5.04,MAPE of 23.96%,and R^(2)of 0.7 for four-site daily ahead 10-hourPM_(2.5)concentration forecasting.

Hourly traffic flow forecasting using a new hybrid modelling method

Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM_(2.5)) Concentration Forecasting

Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment.Particulate Matter(PM_(2.5))is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m.For risk assessment and epidemiological investigations,a better knowledge of the spatiotemporal variation of PM_(2.5) concentration in a constant space-time area is essential.Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression,ignoring a plethora of hidden but crucial manipulating aspects.Many advanced deep learning approaches have been proposed to forecast Particulate Matter(PM_(2.5)).Recurrent neural network(RNN)is one of the popular deep learning architectures which is widely employed in PM_(2.5) concentration forecasting.In this research,we proposed a Two-State Gated Recurrent Unit(TS-GRU)for monitoring and estimating the PM_(2.5) concentration forecasting system.The proposed algorithm is capable of considering both spatial and temporal hidden affecting elements spontaneously.We tested our model using data from daily PM_(2.5) dimensions taken in the contactual southeast area of the United States in 2009.In the studies,three evaluation matrices were utilized to compare the overall performance of each algorithm:Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE).The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

Real-Time Short-Term Forecasting Based on Information Management

Traffic congestions and road accidents continue to increase in industry countries. There are three basic strategies to relieve congestion. The first strategy is to increase the transportation infrastructure. However, this strategy is very expensive and can only be accomplished in the long-term. The second strategy is to limit the traffic demand or make traveling more expensive that will be strongly opposed by travelers. The third strategy is to focus on efficient and intelligent utilization of the existing transportation infrastructures. This strategy is gaining more and more attention because it’s well. Currently, the Intelligent Transportation System (ITS) is the most promising approach to implementing the third strategy. Various forecast schemes have been proposed to manage the traffic data. Many studies showed that the moving average schemes offered meaningful results compared to different forecast schemes. This paper considered the moving average schemes, namely, simple moving average, weighted moving average, and exponential moving average. Furthermore, the performance analysis of the shortterm forecast schemes will be discussed. Moreover, the real-time forecast model will consider the abnormal condition detection.

A demand forecasting model for urban air mobility in Chengdu,China

The successful application of new technologies such as remotely piloted aircraft systems,distributed electric propulsion systems,and automatic control systems on electric vertical take-off and landing(eVTOL)aircraft has prompted Urban Air Mobility(UAM)to be mentioned frequently.UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas,which is thought to share some of the traffic on the ground.One of the prerequisites for UAM to operate on a regular basis is that its demand can support the operating costs,so forecasting UAM demand is necessary.We conduct UAM demand forecasting based on the four-step method,focusing on improving the third-step modal split,and propose a demand forecasting model based on the logit model.The model combines a nested logit(NL)model with a multinomial logit(MNL)model to solve the problem of non-existent UAM sharing rates.We use Chengdu,China as an example,and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method.The results show that UAM is suitable for shared operation during the early stages.With a fully shared operation,the UAM share rate increases by 0.73%for every kilometer increase in distance.Moreover,UAM is more competitive than other modes for delivery distances exceeding 15 km.Finally,using the distributions of the share rate and traffic flow pattern from the simulation,we propose the routes that can be prioritized for UAM operations in Chengdu.

Study of Combination Forecasting in Airline Traffic Turnover

The basic theory and method of the combination forecasting are introduced. Based on the actual data in an airline, the case study was presented. In the case study, two basic forecasting models are set up, which are the time-regression plus seasonal factor model and the logarithm additive Winters model. And two combination models are established with the basic models, which are the optimal combination model and the regressive combination model. The results of the study are guidable to the practice.

A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis(SFA) models and a model average technique. First, considering model uncertainty,a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.

Generalized Weighted Mean Combining Forecasting and Its Application in the Forecasting of Air Materials Consumption

This paper presents the parameter estimation methods of weighting coefficients in generalized weighted mean combining forecasting, and uses this forecasting model to forecast air materials consumption. Finially, the efficiency of generalized weighted mean combining forecasting has been demonstrated by an example.

Transportation robot battery power forecasting based on bidirectional deep-learning method

This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning technique.In the proposed model,the on-board battery power data is measured and transmitted.A WPD(wavelet packet decomposition)algorithm is employed to decompose the original collected non-stationary series into several relatively more stable subseries.For each subseries,a deep learning–based predictor–bidirectional long short-term memory(BiLSTM)–is constructed to forecast the battery power voltage from one step to three steps ahead.Two experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model,which shows the highest forecasting accuracy.The obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged,providing effective support for the safe use of transportation robots.

An air quality forecasting system in Beijing-Application to the study of dust storm events in China in May 2008

An air pollution forecast system,ARIA Regional,was implemented in 2007–2008 at the Beijing Municipality Environmental Monitoring Center,providing daily forecast of main pollutant concentrations.The chemistry-transport model CHIMERE was coupled with the dust emission model MB95 for restituting dust storm events in springtime so as to improve forecast results.Dust storm events were sporadic but could be extremely intense and then control air quality indexes close to the source areas but also far in the Beijing area.A dust episode having occurred at the end of May 2008 was analyzed in this article,and its impact of particulate matter on the Chinese air pollution index （API） was evaluated.Following our estimation,about 23 Tg of dust were emitted from source areas in Mongolia and in the Inner Mongolia of China,transporting towards southeast.This episode of dust storm influenced a large part of North China and East China,and also South Korea.The model result was then evaluated using satellite observations and in situ data.The simulated daily concentrations of total suspended particulate at 6：00 UTC had a similar spatial pattern with respect to OMI satellite aerosol index.Temporal evolution of dust plume was evaluated by comparing dust aerosol optical depth （AOD） calculated from the simulations with AOD derived from MODIS satellite products.Finally,the comparison of reported Chinese API in Beijing with API calculated from the simulation including dust emissions had showed the significant improvement of the model results taking into accountmineral dust correctly.

Review of Maritime Transportation Air Emission Pollution and Policy Analysis

The study of air emission in maritime transportation is new, and the recognition of its importance has been rising in the recent decade. The emissions of CO2, SO2, NO2 and particulate matters from maritime transportation have contributed to climate change and environmental degradation. Scientifically, analysts still have controversies regarding how to calculate the emissions and how to choose the baseline and methodologies. Three methods are generally used, namely the ＇bottom up＇ approach, the ＇top down＇ approach and the STEEM, which produce very different results, leading to various papers with great uncertainties. This, in turn, resuits in great difficulties to policy makers who attempt to regulate the emissions. A recent technique, the STEEM, is intended to combine the former two methods to reduce their drawbacks. However, the regulations based on its results may increase the costs of shipping companies and cause the competitiveness of the port states and coastal states. Quite a few papers have focused on this area and provided another fresh perspective for the air emission to be incorporated in maritime transportation regulations; these facts deserve more attention. This paper is to review the literature on the debates over air emission calculation, with particular attention given to the STEEM and the refined estimation methods. It also reviews related literature on the economic analysis of maritime transportation emission regulations, and provides an insight into such analysis. At the end of this paper, based on a review and analysis of previous literature, we conclude with the policy indications in the future and work that should be done. As the related regulations in maritime transportation emissions are still at their beginning stage in China, this paper provides specific suggestions on how China should regulate emissions in the maritime transportation sector.