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.

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.

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.

Dynamic Prediction Method for Valuable Spare Parts Demand in Weaponry Equipment Based on Data Perception

Missile is an important weapon system of the army.The spare parts of missile equipment are significant effect on military operations.In order to improve the mission completion rate of missile equipment in wartime,this paper introduces data sensing method to forecast the demand of valuable spare parts of missile equipment dynamically.Firstly,the information related to valuable spare parts of missile equipment was obtained by data sensing,and the sample size was determined by Bernoulli uniform sampling probability.Secondly,according to the data quality of multi-source and multi-modal,the data requirement for dynamic demand prediction of valuable spare parts of missile equipment was obtained.Finally,according to the characteristics of the spare parts,the life of the spare parts was predicted,realizing the dynamic prediction of the demand for valuable spare parts of missile equipment.The results show that the demand of valuable spare parts of missile equipment can be predicted dynamically by using this method,the accuracy is higher than 95%,and the real-time performance is more excellent.

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 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.

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.

Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

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%.

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.

Forestland prediction of China based on forest ecosystem services for the first half of 21st century

A new model was developed to predict forestland demand of China during the years of 2010-2050 in terms of the concept of forest ecosystem services. On the basis of the relationship between forest ecosystem services and classified forest management, we hypothesized that the ecological-forest provides ecological services, whereas commercial-forest supplies wood and timber production, and the influences of the growth of population, social-economic development target, forest management methods and the technology changes on forest resources were also taken into account. The prediction reveals that the demand of total forestland of China will be 244.8, 261.2 and 362.2 million ha by the year 2010, 2020 and 2050, respectively. The results demonstrated that China will be confronted with a shortage of forest resources, especially with lack of ecological-oriented forests, in the future. It is suggested that sustainable management of forest resources must be reinforced and more attention should be drown no enhancing the service function of forest ecosystem.

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.

A Predictive Nighttime Ventilation Algorithm to Reduce Energy Use and Peak Demand in an Office Building

The effect of two nighttime ventilation strategies on cooling and heating energy use is investigated for a prototype office building in several northern America climates, using hourly building energy simulation software （DOE2.1E）. The strategies include： scheduled-driven nighttime ventilation and a predictive method for nighttime ventilation. The maximum possible energy savings and peak demand reduction in each climate is analyzed as a function of ventilation rate, indoor-outdoor temperature difference, and building thermal mass. The results show that nighttime ventilation could save up to 32% cooling energy in an office building, while the total energy and peak demand savings for the fan and cooling is about 13% and 10%, respectively. Consequently, finding the optimal control parameters for the nighttime ventilation strategies is very important. The performance of the two strategies varies in different climates. The predictive nighttime ventilation worked better in weather conditions with fairly smooth transition from heating to cooling season.

中国城镇失能老年人口规模及养老服务需求预测

以全国第六次和第七次人口普查数据为基准数据,结合CLHLS微观数据库,对2050年以前城镇失能老年人口养老服务需求进行了预测。结果显示:随着年龄的增加,中国城镇老年人的失能率不断提高,其中在65岁以上、80岁以上及100岁以上的老年人群体中,其平均失能率分别为28.98%、42.12%和76.04%;未来城镇重度失能老年人口规模将不断扩大,2050年以前其所占比例会超过城镇总失能老年人口的25%,而且男性重度失能人口规模始终低于女性重度失能人口规模;未来城镇重度失能老年人养老服务人员需求数量处于上升状态,预计2050年以前其每年需求的平均规模会超过500万人;随着城镇化的推进,未来城镇失能人口将高于农村失能人口。因此,应尽快建立覆盖城镇居民的长期照护机制;针对不同的城镇失能人群构建差异化的长期护理模式;加快建设针对城镇失能老年人的专业照护人员队伍;完善失能老年人养老服务规制。

考虑风电出力不确定性的多源联合系统双层优化调度

针对含风-火-储的多源联合系统,风电出力具有不确定性的特点,风机在特定时间段内的预测功率与实际功率之间存在误差,当风机实际出力无法满足调度计划中安排的功率时会导致系统经济效益大幅下降。为此,文中提出了考虑风电预测误差和需求侧响应的双层优化策略,上层模型以风电、火电和可平移负荷总运行成本最少为目标,采用改进粒子群算法(Improved Particle Swarm Algorithm, IPSO)制定火电和可平移负荷的最优调度策略,然后通过Gibbs法对风机最大出力预测误差的概率密度函数进行抽样获取一定量的样本,得到各样本上层电源的功率缺额;下层模型以储能和可中断负荷总运行成本最少为目标,采用线性规划方法对冲上层电源功率缺额,进而制定下层模型电源调度策略。在大量抽样样本背景下,通过对比各样本总成本函数值的期望和方差验证了所提双层优化策略的经济性和有效性。

面向动态交通分配的交通需求深度学习预测方法

为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数。应用西安市长安区的卡口车牌数据验证该方法。结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55%~19.80%,均方根误差降低了11.20%~17.99%,决定系数提升了8.62%~12.48%;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36%~13.81%,均方根误差降低了4.23%~10.67%,决定系数提升了3.50%~7.01%,且本文模型运行时间最短。说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度。

鄂尔多斯红庆河采煤矿区生态需水研究

鄂尔多斯红庆河煤矿区处在西部干旱半干旱内陆区域,由于煤炭开采导致生态环境问题较为突出,该区域水资源短缺、供需失衡,严重制约了矿区的生态恢复。在充分的自然概况调查基础上,将红庆河煤矿区生态需水划分为天然生态需水和人工生态需水2种类型。通过植被蒸散法对植被生态需水进行计算,使用马尔科夫链进行定性预测,利用灰色预测模型GM(1,1)进行定量预测。结果表明:以2020年为基准年,红庆河煤矿区总生态需水量为37.52×10^(7)m^(3),天然植被每平方千米乔木、灌木、草地生态需水量分别为1.11×10^(6)、1.06×10^(6)、0.36×10^(6)m^(3);人工生态需水量为1.39×10^(7)m^(3)。进一步对研究区生态需水进行定性、定量预测,规划年(2025年、2030年、2035年)矿区天然生态需水量相比基准年分别上升了0.61%、2.57%和4.57%;人工生态需水量分别上升了3.60%、11.51%和20.86%。研究结果可为该地区生态恢复中植被类型选择、水资源开发利用、合理调配水资源提供科学依据。

2025-2035年中国天然石墨资源需求预测

天然石墨广泛应用于传统工业和战略性新兴产业,是支撑我国高新技术发展的重要原材料。本文论述了我国天然石墨资源现状,并通过分析天然石墨主要消费部门需求,预测了未来耐火材料、铸造、铅笔、密封材料、摩擦材料、润滑吸附材料、锂电池负极材料等产业对天然石墨的需求。研究发现:我国天然石墨资源丰富,未来需求量将快速增长,预计到2025年、2030年、2035年我国天然石墨的需求量将分别达到109.0万t、188.3万t和278.3万t。全球天然石墨供给侧正在重塑,我国在全球天然石墨产业链供应链中的地位正在下降。我国石墨消费重心正从传统产业向战略性新兴产业转移,高端石墨产业发展面临机遇,为天然石墨产业发展带来新的增长点。通过健全天然石墨产业链和加强天然石墨资源保护力度,使我国从石墨资源大国发展成为石墨资源强国,支撑我国未来在新能源、关键装备密封润滑材料、高温材料等高端领域的石墨产品需求发展。