Flow Forecasting Research Articles

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.

СИСТЕМЫ ПОДГОТОВКИ И ВЫПУСКА ГИДРОЛОГИЧЕСКИХ ПРОГНОЗОВ ГИДРОМЕТЦЕНТРА РОССИИ

Nowadays operational forecasts of river flow are implemented in the form of automated systems for issuing forecasts, as well as delivering forecast products to users. Only within the framework of these systems, using modern software, the maximum efficiency is achieved from the use of hydrological models by automating all processes, combining them with operational database management systems, as well as with modern GIS and web tools for generating forecast products and delivering them to users. Over the past decade, the Hydrometeorological Research Center of the Russian Federation (Hydrometcenter of Russia) has been actively developed automated systems for issuing hydrological forecasts and communicating them to users. The systems are based on modern hydrological models, all available hydrometeorological information, including numerical weather forecasts, as well as modern software tools, including GIS and web technologies. Such systems have been implemented for large river basins, including the basin of the Kuban and the rivers of the Black Sea coast of Russia, the Amur and Volga basins. In recent years, the system has been developed for the entire territory of Russia. These systems are actively used by the Roshydromet operational forecasting divisions, as well as by specialists from the EMERCOM of Russia.

Traffic Flow Forecast using Time Series Analysis based on Machine Learning Algorithm

Time series forecasting plays a critical role in predicting future trends and making informed decisions across various domains. In this research paper, we present a multi-model approach to improve the accuracy and robustness of time series forecasting. Our methodology involves integrating two powerful models, Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA), to capture complex temporal patterns and seasonality in the data. We leverage real-world traffic data from different junctions and employ a comprehensive pipeline for data preprocessing, including data selection, resampling, and sequence generation. Our study focuses on four junctions, and for each junction, we individually train LSTM models, followed by SARIMA models for comparison. The LSTM models are designed to capture long-term dependencies in the time series data, while the SARIMA models are tailored to handle seasonality and autocorrelation. We implement a custom sequence generation process and split the dataset into training and testing sets for model evaluation. The LSTM models are trained using Pytorch, optimizing them for accurate short-term predictions. Our results show that the combination of LSTM and SARIMA models yields superior forecasting performance compared to using each model individually. We present a detailed analysis of the forecasted results, including Root Mean Square Error (RMSE) calculations and visualizations to demonstrate the effectiveness of our multi-model approach. This research contributes to the field of time series forecasting by showcasing the benefits of combining deep learning and classical statistical methods. The proposed approach provides a flexible and robust framework applicable to various time series prediction tasks, offering improved accuracy and reliability in forecasting future trends.

Contrastive learning for traffic flow forecasting based on multi graph convolution network

AbstractContrastive learning is an increasingly important research direction and has attracted considerable attention in the field of computer vision. It can greatly improve the representativeness of image features through data augmentation, unsupervised learning, and pre‐trained models. However, in the field of traffic flow forecasting, most graph‐based models focus on the construct of spatial–temporal relationships between road segments and ignore the use of temporal data augmentation and pre‐trained models, which can improve the representation ability of the forecasting model. Therefore, in this work, contrastive learning are used to expand the distribution of sequence samples and improve the quality and generalization of forecasting models. Based on this, a novel forecasting model called contrastive learning based on multi graph convolution network (CLMGCN) is proposed, which is combined with four components: multi graph convolution network, which learns the spatial–temporal feature of the input traffic data; temporal data augmentation, which obtains the augmentation data of the input traffic data; contrastive learning, which achieves the pre‐training phase and improve the quality of output feature of multi graph convolution network; output block, which utilizes the enhanced output feature of multi graph convolution network for predicting the future traffic data. Finally, by the experimental results of four public traffic flow datasets, it can be shown that CLMGCN achieves higher traffic forecasting accuracy with lower model complexity.

Experimental Analysis of Time Series Models for Traffic Flow Prediction

Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. In this context, one of the most in-demand techniques of Machine Learning, especially time series-based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. We aim to perform computations related to traffic on the traffic datasets and determine the accuracy of each model. For this purpose, we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves the highest prediction accuracy with the lowest root mean squared error.

Study on Induced Passenger Flow Forecast for Intercity High-Speed Rail

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

AbstractIt is widely recognized that the tourism industry is susceptible to crisis or natural disaster. Although some literature has studied the consequences of the crisis and disaster, there remains a lack of study on the effect of COVID‐19. Against this background, this paper investigates the tourist flow forecasting by adopting an advanced nonparametric mixed‐frequency vector autoregressions model using Bayesian additive regression trees. This is particularly suitable for forecasting the presence of extreme observations, for example, the COVID‐19 pandemic. We investigate tourism demand forecasting using a large number of predictors, including industrial production index, CPI, exchange rate, economic policy uncertainty, Google trends index, and COVID‐19 infection rate. The data used for this study relate to tourist flows in Chinese Hong Kong, Japan, and South Korea. Empirical study demonstrates that this novel model significantly outperforms the traditional mixed‐frequency vector autoregressions model to quarterly tourist flow forecasting. Therefore, this model can significantly enhance tourism forecast accuracy in the face of extreme events. This study contributes to the literature on tourism forecasting and provides policymakers with policy implications.

Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction

Traffic flow forecasting is of great importance in intelligent transportation systems for congestion mitigation and intelligent traffic management. Most of the existing methods depend on deep learning to extract the spatial–temporal correlation of traffic nodes but ignore the traffic flow characteristics. In this paper, we design three traffic congestion indexes to reflect the operational status of nodes based on traffic flow theory and design a traffic flow matrix to better represent the relationship between nodes. We also design a novel graph convolution network with attention mechanisms called TFM-GCAM to better capture the spatial–temporal features and dynamic characteristics of nodes. A novel Fusion Attention mechanism is proposed to effectively fuse the dynamic characteristics and the spatial–temporal features for improvement. Experiments and ablation studies on the public dataset show the superiority of TFM-GCAM. We also discuss it with our previous works for a better understanding. Our research proposes to better integrate traffic flow theory into deep learning models and to better combine the respective strengths of attention mechanisms and graph neural networks for more effective traffic flow prediction.

Research on Traffic Flow Forecasting Based on Dynamic Spatial-Temporal Transformer

Accurate traffic flow forecasting is crucial for urban traffic control and route planning. Aiming at the difficulty in capturing dynamic spatio-temporal complexity of traffic flow, a dynamic spatio-temporal transformer (DST-Trans) model capable of modeling dynamic correlation of traffic flow is proposed, which consists of gated temporal convolutional network (GTCN), graph convolutional network (GCN), and spatio-temporal transformer (ST-TF). GTCN and GCN are utilized to capture the temporal and spatial characteristics of traffic flow, respectively. ST-TF includes a temporal transformer using temporal gated convolution and temporal multi-head self-attention to capture short-long term temporal features, and spatial transformer using spatial gated graph convolution and spatial multi-head self-attention to capture local-global dynamic spatial features. In addition, to take full advantage of the dynamic and static associations of road networks, multi-graph models of road relationship graph, similarity graph, and adaptive dynamic graph with SGGC are constructed. Experimental results show that the DST-Trans model in this paper shows good prediction performance in short-term (15 min), medium-term (30 min), and long-term (60 min) prediction, outperforming existing state-of-the-art models by up to approximately 7%.

Dynamic spatial–temporal graph convolutional recurrent networks for traffic flow forecasting

Traffic flow forecasting is crucial for making appropriate route guidance and vehicle scheduling schemes in intelligent transportation systems. However, recent graph-based methods leverage pre-defined static graphs to describe the spatial–temporal characteristic of road networks. The interactions of two road segments are changeable under the influence of natural environmental and socioeconomic factors, while these methods are not sufficient to capture the complicated dynamic correlations of different nodes. To address these problems, we propose a novel dynamic graph-based deep learning framework with dynamic graph recurrent network for traffic flow forecasting, called Dynamic Spatial–temporal Graph Recurrent Neural Networks. In this framework, a novel dynamic graph generator is designed to obtain the dynamic representation of nodes, which employs multi-head attention network and dynamic node embedding to capture hidden spatial dependency more effectively. To infer the edge status of dynamic graph at different times, the generated dynamic graph is trained as special time series data via dynamic graph recurrent neural network for downstream time-series prediction. In contrast to methods straightforwardly concatenating static graphs and dynamic graphs, a novel fusion framework integrates two-channel convolutional networks with penalty terms and a gate fusion layer to extract dynamic spatial dependency from multiple graphs for improving forecasting accuracy and reducing computational consumption. Experiments on three real-world datasets are carried out to evaluate the superior performance of our model. Compared with previous state-of-the-art baselines, the proposed method performs much better with 10%–26% improvements on three datasets. The results also indicate that our model is robust against emergent traffic situations.

How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method

Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study’s results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis.

MG-ASTN: Multigraph Framework With Attentive Spatial–Temporal Networks for Crowd Mobility Prediction

Predicting urban crowd patterns/flows is a challenging task due to complex spatial-temporal (ST) dependencies. In this paper, we aim to examine and report the capability and effectiveness of the current most widely used Graph Convolutional Networks (GCNs) on mobile data analysis in ST networks for crowd flow forecasting. Specifically, we propose a novel dual-stream framework leveraging Multi-Graph with Attentive Spatial-Temporal Networks (MG-ASTN) to simultaneously predict crowd in-out flow and OD flow based on the trajectory data collected by on-board devices (e.g., GPS). MG-ASTN utilizes Multi-Graph Convolutional Networks encoding non-Euclidean correlations to explore pair-wise relationships among regions. In addition, we further apply a cross-channel attention mechanism with 3D Temporal Convolutional Network to address the heterogeneity of ST features and capture more meaningful data representations for multi-task learning. In the evaluation, we conduct experiments based on 2 real-world datasets and verify most well-known state-of-the-art methods for crowd flow prediction. The results demonstrate that MG-ASTN could outperform other solutions -in-out flow prediction with lowest RMSE and MAE, and OD flow prediction beyond others in most cases, thus it has great potential in modeling the complex correlations among regions in ST networks and enabling accurate prediction in urban computing.

Self-Supervised Spatiotemporal Masking Strategy-Based Models for Traffic Flow Forecasting

Traffic flow forecasting is an important function of intelligent transportation systems. With the rise of deep learning, building traffic flow prediction models based on deep neural networks has become a current research hotspot. Most of the current traffic flow prediction methods are designed from the perspective of model architectures, using only the traffic features of future moments as supervision signals to guide the models to learn the spatiotemporal dependence in traffic flow. However, traffic flow data themselves contain rich spatiotemporal features, and it is feasible to obtain additional self-supervised signals from the data to assist the model to further explore the underlying spatiotemporal dependence. Therefore, we propose a self-supervised traffic flow prediction method based on a spatiotemporal masking strategy. A framework consisting of symmetric backbone models with asymmetric task heads were applied to learn both prediction and spatiotemporal context features. Specifically, a spatiotemporal context mask reconstruction task was designed to force the model to reconstruct the masked features via spatiotemporal context information, so as to assist the model to better understand the spatiotemporal contextual associations in the data. In order to avoid the model simply making inferences based on the local smoothness in the data without truly learning the spatiotemporal dependence, we performed a temporal shift operation on the features to be reconstructed. The experimental results showed that the model based on the spatiotemporal context masking strategy achieved an average prediction performance improvement of 1.56% and a maximum of 7.72% for longer prediction horizons of more than 30 min compared with the backbone models.

TRELM-DROP: An impavement non-iterative algorithm for traffic flow forecast

Accurate prediction of traffic flow is crucial to building a smart city. Given the nonlinearity of traffic flow, this paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence combined with the DROP strategy. The algorithm is referred to as TRELM-DROP. The Tent chaos strategy and residual correction method reduce the impact of randomness in traffic flow. On this basis, the Tent and residual correction strategy avoids the weight optimization of the ELM algorithm using the iterative method. A DROP strategy is proposed in the proposed algorithm to improve its ability to predict traffic flow under varying conditions. A comprehensive comparison of 36 real-world datasets is presented in this paper, comparing TRELM-DROP with other benchmark models. The results show that the proposed algorithm can produce the best prediction performance regarding various prediction error metrics under various traffic conditions without iterative optimization.

Analysis and forecast of passenger flows in public transport - the case of Poland

Public transportation provides its services to both urban centers and neighboring areas in the immediate vicinity of the city. The problem of urban transportation is evident, the number of people willing to use public transportation has decreased. Therefore, there is a need to delve into the issue and conduct an analysis of the demand for urban transportation in Poland in 2000-2030, this will allow us to assess in what direction urban transportation is heading, whether there is an increase in the number of people using it, or whether there is a downward trend (Zielińska 2018). Based on CSO statistics from 2009-2020 for the analysis of demand for public transportation in Poland, a forecast of people using public transportation was conducted using Statistica software for 2021-2030. Due to the situation with the COVID-19 pandemic, the study was conducted in 2 ways – with and without 2020. Public transportation will make less and less profit and even losses for the next few years through rising gasoline and energy prices. Virtually in each of the provinces, and likewise throughout Poland, a decline in the number of people willing to use public transportation is evident. conclusions. On the basis of the surveys carried out, there is a general trend that shows the current state of public transport. In most of the cases studied, a similar conclusion emerges, namely that public transport will experience a marked decline in the coming years. The number of people who want to use public transport will decrease, mainly due to the COVID-19 pandemic and people’s fears for their own safety.