Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely Gallat ( G raph prediction with all at tention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.

Recursive Neural Network-Based Market Demand Forecasting Algorithm for Calligraphy Practice Products

In today’s society, calligraphy, which reflects one’s basic writing skills, is becoming more and more important to people. People are influenced by calligraphy in their studies, work, etc. Improving calligraphy writing skills has become one of the key directions for developing one’s abilities at this stage. As an important means of improving writing skills, calligraphy practice products are attracting more and more attention and purchases. In particular, in recent years, as the market economy has developed in a deeper direction, people’s demand for calligraphy practice products has diversified and calligraphy practice product companies have launched a variety of products to meet the public’s calligraphy practice needs in order to adapt to the reality of consumer demand. However, with the development of the Internet culture industry and influenced by objective factors such as school holidays and seasons, the current market demand for calligraphy practice products is rapidly and dynamically changing, making market changes difficult to grasp and leading to poor sales, which directly affects the profits of calligraphy practice product-related companies. The artificial intelligence neural network method realizes the nonlinear relationship between the input and output of sample data through the self-learning ability of each neuron and has a certain nonlinear mapping ability in prediction, which plays a great role in the market demand prediction of many commercial products. Based on this, this paper proposes a recursive neural network-based algorithm to predict the future demand and development trend of calligraphy practice products through extensive and in-depth research, so as to provide positive and beneficial guidance for enterprises’ future production and sales.

Artificial Intelligence as an Emerging Technology in Global Trade

With Industry 4.0 and now 5.0 technologies, the entire globe is embracing these changes. Artificial intelligence-powered systems have immense potential to eliminate international geographical barriers and prove to influence global trade worldwide. The present study highlights how AI increases productivity, economic development, and provides international trade with new horizons. The global value chains, prediction of future trends like changes in consumer demand, risk management, supply chain links are some of the key applications of AI in the sector. AI empowers international trade negotiations to analyze economic trajectories of negotiating partners, adjustments of trade barriers at different rates and scenarios. The chapter will cover the support of AI to access global trade data, its response to diverse challenges, international expansions through digital platforms, support in translations, mechanism of demand prediction, automation of administration with increased efficiency and utility, smart manufacturing, barriers, and influences.

Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

Prediction of Bike Share Demand by Machine Learning

In the fourth industrial revolution period, multinational companies and start-ups have applied a sharing economy concept to their business and have attempted to better serve customer demand by integrating demand prediction results into their business operations. For survival amongst today’s fierce competition, companies need to upgrade their prediction model to better predict customer demand in a more accurate manner. This study explores a new feature for bike share demand prediction models that resulted in an improved RMSLE score. By applying this new feature, the number of daily vehicle accidents reported in the Washington, D.C. area, to the Random Forest, XGBoost, and LightGBM models, the RMSLE score results improved. Many previous studies have primarily focused on feature engineering and regression techniques within given dataset. However, this study is meaningful because it focuses more on finding a new feature from an external data source.