Flow Forecasting Research Articles

The Relationship between Built Environment Characteristics and Metro Stations Pedestrian Catchment Areas

The study of pedestrian catchment areas for metro stations serves as the foundation for addressing issues such as‐passenger flow forecasting, land use planning around stations, and transportation link facility planning. This study aims to investigate how various factors related to pedestrians, the environment, and the characteristics of metro stations affect the areas where pedestrians are attracted to. The goal is to gain insights into design strategies and policy interventions that can enhance the appeal of metro stations. The present study considers the metro stations in the Chengdu area as the primary research objects. A framework for examining the connection between the built environment and the pedestrian catchment area of a subway station through the use of a hybrid gradient decision tree approach has been used. The study examines the pedestrian catchment areas of these stations and confirms the presence of a nonlinear relationship between pedestrian catchment areas and variables at these stations. There exists a negative correlation, within specific thresholds, between the number of companies, bus stops, and road network density with the catchment area of passengers. Concurrently, there is a positive correlation between the distances to the city center and the catchment area. Furthermore, this framework, which accounts for spatial heterogeneity, showed significant goodness‐of‐fit and predictive capability, aspects that were overlooked in earlier research. As a result, these research findings can serve as a key foundation for advancing the theoretical framework and statistical proof to facilitate land planning and development near metro stations, predicting passenger flow, and designing essential connecting amenities in the vicinity of such facilities.

Thinking outside the container: A sparse partial least squares approach to forecasting trade flows

Global container ship movements may reliably predict trade flows. First, this paper provides the methodology to construct maritime shipping time series from a dataset comprising millions of container vessel positions annually. Second, to forecast monthly goods trade using these time series, this study outlines the use of the least absolute shrinkage and selection operator (LASSO) in combination with a partial least squares process (PLS). An expanding window, out-of-sample exercise demonstrates that constructed forecasts outperform benchmark models for the vast majority of 76 countries and regions. The performance holds true for unilateral and bilateral trade flows, for trade of developed and developing countries, for real and nominal trade, as well as for time periods of economic crisis such as the COVID-19 pandemic. The resulting forecasts of trade flows precede official statistics by several months and may facilitate quantification of supply chain disruptions and trade wars.

ВПЛИВ МАКРОЕКОНОМІЧНИХ РИЗИКІВ НА ПРОЦЕДУРУ КРИТЕРІАЛЬНОГО ФОРМУВАННЯ ФІНАНСОВОЇ СТІЙКОСТІ ПІДПРИЄМСТВА

The authors determine the influence of macroeconomic risks on the procedure of criterion formation of the financial stability of the enterprise. The article examines macroeconomic risks that have a significant impact on the financial stability of the enterprise. To ensure it, it is necessary to constantly monitor the macroeconomic situation and adapt the company's financial strategies to the conjunctural changes in the market, as well as take into account the factors of influence of macroeconomic indicators on the financial stability of enterprises. It was established that the main detailed elements of the process of criterial formation of the financial stability of enterprises include scenario analysis, analysis of the macroeconomic environment, diversification of assets, financial reserves, risk management strategy. Ways to overcome the problems of the influence of macroeconomic risks on the procedure of criterion formation of the financial stability of the enterprise in the modern market economic environment are indicated. Inflation, which contributes to the increase in the prices of goods and services, reduces the purchasing power of the enterprise and increases its costs. A sharp fluctuation in exchange rates can significantly change the profitability of export-import operations, which negatively affects financial stability. Since changes in exchange rates are unpredictable, it complicates the forecasting and planning of financial flows. An increase in interest rates leads to an increase in the cost of loan capital, which reduces the profitability of the enterprise and its opportunities for development. Economic fluctuations in the market environment can lead to the emergence of economic crises, recessions or a decrease in the general level of economic growth can lead to a decrease in demand for the company's products. To ensure optimal provision of financial stability, it is necessary to constantly monitor the macroeconomic situation and adapt the company's financial strategies to market changes. Therefore, in the process of forced adaptation to new conditions in order to maintain financial stability, enterprises are forced to constantly adapt to changes in the macroeconomic environment, which may require cost reduction, restructuring of liabilities, liquidation of debts or changes in production processes.

Design of toll-plaza: A case study of Bengaluru-Chennai highway

This study aims to calculate the number of bays needed in the design year 2037 for a toll plaza on the Bengaluru – Chennai Highway (National Highway No. 48). Data on the classified traffic volume count was gathered from the National Highway Authority of India (NHAI) office and relates to the “Nemili toll plaza” on the Bengaluru – Chennai Highway (NH-48). Average daily traffic is calculated using the tollable traffic data that has been gathered as a base. A suitable seasonal correction factor was assumed in order to estimate the annual average daily traffic. Using growth elasticity values derived from the Annual Average Growth Rates (AAGR) of Population, Per-Capita Income, and Net State Domestic Product of the influencing states collected from credible sources and state influencing factors, traffic in the horizon year (2037) was estimated. The necessary number of Electronic Toll Collection (FASTag) bays at the Nemili toll plaza was determined in accordance with Indian Road Congress standards based on the traffic flow forecast for the upcoming year.

Optimization of Short-Term Passenger Flow Forecast Based on Improved Sparrow Search Algorithm in LSTM Network

Optimization of Short-Term Passenger Flow Forecast Based on Improved Sparrow Search Algorithm in LSTM Network

TMFO-AGGRU: A Graph Convolutional Gated Recurrent Network for Metro Passenger Flow Forecasting

Accurately exploiting the spatial correlation of passenger flow related to the metro network is important for improving metro passenger flow prediction. In this paper, a novel metro passenger flow forecasting model termed TMFO-AGGRU is proposed based on parameter estimation optimization for a graph convolutional gated recurrent neural network. First, we model a graph convolutional gated recurrent neural network, which is capable of real-time spatial and temporal information training on the passenger flow at a target station. Second, we design graph convolution operations to replace the linear operations of gated recurrent neural networks. To fully utilize the weighted distribution of output data based on different traffic characteristics, we convert the adjacent static matrices into dynamic characteristic matrices using feature learning and construct the attention mechanism module. Last, an adaptive moth-flame optimization method based on T-variance is proposed in this paper to achieve dynamic optimization of structural parameters and to avoid affecting the accuracy of prediction due to improper parameter selection. Experimental results show that the proposed TMFO-AGGRU effectively improves the prediction accuracy and convergence speed, which outperforms traditional metro passenger flow prediction methods.

ИСПОЛЬЗОВАНИЕ МЕТОДОВ МЯГКИХ ВЫЧИСЛЕНИЙ ДЛЯ ПРОГНОЗИРОВАНИЯ ДЕНЕЖНЫХ ПОТОКОВ

This article is devoted to the study and application of soft computing methods in the context of cash flow forecasting. Soft computing is an approach that allows for the uncertainty, fuzziness, and ambiguity in data that often characterizes financial forecasts. The authors consider the stages of forecasting, including calculations of cash inflows, determination of possible outflows of funds, and sensitivity analysis to changes in assumptions. The application of soft computing in this context can improve the accuracy of forecasts, taking into account various factors and uncertainties, which is critical in a rapidly changing economic environment.

Unleashing the Potential of Boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems.

ВКЛАД НГМС РК В СЕЛЕВУЮ БЕЗОПАСНОСТЬ СТРАНЫ

In this paper, the scientific activities of the National Hydrometeorological Service of Kazakhstan to study the origin, development and degradation of debris flows, carried out with the aim of reducing the damage caused by debris flows, are presented over a long period. The features of precipitation, emptying of surface and intraglacial reservoirs, mineralogical and granulometric compositions of debris rocks, geomorphological conditions of debris flow formation, recurrence and characteristics of debris flows were studied. Methods for computation of the viscosity and plasticity of debris-flow mass, used in mathematical modeling of debris flow formation have been developed. A method for ultra-short-term forecasting of catastrophic debris flows caused by rainfall, based on data on preliminary soil moisture, the position of the zero isotherm and seasonal snow line, and current actual meteorological information has been developed. The feasibility of this method is tens of times greater than the feasibility of the method for short-term forecasting of debris flows caused by rainfall, which was used previously. The computation of the characteristics of debris flows is based on the discovery of an ambiguous dependence between the density of debris-flow mass and the slope of way of debris flow. A debris-flow hazard map for the territory of the Republic of Kazakhstan has been developed. A concept for protection against debris flows of Almaty city under climate changing has been proposed. A geological and geomorphological method has been developed for determining the genesis of fan, which allows optimizing the work on assessing the risk of urbanization of territories located in the mountain and foothill zones.

СВЕРХКРАТКОСРОЧНЫЙ ПРОГНОЗ ДОЖДЕВЫХ СЕЛЕЙ И ФОРМИРОВАНИЕ СЕЛЕЙ В БАССЕЙНАХ РЕК КИШИ И УЛКЕН АЛМАТЫ 21 ИЮЛЯ 2023 ГОДА

The conditions for formation of rain-induced debris flows in the Kishi and Ulken Almaty river basins on July 21 2023 given in the paper. These debris flows was predicted using the ultra-short-term method for forecasting rain-induced debris flows, which developed by the RSE “Kazhydromet”. The method is basing on actual meteorological information that is as close as possible to the predicted phenomenon. The accuracy of the ultra-short-term forecast significantly exceeds that of the short-term forecast of rain-induced debris flows. The need to develop a method for ultra-short-term forecasting of rain-induced debris flows is associated with great moral and material damage caused by unfulfilled forecasts. The importance of forecasting rain-induced debris flows was evidenced by debris flows 1950 and 2006, which formed in the Ulken Almaty river basin.

Research on stage–discharge relationship model based on random forest algorithm

Hydrological simulations and predictions are vital aspects of hydrological change research. Accurate predictions of hydrological factors such as stage and discharge are essential for water resources planning, reservoir dispatching and operation, shipping management and flood control. River discharge forecasting during flood seasons is an important issue in water resources planning and management. To improve the calibration accuracy and stability of the stage–discharge relationship model, the feasibility of integrated algorithms for studying the stage–discharge relationship was explored. A random forest (RF) algorithm based on a neural network (NN) was developed using a framework of integrated algorithms. First, the Levenberg–Marquardt (LM) algorithm was used to optimise the weight updating process of a back-propagation (BP) NN and improve the convergence speed. Then, the LM-BP algorithm was used as a decision tree to build an RF algorithm. The model was tested using hydrological data from Hongqi station on the Dadu River in China in the flood season. Results for the classical model, BP NN model, LM-BP NN model and optimised algorithm model were evaluated based on the mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The performance indicators showed that the optimised algorithm model (MAE = 3.13 m3/s, MSE = 19.28 m3/s and MAPE = 1.8%) was superior to the other models and showed high accuracy and good stability in flood-season flow forecasting.

GSA-KELM-KF: A Hybrid Model for Short-Term Traffic Flow Forecasting

Short-term traffic flow forecasting, an essential enabler for intelligent transportation systems, is a fundamental and challenging task for dramatically changing traffic flow over time. In this paper, we present a gravitational search optimized kernel extreme learning machine, named GSA-KELM, to avoid manually traversing all possible parameters to improve the potential performance. Furthermore, with the interference of heavy-tailed impulse noise, the performance of KELM may be seriously deteriorated. Based on the Kalman filter that cleverly combines observed data and estimated data to perform the closed-loop management of errors and limit the errors within a certain range, we propose a combined model, termed GSA-KELM-KF. The experimental results of two real-world datasets demonstrate that GSA-KELM-KF outperforms the state-of-the-art parametric and non-parametric models.

Passenger Flow Prediction and Risk Early Warning for Metro Cross-section based on Transportation Big Data

With the increasing pace of urbanization and the diversification of urban transportation modes, the demand for urban transportation in people's life is also increasing. For the purpose of relieving urban traffic, cities have made great efforts to develop public transport, especially metro transport. This paper will use the subway swipe card data of Changsha city from March 1-15, 2023, and optimize the LSTM model based on particle swarm algorithm, to predict the metro cross-sectional passenger volume. Based on the data and methods, this paper investigates the short-time cross-sectional passenger flow forecasting of the metro using the variables of initial passenger flow, inbound passenger volume and outbound passenger volume as references. It is found that the combined short-time passenger flow prediction model (PSO-LSTM) has good prediction effect. Based on the standard LSTM model, the PSO algorithm optimizes the learning rate, the number of iterations and the number of hidden neurons in the LSTM parameters to construct the PSO-LSTM model. The video recognition technology more accurately reflects the traffic congestion and makes early warning. The short-time cross-sectional passenger flow prediction of subway will help to improve people's living standard and save transportation time cost, which is of practical significance to urban rail operating companies.

Analysis of Urban Rail Transit Short-term Passenger Flow Forecast and Its Optimization

With the growth of urban population and the increase of car ownership, the number of cars continues to increase, leading to traffic congestion becoming a major problem in many cities around the world. Traffic congestion not only wastes time, but also increases air pollution and energy consumption. To address these challenges, governments and transportation authorities around the world have begun to adopt advanced technical models and big data based approaches. By using the power of intelligent data analysis, this paper enables people to understand rail transit more deeply, establish an ARIMA model for short-term prediction, optimize traffic flow, and improve the overall performance of the system. Through the final experimental results, it is found that the trend of rail transit passenger volume in the next few days is increasing, but its growth trend is not as high as that of the previous few days. The ARIMA model infers that the peak period in the next few days may not be as high as that of the previous few days.

Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model

Urban rail transit is very important in modern urban transportation, which meets the needs of People's Daily travel. This paper aims to introduce the ARIMA model into the short-term passenger flow forecast of urban rail transit, analyze its principle and application method, and build ARIMA model combined with AFC data to forecast short-term passenger flow of urban rail transit. Through the research and improvement of ARIMA model, it is anticipated that it would give urban rail transport operators more precise and trustworthy short-term passenger flow forecasting capabilities, so as to realize more intelligent and efficient operation of urban rail transit system. In this study, the ARIMA model was established, and rigorous scientific methods such as residual test, autocorrelation function (ACF) and partial autocorrelation function (PACF) test, and comparison of predicted value and real value were adopted to conduct tests step by step according to the experimental process. Finally, it was confirmed that the excellent prediction accuracy of the ARIMA prediction model used in this paper, can provide accurate prediction results, and can be used in practical scenarios.

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

A Hybrid Deep Learning Method for Short-Term Traffic Flow Forecasting: GSA-LSTM

Short-time subway passenger flow forecast based on the ARIMA-LSTM

In recent years, with the rapid development of Chinas economy and the continuous increase in population, the urbanization process has intensified. This travel issue caused by peak traffic congestion affects a significant portion of urban populations. In order to conduct a thorough analysis, this article primarily focuses on the urban rail transit industrys subway system. In this study, three models - LSTM, ARIMA and ARIMA-LSTM were employed to analyze and predict short-term inbound flow data for the Hangzhou subway in January 2019. While the LSTM model used for forecasting, it was found to be ineffective in predicting data with extreme peaks. The ARIMA model is subsequently employed for data prediction, revealing its inadequacy in accurately forecasting unstable and non-patterned data. To overcome this limitation, a combined ARIMA-LSTM model is proposed to mitigate the shortcomings of individual models and achieve superior performance. The implementation of the ARIMA-LSTM model effectively mitigates subway congestion issues, thereby facilitating informed decision-making by subway operators. Moreover, it has a certain degree of robustness, even if the other data are not regular enough. The model can be applied in the real subway passenger flow prediction, which is conducive to the choice of people traveling to work and the management decisions of subway operators.

Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment

Abstract The existing scenic spot passenger flow prediction models have poor prediction accuracy and inadequate feature extraction ability. To address these issues, a multi-attentional convolutional bidirectional long short-term memory (MACBL)-based method for predicting tourist flow in tourist scenic locations in a location-based services big data environment is proposed in this study. First, a convolutional neural network is employed to identify local features and reduce the dimension of the input data. Then, a bidirectional long short-term memory network is utilized to extract time-series information. Second, the multi-head attention mechanism is employed to parallelize the input data and assign weights to the feature data, which deepens the extraction of important feature information. Next, the dropout layer is used to avoid the overfitting of the model. Finally, three layers of the above network are stacked to form a deep conformity network and output the passenger flow prediction sequence. In contrast to the state-of-the-art models, the MACBL model has enhanced the root mean square error index by at least 2.049, 2.926, and 1.338 for prediction steps of 24, 32, and 60 h, respectively. Moreover, it has also enhanced the mean absolute error index by at least 1.352, 1.489, and 0.938, and the mean absolute percentage error index by at least 0.0447, 0.0345, and 0.0379% for the same prediction steps. The experimental results indicate that the MACBL is better than the existing models in evaluating indexes of different granularities, and it is effective in enhancing the forecasting precision of tourist attractions.

Accurate and Intelligent Early Warning Method of Debris Flow Formation Based on IGWO-LSTM Algorithm

To improve the accuracy of debris flow forecasts and serve as disaster prevention and mitigation, an accurate and intelligent early warning method of debris flow initiation based on the IGWO-LSTM algorithm is proposed. First, the entropy method is employed to screen the early warning indicators. Then, the improved grey wolf algorithm (IGWO) is obtained by optimizing the grey wolf algorithm by combining elite reverse learning and adaptive convergence factors. Finally, the IGWO-LSTM algorithm is obtained by using IGWO to improve the total connection layer weight and bias parameters of LSTM, which takes the screened early warning indicators as input and outputs the early warning results of the debris flow formation risk level. In comparison with the methods introduced in earlier studies, the results demonstrate that the proposed method achieves superior outcomes in terms of assessing a single warning of multiple debris flow gullies, a multi-year warning of a single debris flow gully, and a multi-year warning of multiple debris flow gullies. The mean absolute error and root mean square error of the early warning results of the ANN model and PEEM method show low values, while the early warning hit rate shows high values, surpassing 90%. Also, the other two methods developed in the previous studies show low values of the early warning coverage rate, reaching 90% at most. Moreover, the triggered traffic model and MLPG method show high values in the early warning coverage rate, exceeding 90%, and low values in the early warning hit rate of less than 90%, and the average absolute error and root mean square error are high. On the other hand, the results of the proposed method show that the overall early warning hit rate is higher than 95%, the coverage rate is close to 100%, and the error is less than 1.5. Thus, the comprehensive analysis results show that the proposed method has better performance and higher reliability than other studied methods.

Does firm environmental performance mitigate the market reaction to COVID-19 uncertainty?

This paper investigates how firms’ environmental performance affected the market reaction caused by the uncertainty of the COVID-19 pandemic. Our analysis focuses on a sample of 3854 non-financial listed companies from 21 advanced economies. We find that high environmentally sustainable practices (ESP) are associated with lower returns during the COVID-19 crisis, especially when the pandemic began to spread globally. Also, our results show that ESP is not significantly related to volatility in the whole COVID-19 period. We find a positive relationship between ESP and volatility only in the early phases of the pandemic. The stronger contraction in earnings and cash flow forecasts for high ESP firms explains the negative effect of ESP on market returns. The cost structure of high ESP firms makes them more exposed to the unexpected global demand shock. Our findings suggest that firm environmental performance did not immunise against market turmoil.