Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Taxi origin and destination demand prediction based on deep learning:a review

Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

Demand prediction and purchase optimization decision model for alloys in steel making

In this study,related models of alloy purchasing decision system in the Baoshan base of Baosteel are discussed.First,the corresponding relationship between steel grades and alloy consumption is established through metallurgical-mechanism modeling and statistical analysis.Then,the alloy-demand prediction model based on alloy unit consumption and time series analysis is developed by combining sales plans and historical data.Finally,the alloy purchasing and inventory optimization model is developed to minimize the total cost of purchase and storage by combining inventory optimization theories.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Ride-hailing origin-destination demand prediction with spatiotemporal information fusion

Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction.

Urban construction land demand prediction and spatial pattern simulation under carbon peak and neutrality goals:A case study of Guangzhou,China

Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for territorial spatial planning.This study analyzed quantitative relationships between carbon emissions and urban construction land,and then modified the construction land demand prediction model.Thereafter,an integrated model for urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals was developed,where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types.Using Guangzhou as a case study,the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030–2060 under baseline development and“dual carbon”goals scenarios.The simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030,with the spatial pattern not showing an intensive development trend.Guangzhou’s urban construction land expansion slowed during 2030–2060,with an average annual growth rate of 0.2%,and a centralized spatial pattern trend.Under the“dual carbon”goal scenario,Guangzhou’s urban construction land evolved into a polycentric development pattern in 2030.Compared with the baseline development scenario,urban construction land expansion in Guangzhou during 2030–2060 is slower,with an average annual growth rate of only 0.1%,and the polycentric development pattern of urban construction land was more prominent.Furthermore,land maintenance and growth,that is,a carbon sink,is more obvious under the“dual carbon”goals scenario,with the forest land area nearly 10.6%higher than that under the baseline development scenario.The study of urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals provides a scientific decision-making support tool for territorial spatial planning,aiding in quantifying territorial spatial planning.

Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Understanding the demand predictability of bike share systems:A station-level analysis

Predicting demand for bike share systems(BSSs)is critical for both the management of an existing BSS and the planning for a new BSS.While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors,there are few studies examining the inherent randomness of stations'observed demands and to what degree the demands at individual stations are predictable.Using Divvy bike-share one-year data from Chicago,USA,we measured demand entropy and quantified the station-level predictability.Additionally,to verify that these predictability measures could represent the performance of prediction models,we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability.Furthermore,we explored how city-and system-specific temporallyconstant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable.Our results show that entropy and predictability of demands across stations are polarized as some stations exhibit high uncertainty(a low predictability of 0.65)and others have almost no check-out demand uncertainty(a high predictability of around 1.0).We also validated that the entropy and predictability are a priori model-free indicators for prediction error,given a sequence of bike usage demands.Lastly,we identified that key factors contributing to station-level entropy and predictability include per capita income,spatial eccentricity,and the number of parking lots near the station.Findings from this study provide more fundamental understanding of BSS demand prediction,which can help decision makers and system operators anticipate diverse stationlevel prediction errors from their prediction models both for existing stations and for new ones.

Robust Charging Demand Prediction and Charging Network Planning for Heterogeneous Behavior of Electric Vehicles

This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.

Prediction of the Logistics Demand Based on an Innovative Mixed Model: an Empirical Case from Nanping City,China

The research intends to make scientific prediction of the logistics demand of Nanping City based on mathematical model calculation so as to provide reasonable strategic guidance for the sustainable and healthy development of urban logistics industry.It constructs a comprehensive index system composed of freight volume and other eight relevant economic indices to form the foundation for the model construction.Combining forecasting models of principal component regression and GM(1,1)together,it makes mathematical calculation to predict the logistics demand of Nanping City from the years 2018 to 2022.The research makes systematical analyses of the indices influencing the precise prediction of logistics demand from a new perspective,which offers an innovative and practical option for urban logistics prediction.In line with the prediction,it offers some suggestions for the improvement of demand prediction and some strategies for the better development of the logistics industry in Nanping City.

Modeling and scenario prediction of a natural gas demand system based on a system dynamics method

Based on the study of the relationship between structure and feedback of China’s natural gas demand system, this paper establishes a system dynamics model. In order to simulate the total demand and consumption structure of natural gas in China, we set up seven scenarios by changing some of the parameters of the model. The results showed that the total demand of natural gas would increase steadily year by year and reach in the range from 3600 to 4500 billion cubic meters in 2035. Furthermore, in terms of consumption structure, urban gas consumption would still be the largest term, followed by the gas consumption as industrial fuel, gas power generation and natural gas chemical industry. In addition, compared with the population growth, economic development still plays a dominant role in the natural gas demand growth, the impact of urbanization on urban gas consumption is significant, and the promotion of natural gas utilization technology can effectively reduce the total consumption of natural gas.

Prediction of Commuter Vehicle Demand Torque Based on Historical Speed Information

The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed,which uses machine learning to predict and analyze vehicle demand torque.Firstly,the big data of vehicle driving is collected,and the driving data is cleaned and features extracted based on road information.Then,the vehicle longitudinal driving dynamics model is established.Next,the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle,and the driving torque of the vehicle is obtained.Finally,the travel is divided into several accelerationcruise-deceleration road pairs for analysis,and the vehicle demand torque is predicted by BP neural network and Gaussian process regression.

Prediction on the Farmland Demand of Yunnan Province in 2020 Based on Food Security

According to the cultivated area and grain yield during 1996-2008 and adopting the prediction method of farmland demand based on food security,five indexes,including the cultivated area,grain sown area,yearly food yield per unit area,total population and per capita grain yield,are selected to analyze and predict the farmland demand in Yunnan Province in 2020.As the prediction results of each index show,the total population of Yunnan Province in 2020 will reach 51 464 000,significantly higher than the upper bound(50 million);the per capita food demand of Yunnan Province in 2020 will be 400 kg below the bottom line of the well-off type;food self-sufficient ratio will be respectively given the value of 100%,95% and 90% in three schemes;the prediction will be conducted with the yearly food yield per unit area at an average annual growth rate of 2.5% and 3.0% in two schemes;the rate of grain sowing in 2010 is determined to be 66%.As the prediction results of farmland demand show,there are totally 6 schemes about farmland demand in Yunnan Province obtained through analysis,among them,scheme Ⅰ is difficult to achieve,the prediction results of scheme Ⅳ,Ⅴ and Ⅵ are relatively low,which do not conform to the state policies and regulations to protect farmland and are also not conductive for ensuring the food security;scheme Ⅱ and Ⅲ are close to each other,but scheme Ⅲ obtains better prediction results and determines the farmland demand of Yunnan Province in 2020 based on food security to be 5.9 million so as to ensure the provincial food security and realize the "red line" of basic provincial food self-sufficiency.

Prediction on Cold Chain Logistics Demand of Urban Residents in Jiangsu Province during the Twelfth Five-Year Plan Period——Based on Estimates of GM(1,1) Model

This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,dairy,and medicine.Through selecting the consumption data of urban residents on transported products via cold chain in Jiangsu Province from 2005 to 2000 as sample,this paper establishes grey prediction model GM(1,1) of cold chain logistics demand and uses DPS7.05 software for test,to predict the cold chain logistics demand of urban residents in Jiangsu Province during the Twelfth Five-Year Plan period.The results show that in the period 2010-2015,the cold chain logistics demand of urban residents in Jiangsu Province is 1 151.589 1,1 185.136 6,1 219.661 3,1 255.191 8,1 291.757 3,1 329.388 1 t respectively;in the period 2005-2010,the cold chain logistics demand of urban residents in Jiangsu Province increases at annual growth rate of 3.9%;in the period 2011-2015,the growth rate declines to some extent,increasing slowly at rate of 2.9%.

Development and Utilization of the World＇s and China＇s Bulk Mineral Resources and their Supply and Demand Situation in the Next Twenty Years

Bulk mineral resources of iron ores, copper ores, bauxite, lead ores, zinc ores and potassium salt play a pivotal role on the world＇s and China＇s economic development. This study analyzed and predicted their resources base and potential, development and utilization and their world＇s and China＇s supply and demand situation in the future 20 years. The supply and demand of these six bulk mineral products are generally balanced, with a slight surplus, which will guarantee the stability of the international mineral commodity market supply. The six mineral resources （especially iron ores and copper ores） are abundant and have a great potential, and their development and utilization scale will gradually increase. Till the end of 2014, the reserve- production ratio of iron, copper, bauxite, lead, zinc ores and potassium salt was 95 years, 42 years, 100 years, 17 years, 37 years and 170 years, respectively. Except lead ores, the other five types all have reserve-production ratio exceeding 20 years, indicative of a high resources guarantee degree. If the utilization of recycled metals is counted in, the supply of the world＇s six mineral products will exceed the demand in the future twenty years. In 2015-2035, the supply of iron ores, refined copper, primary aluminum, refined lead, zinc and potassium salt will exceed their demand by 0.4-0.7 billion tons （Gt）, 5.0-6.0 million tons （Mt）, 1.1-8.9 Mt, 1.0-2.0 Mt, 1.2-2.0 Mt and 4.8-5.6 Mt, respectively. It is predicted that there is no problem with the supply side of bulk mineral products such as iron ores, but local or structural shortage may occur because of geopolitics, monopoly control, resources nationalism and trade friction. Affected by China＇s compressed industrialized development model, the demand of iron ores （crude steel）, potassium salt, refined lead, refined copper, bauxite （primary aluminum） and zinc will gradually reach their peak in advance. The demand peak of iron ores （crude steel） will reach around 2015, 2016 for potassium salt, 2020 for refined lead, 2021 for bauxite （primary aluminum）, 2022 for refined copper and 2023 for zinc. China＇s demand for iron ores （crude steel）, bauxite （primary aluminum） and zinc in the future 20 years will decline among the world＇s demand, while that for refined copper, refined lead and potassium salt will slightly increase. The demand for bulk mineral products still remains high. In 2015-2035, China＇s accumulative demand for iron ores （crude steel） will be 20.313 Gt （13.429 Gt）, 0.304 Gt for refined copper, 2.466 Gt （0.616 Gt） of bauxite （primary aluminum）, 0.102 Gt of refined lead, 0.138 Gt of zinc and 0.157 Gt of potassium salt, and they account for the world＇s YOY （YOY） accumulative demand of 35.17%, 51.09%, 48.47%, 46.62%, 43.95% and 21.84%, respectively. This proportion is 49.40%, 102.52%, 87.44%, 105.65%, 93.62% and 106.49% of that in 2014, respectively. From the supply side of China＇s bulk mineral resources, it is forecasted that the accumulative supply of primary （mine） mineral products in 2015-2035 is 4.046 Gt of iron ores, 0.591 Gt of copper, 1.129 Gt of bauxite, 63.661 Mt of （mine） lead, 0.109 Gt of （mine） zinc and 0.128 Gt of potassium salt, which accounts for 8.82%, 13.92%, 26.67%, 47.09%, 33.04% and 15.56% of the world＇s predicted YOY production, respectively. With the rapid increase in the smelting capacity of iron and steel and alumina, the rate of capacity utilization for crude steel, refined copper, alumina, primary aluminum and refined lead in 2014 was 72.13%, 83.63%, 74.45%, 70.76% and 72.22%, respectively. During 2000-2014, the rate of capacity utilization for China＇s crude steel and refined copper showed a generally fluctuating decrease, which leads to an insufficient supply of primary mineral products. It is forecasted that the supply insufficiency of iron ores in 2015-2035 is 17.44 Gt, 0.245 Gt of copper in copper concentrates, 1.337 Gt of bauxite, 38.44 Mt of lead in lead concentrates and 29.19 Mt of zinc in zinc concentrates. China has gradually raised the utilization of recycled metals, which has mitigated the insufficient supply of primary metal products to some extent. It is forecasted that in 2015-2035 the accumulative utilization amount of steel scrap （iron ores） is 3.27 Gt （5.08 Gt）, 70.312 Mt of recycled copper, 0.2 Gt of recycled aluminum, 48 Mt of recycled lead and 7.7 Mt of recycled zinc. The analysis on the supply and demand situation of China＇s bulk mineral resources in 2015-2035 suggests that the supply-demand contradiction for these six types of mineral products will decrease, indicative of a generally declining external dependency. If the use of recycled metal amount is counted in, the external dependency of China＇s iron, copper, bauxite, lead, zinc and potassium salt will be 79%, 65%, 26%, 8%, 16% and 18% in 2014, respectively. It is predicted that this external dependency will decrease to 62%, 64%, 20%, -0.93%, 16% and 14% in 2020, respectively, showing an overall decreasing trend. We propose the following suggestions correspondingly. （1） The demand peak of China＇s crude steel and potassium salt will reach during 2015-2023 in succession. Mining transformation should be planned and deployed in advance to deal with the arrival of this demand peak. （2） The supply-demand contradiction of China＇s bulk mineral resources will mitigate in the future 20 years, and the external dependency will decrease accordingly. It is suggested to adjust the mineral resources management policies according to different minerals and regions, and regulate the exploration and development activities. （3） China should further establish and improve the forced mechanism of resolving the smelting overcapacity of steel, refined copper, primary aluminum, lead and zinc to really achieve the goal of ＂reducing excess production capacity＂. （4） In accordance with the national strategic deployment of ＂One Belt One Road＂, China should encourage the excess capacity of steel, copper, alumina and primary aluminum enterprises to transfer to those countries or areas with abundant resources, high energy matching degree and relatively excellent infrastructure. Based on the national conditions, mining condition and geopolitics of the resources countries, we will gradually build steel, copper, aluminum and lead-zinc smelting bases, and potash processing and production bases, which will promote the excess capacity to transfer to the overseas orderly. （5） It is proposed to strengthen the planning and management of renewable resources recycling and to construct industrial base of renewable metal recycling. （6） China should promote the comprehensive development and utilization of paragenetic and associated mineral species to further improve the comprehensive utilization of bulk mineral resources.

Bayesian inference for ammunition demand based on Gompertz distribution

Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.

Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.

Oil and Gas Supply and Demand in China and Its Development Strategy

The paper makes a comprehensive prediction of China＇s future demand for oil and gas with two methods, i.e. the prediction method based on the demand for individual oil and gas products and the method for predicting the total demand. According to the demand prediction of, and the historical data on, the oil and gas consumption, we conduct an analysis of the oil and gas consumption trends, which can be described as six different development periods. On the other hand, the paper makes a comprehensive analysis of the domestic oil and gas resources and, on this basis, makes a basic prediction of the domestic output of oil and gas. The supply and demand situation is also analyzed. By using the SWOT analysis method, the paper puts forward the development strategies for China＇s oil and gas industry and gives some related strategic measures.

Analysis of China’s Crude Oil Supply and Demand in the Year 2010 and the Year 2020

Along with the speedy development of the economic growth in China, the shortage of oil and gas becomes more and more serious. Based on summarizing some related research results, the prediction of China＇s oil demand and supply in the year 2010 and the year 2020 has been given in the paper. The oil supply and demand situation is discussed on three different levels. Accordingly, suggestions about the oil supply safety and the national economy safety strategies have been given.